2026-04-29 23:29:59
We analyzed five high-performing brand posts on HackerNoon from early 2026 to understand what actually drives results — from the hook and structure to credibility signals and a tactic you can apply to your next piece.
2026-04-29 23:01:57
A conservation foundation from the end of the world, a Google Research collaboration, and 49,383 acoustic detections later, this is what happened.
The goal is the first complete acoustic inventory of a Chilean Patagonian nature sanctuary using a 14,795-taxon foundation model.
We are Fundación Kreen. We operate from Coyhaique, in Chilean Patagonia 45°34′16″S 72°04′07″O / -45.5712, -72.0685 a city of 60,000 people surrounded by mountains, rivers, and fjords that most of the world has never heard of. We manage the Meullín-Puye Nature Sanctuary, 100,000 hectares (247105 acres) in remote Patagonian fjord of one of the least-disturbed coastal ecosystems on the planet (-45.1386, -72.9697).
We are not a tech company. We are seven people in a NGO who work in conservation at the end of the world.
But this April, we built something that, honestly, we did not expect to build.
The fjords of Chilean Patagonia look pristine. And in many ways, they are. But underneath that postcard image, something is happening that nobody has a system to monitor.
Chile is the second largest salmon producer in the world. The industry operates hundreds of farming centers directly inside the fjords, the same fjords where six threatened and near-threatened seabird species nest, feed, and migrate. These birds, the Flightless Steamer Duck (Tachyeres pteneres, Vulnerable), the Black-browed Albatross, the Pink-footed Shearwater, the Imperial Cormorant,transit between the industrial aquaculture zones and pristine protected areas like our sanctuary.
The salmon industry releases over 350 metric tons of antibiotics into Patagonian waters every year. The birds eat in those waters. Then they fly to the sanctuary. Nobody was measuring what they carry with them.
And then, on April 2026, two days before we submitted our grant application, Chile’s SAG (Servicio Agrícola y Ganadero) declared a sanitary emergency in the Biobío Region after confirming H5N1 avian influenza in a wild bird in Arauco. Nine Chilean regions now have confirmed cases. Aysén has no active bio surveillance system for wild bird H5N1 vectors. That is the problem we are trying to solve.
The central hypothesis is this: migratory seabirds transiting between salmon aquaculture centers and pristine peatlands are acting as biological vectors, silently transporting antimicrobial resistant bacteria, nutrient subsidies, and potentially H5N1 across ecosystem boundaries. They are the connective tissue between an industrial zone and a protected area. And nobody is listening to them.
Until now, Perch v2 by Google DeepMind
We had been running three Bird Weather PUC acoustic sensors at the sanctuary since November 2024. These little devices record ambient audio continuously and process it through BirdNET, generating validated species detections. Over 14 months, they produced 1,701 confirmed detections across 21 species, real field data, georeferenced, timestamped, stored in our Google Drive.
But BirdNET, as good as it is, is a black box. We needed something open-source, trainable, and scientifically publishable.
Perch v2 is Google DeepMind's bioacoustics foundation model 14,795 species, 1,536-dimensional embeddings, trained on a massive global corpus. Unlike Perch 1 (which was bird-vocalization-classifier/4 on TF Hub and had significant Southern Hemisphere gaps), Perch v2 uses iNaturalist taxonomy and actually covers the species we care about.
We loaded it via perch-hoplite:
from perchhoplite.zoo import modelconfigs
model = modelconfigs.loadmodelbyname('perch_v2')
outputs = model.embed(waveform)
# outputs.embeddings: shape (1, 1, 1536)
# outputs.logits['label']: shape (1, 14795)
Then we built a geofencing layer, a curated list of 35 species ecologically relevant to Aysén, mapped by scientific name directly to Perch v2's iNaturalist taxonomy indices. Species like Tachyeres pteneres at index 13,328. Thalassarche melanophris at index 13,594. Falco peregrinus at index 5,088.
==TARGET_SPECIES = {==
=='Tachyeres pteneres': ('Quetro No Volador', 'CENTINELA', 'VU'),==
=='Thalassarche melanophris': ('Albatros Ceja Negra', 'CENTINELA', 'NT'),==
=='Ardenna creatopus': ('Fardela de Collar', 'CENTINELA', 'VU'),==
==# … 32 more species across 6 habitat categories==
==}==
Calibrated thresholds by habitat type,40% for sentinel species (more sensitive), 60% for resident forest species (more strict), and ran the full analysis on 4,998 FLAC recordings from the sanctuary.
We were not expecting what came out.
49,383 acoustic detections. 35 species. All 6 sentinel species detected.
| Species | IUCN | Detections | Max Confidence | |----|----|----|----| | Thalassarche melanophris | NT | 2,739 | 99.6% | | Spheniscus magellanicus | NT | 2,494 | 98.5% | | Tachyeres pteneres | VU | 1,343 | 74.3% | | Leucocarbo atriceps | LC | 883 | 90.1% | | Ardenna creatopus | VU | 415 | 99.6% | | Leucophaeus scoresbii | NT | 180 | 97.9% |
The Flightless Steamer Duck,Tachyeres pteneres,the most critical sentinel species for our project, the one that was completely absent from Perch 1's training corpus, appeared 1,343 times. At 74.3% maximum confidence. In recordings made at coordinates where we have confirmed field sightings.
The Peregrine Falcon appeared in 4,998 detections at 99.4% confidence. A known trans-hemispheric migrant. Exactly where it should be.
Here is something we did not plan for but that changed everything.
While working on this, we noticed that Perch 1 was missing several Southern Hemisphere species from its training data. We filed Github issue in the google-research/perch repository, reporting the gap and framing it as a One Health biosurveillance problem,not just a birding inconvenience.
Tom Denton, a researcher from Google Research who works on Perch, responded within hours. He confirmed that all 11 missing species are actually covered in Perch v2, pointed us to the correct model variant, and offered a free technical support for us. His bio says "in it for the birbs."
He also provided the URL to download the Perch v2 labels file directly from Google Cloud Storage,which solved a technical problem we had been fighting for two days:
[https://storage.googleapis.com/chirp-public bucket/models/perchv2/assets/labels.csv ](https://storage.googleapis.com/chirp-public bucket/models/perchv2/assets/labels.csv)
That one line of information was worth more than five days of debugging.
What We Learned About Running Perch v2 in the Real World
A few things that are not obvious from the documentation:
We have 53,818 FLAC recordings sitting in Google Drive. We are running the full analysis now,all of them, no geofencing, every taxon Perch v2 knows about at ≥70% confidence. The goal is the first complete acoustic inventory of a Chilean Patagonian nature sanctuary using a 14,795-taxon foundation model.
We are also applying to the Google.org Impact Challenge: AI for Science with this work. The project,Silent Vectors— proposes combining Perch v2 with AlphaEarth Foundations (for peatland carbon dynamics) and field biosurveillance protocols with Universidad de Concepción to build the first One Health automated monitoring system for H5N1 and AMR in Patagonian seabirds.
All code is open source. The full acoustic dataset will be published on Google Earth Engine under CC-BY 4.0.
A Note on Working From the End of the World
People sometimes ask us why a small conservation foundation in Coyhaique is building AI pipelines instead of just doing fieldwork.
The honest answer is that fieldwork alone is no longer enough.
The H5N1 emergency in Chile is real and moving south. The salmon industry is expanding. The fjords are changing faster than anyone can survey them manually. The only way to generate the baseline data that conservation decisions will need for the next 30 years is to automate detection at scale,and to do it with models that are open, reproducible, and honest about what they know and what they do not.
Perch v2 is not perfect. may It needs fine-tuning for Southern Hemisphere species. It requires calibration for the specific soundscape of Patagonian fjords. We know that.
But 49,383 detections including all 6 sentinel species,including a Vulnerable endemic duck that most acoustic models have never heard of,is a pretty good start.
\ … in it for the birbs!!
\
2026-04-29 22:01:14
The real risks of the AI transition are an imminent concern. We have discussed these risks at length here at HackerNoon, focusing on several aspects that go beyond a simplistic doomsday vision. We’ve all heard the warnings. Elon Musk, Sam Altman, and a chorus of AI leaders paint a future straight out of Terminator, featuring superintelligent systems that seize control and outsmart us. They might even wipe humanity off the map. This narrative is dramatic, cinematic, and easy to rally against - but it’s also the wrong story.
\ The real risk of advanced AI, from early AGI to full ASI, isn’t violent extinction. It’s something quieter, slower, and already visible in the data: a collective, civilization-wide loss of self-worth. When machines can do everything humans can do, but better, faster, and cheaper, what remains of our sense of competence? Our pride? Our reason to get out of bed?
\ This is not a fringe worry. Several earlier very interesting pieces on HackerNoon circle the same underlying fracture, each from a different angle, and each with deep insights worth crediting explicitly. Jin Park’s “Human Learning No Longer Exists - Enter Human Meaning” (link below) is especially sharp on the psychological mechanism, arguing that when machines lift the weight of struggle, we do not merely automate tasks, we automate the friction that for generations shaped identity, character, and meaning.
\ CeThe.World’s “Are We Going to Become the ‘Workless’ Generation?” offers another deeply insightful route into the same territory, emphasizing that technological displacement is not only about income, but about the collapse of identity, social status, and the sense of being needed, and it draws a long historical line from the Luddites to modern knowledge workers.
\ And Anton Voichenko’s “Experts Aren’t Discussing Nearly Enough the Tax Implications of Replacing Workers With AI” frames the economic plumbing of the transition while still naming the human core of it: if people become less essential in production, societies must confront how to redefine work, purpose, and survival.
\ https://hackernoon.com/human-learning-no-longer-exists-enter-human-meaning \n https://hackernoon.com/are-we-going-to-become-the-workless-generation \n https://hackernoon.com/experts-arent-discussing-nearly-enough-the-tax-implications-of-replacing-workers-with-ai
\ I would argue that to understand what’s coming, we need to look backward, not to science fiction, but to the documented collapse of indigenous cultures after contact with Western technological civilization.
\ Consider the Inuit (“Eskimos”), the First Nations of Canada, and the Native peoples of North and South America. These were not primitive societies. They had complex oral traditions, masterful hunters, brilliant artists, and sophisticated spiritual systems refined over millennia. Then came steel tools, rifles, printed books, mass-produced goods, and industrial-scale agriculture.
\ Suddenly, the best hunter in the tribe was outclassed by a teenager with a scoped rifle. The finest carver’s work looked crude next to the colorful packaging and plastic toys that washed up in trading posts or garbage heaps. The shaman’s healing rituals were eclipsed by antibiotics and surgery.
\ The result wasn’t just technological displacement. It was a total psychological rout. The cultural elite, the people who embodied the society’s highest skills, lost confidence. When your best is objectively inferior to what the newcomers can casually produce, self-respect evaporates. Entire cultures fractured. Suicide rates soared. Alcoholism and drug addiction became epidemics. Crime rose. Birth rates plummeted. In many communities, populations collapsed not only from violence or disease but from a deeper failure of will. Historians and anthropologists have documented this pattern repeatedly: the loss of purpose preceded, and amplified, the demographic decline.
\ We will almost certainly be spared the kind of 90 % die-off from novel diseases that devastated indigenous populations upon first contact. Unlike those societies, we possess modern medicine, global surveillance systems, and rapid vaccine development, tools that blunt the worst effects of new pathogens. That said, the picture is not entirely reassuring. As the human population climbs toward a projected peak near 10 billion, record levels of density, urbanization, and habitat disruption have dramatically raised the baseline risk of zoonotic spillovers.
\ Compounding this is the unprecedented speed of global connectivity: a virus that once took months or years to circle the globe can now hitch a ride on commercial flights and reach every continent within days. Epidemiologists have been warning for years that these factors are making severe pandemics far more frequent, shifting from rare events spaced centuries apart to something that could strike every decade or so.
\ Recent studies project the annual probability of a COVID-scale outbreak rising sharply, with some estimates placing a 27% chance of another within the next ten years and a roughly 50% likelihood over the next quarter-century. Bio-hackers wielding today’s synthetic-biology tools add yet another layer of deliberate risk. Even so, the deeper psychological mechanism remains unchanged: the profound, civilization-wide erosion of purpose when machines outperform us at virtually every meaningful human endeavor.
\ Look around today. Fertility rates in every developed nation and most developing ones outside sub-Saharan Africa are already below replacement, many trending toward 1.0 or lower. South Korea, Italy, Spain, Japan, and increasingly China are on track for populations that halve with each generation. The United States is only slightly better, propped up by immigration.
\ Demographers now project the global population peaking near 10 billion mid-century before declining sharply. We are already living through the early stages of the same self-reinforcing spiral: fewer births, less investment in the future, more despair.
\ Advanced AI will accelerate this dramatically.
\ White-collar jobs are disappearing first. Legal research, financial analysis, coding, graphic design, medical diagnostics, and even creative writing, tasks once reserved for highly educated humans, are now being done better by models that never sleep, never unionize, and improve daily. Humanoid robots capable of physical labor are no longer science fiction; they are in prototype and headed to factories and warehouses within years. When the last blue-collar strongholds fall, the economic rationale for most human labor evaporates.
\ Policymakers’ favorite answer is universal basic income, sometimes called “citizens’ income.” It sounds compassionate: let the machines create the wealth, and we’ll tax the robots to feed the humans. But history offers a bitter precedent. When Marie Antoinette was told the peasants had no bread, she supposedly replied, “Why don’t they eat cake?” The line may be apocryphal, but the attitude is real. A monthly check will not restore dignity when every meaningful contribution has been automated. People do not merely want to consume; they want to matter.
\ This is exactly where those three HackerNoon contributions, and several others, become so relevant arguments here. Jin Park’s essay is a particularly interesting and incisive articulation of why mere convenience does not equal meaning, and why outsourcing the hard parts of becoming competent risks hollowing out the self. CeThe.World’s piece is equally interesting in how it shows that the pain point in technological revolutions is often not the machine itself, but what it does to status, belonging, and the perceived value of a human life in the system. And Voichenko’s argument usefully complements the psychological claim by pointing at the structural incentives and policy machinery that can quietly push societies toward replacement instead of augmentation, leaving the “purpose problem” to metastasize even if the bills are paid.
\ So what does the future actually look like?
\ Population decline itself is not inherently catastrophic. A world of one billion people, the same range humanity inhabited in the early 1800s, or two billion around 1900, could be wealthier per capita, with dramatically lower ecological pressure.
\ Large parts of Earth could be returned to wilderness, and this could happen far more extensively than a hundred or two hundred years ago because modern high-efficiency farming, combined with the strong urbanization seen in all parts of the world, allows much more human land use to be concentrated while vast areas are spared or restored: rewilded forests, restored wetlands, thriving megafauna corridors.
\ This process is not merely a future ambition; it has already begun, as evidenced by the increasingly larger natural preserves added each year across the globe. Consequently, “natural preserves” would no longer be token parks but vast, self-sustaining ecosystems.
\ Meanwhile, the heavy lifting of industry, mining, manufacturing, and energy production would migrate off-planet. Abundant solar power in orbit (24/7, no weather, no atmosphere), combined with asteroid and lunar resources, offers essentially unlimited raw materials and energy. Elon Musk’s vision of giga to terawatt-scale compute clusters in space is only the beginning. Entire supply chains could relocate, freeing Earth from the smokestacks and strip mines that defined the 20th century. The carbon footprint and resource demands of humanity on Earth would shrink even as technological capability explodes.
\ The machines will keep advancing. ASI will solve fusion, climate modeling, materials science, and biology at speeds we cannot imagine. Civilization’s technological footprint will not shrink; it will simply move upward and outward.
\ The open question, perhaps the only question that truly matters, is what the remaining humans will do with themselves.
\ Some will thrive as curators, philosophers, artists, or explorers of entirely new domains the AIs have not yet colonized (or perhaps never will). Others may choose lives of leisure, study, or service. But for millions, perhaps the majority, the transition will feel like the indigenous experience writ large: a sudden, irreversible realization that the game has changed and the old scoreboard no longer applies.
\ This is not a call for Luddite rebellion or AI moratoriums. It is a call for intellectual honesty. The extinction narrative is a distraction. The real risk is subtler and more insidious: a slow erosion of the human spirit, a civilization that keeps its body but loses its soul. Over time, if societies settle into years of one-child families or none at all, many family lines (bloodlines) will steadily diminish and, eventually, some will disappear entirely.
\ We do not need to fear the machines rising up against us. We need to fear the day they make us irrelevant.
\ The choice before us is not whether to build AGI and ASI. That train has left the station. The choice is whether we prepare psychologically, culturally, and spiritually for a world in which humanity is no longer the smartest, fastest, or most productive species on the planet, or in the solar system.
\ If we fail to address the coming crisis of purpose, the collapse we witness may not be loud and explosive like Terminator. It will be quiet, statistical, and heartbreakingly human: fewer births, more despair, empty playgrounds, and a species that quietly decides it no longer needs to continue.
\ The machines won’t kill us.
\ We might simply stop trying to live.
\ “¡Mira, mira, Viene la tormenta!” \n “What did he just say?” \n “He said there’s a storm coming in.” \n “…Yes, I know.”
\ What do you think happens to human meaning when every meaningful task is done better by code and silicon? The floor is open.
2026-04-29 22:01:05
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How to use Machine learning, Deep learning and Computer Vision for building Optical Character Recognition (OCR) solution for text recognition.
I did lot of research as well developed this software system using various Machine learning methods. I have spent around one year on this project to implement this technology for a local state government. Unfortunately It didn't materialised. But I am interested in contributing to open source community. It can accurately identify, segment, recognise objects in video feeds (92 types of semantic attributes of a person in video feeds). The most interesting part is the accuracy of our facial recognition of wild shots from street cctv cameras.
Whether it be for fun in a Snapchat filter, for a movie, or even to remove a few riddles, we all have a utility in mind for being able to change our age in a picture.
A2D2, ApolloScape, and Berkeley DeepDrive are among the best autonomous driving datasets available today.
ShaRF stands for Shape-conditioned Radiance Fields from a Single View. The goal is to take a picture of a real-life object, and translate this into a 3D scene.
The image processing library which stands for Open-Source Computer Vision Library was invented by intel in 1999 and written in C/C++
Object detection is a product of Computer Vision and is a very effective technique to precisely locate items of different shapes and sizes and label them.
Scaling AI for the real world requires peeling back the layers of abstraction we've gotten too comfortable with.
We’ve seen AI generate text, then generate images and most recently even generate short videos
Computer vision will radically change smart technology. Here are five ways it's already impacting smart cities.
OpenAI just released the paper explaining how DALL-E works! It is called "Zero-Shot Text-to-Image Generation".
Here are some tips to improve your dataset collection
How to Spot a Deep Fake in 2021. Breakthrough US Army technology using artificial intelligence to find deepfakes.
Imagine if you could get all the tips and tricks you need to hammer a Kaggle competition. I have gone over 39 Kaggle competitions including
Artificial Intelligence(AI) has already proven to solve some of the complex problems across the wide array of industries like automobile, education, healthcare, e-commerce, agriculture etc. and yield greater productivity, smart solutions, improved security and care, business intelligence with the aid of predictive, prescriptive and descriptive analytics. So what can AI do for Manufacturing Industry?
From vehicle counting and smart parking systems to Autonomous Driving Assistant Systems, the demand for detecting cars, buses, and motorbikes is increasing and soon will be as common of an application as face detection.
And of course, they need to run real-time to be usable in most real-world applications, because who will rely on an Autonomous Driving Assistant Systems if it cannot detect cars in front of us while driving.
In this post, I will show you how you can implement your own car detector using pre-trained models that are available for download: MobileNet SSD and Xailient Car Detector.
Introduction: (How I got the idea and the process of how the dataset was developed)
You can easily make changes to your dataset using DVC to handle data versioning. This will let you extend your models to handle more generic data.
C++ pipeline for LiDAR-based autonomous driving.
The next step for view synthesis: Perpetual View Generation, where the goal is to take an image to fly into it and explore the landscape!
DeOldify is a technique to colorize and restore old black and white images or even film footage. It was developed by Jason Antic.
Great way to improve your Computer Vision models metrics
We at TaQadam produce different computer vision technologies. In this blog we tell about using machine vision in production for some common use-cases.
With the spread of COVID-19 wearing face masks became obligatory. At least for most of the population. This created a problem for the current identification systems. For example, Apple’s FaceID struggled to recognize faces with masks.
Extract features with a pretrained CNN, cluster unlabeled images, propagate labels with pseudo-labelling, and train a semi-supervised classifier.
Digital Technology is everywhere and it is redefining how we live, communicate, and work. Most importantly, it accelerates how we innovate.
In the spring of 1993, a Harvard statistics professor named Donald Rubin sat down to write a paper. Rubin’s paper would go on to change the way that artificial intelligence is researched and practiced, but its stated goal was more modest: analyze data from the 1990 U.S. census, while preserving the anonymity of its respondents.
Computer-controlled robots are monotonous. They are mostly able to perform a sequence of processing operations that is fixed by the equipment configuration and
What is anomaly detection? How does it work? And how can you incorporate it into your company’s processes and workflows? Let's find out!
Recent developments in the field of training Neural Networks (Deep Learning) and advanced algorithm training platforms like Google’s TensorFlow and hardware accelerators from Intel (OpenVino), Nvidia (TensorRT) etc., have empowered developers to train and optimize complex Neural Networks in small edge devices like Smart Phones or Single Board Computers.
We’ve seen AI generate images from other images using GANs. Then, there were models able to generate questionable images using text. In early 2021, DALL-E was published, beating all previous attempts to generate images from text input using CLIP, a model that links images with text as a guide. A very similar task called image captioning may sound really simple but is, in fact, just as complex. It is the ability of a machine to generate a natural description of an image.
RLHF is an innovative approach to mitigating bias in LLMs. It incorporates human input in the training process to reduce bias and improve fairness.
In the previous article, it was described a six-point method to unwrap wine labels. Finding anchor points were performed with Hough transform. It gave fair results for good labels, but for many real cases it was quite unstable, and the efforts to tune it didn’t help much. It became clear at some point, Hough transform itself wasn’t capable of handling the variety of label forms, so the next step was training a neural network.
Most people think self-driving cars will improve traffic and safety — but that may not be correct. Here are some traffic issues self-driving cars could cause.
IARPA’s Video LINC program could be repurposed to spy on protesters, enforce 15-minute smart city compliance: perspective
Business applications of computer vision technology for Enterprises, retail analytics, edge computing, intrusion detection and monitoring
With the help of a facial recognition system, federal agents could capture a person suspected of illegal activity.
How Images are turned into arrays in Computer Vision
Using a modified GAN architecture, they can move objects in the image without affecting the background or the other objects!
Let me walk you through how I optimized a multi-modal perception model to run 40% faster while keeping it sharp enough to dodge pedestrians and parked cars.
This is a video of the 10 most interesting research papers on computer vision in 2020.
This is exactly what I tackled in the Alexa Prize SimBot Challenge where we built an embodied conversational agent that could understand instructions
When a human sees an object, certain neurons in our brain’s visual cortex light up with activity, but when we take hallucinogenic drugs, these drugs overwhelm our serotonin receptors and lead to the distorted visual perception of colours and shapes. Similarly, deep neural networks that are modelled on structures in our brain, stores data in huge tables of numeric coefficients, which defy direct human comprehension. But when these neural network’s activation is overstimulated (virtual drugs), we get phenomenons like neural dreams and neural hallucinations. Dreams are the mental conjectures that are produced by our brain when the perceptual apparatus shuts down, whereas hallucinations are produced when this perceptual apparatus becomes hyperactive. In this blog, we will discuss how this phenomenon of hallucination in neural networks can be utilized to perform the task of image inpainting.
Explore the influence of topology awareness on the generalization performance of Graph Neural Networks (GNNs) in this comprehensive study.
Most of us are convinced that we can dissociate humans from machines, but is it really the case? Would you swipe right for an AI-generated profile?
Computer vision is a multidisciplinary field of study that teaches computers to interpret images and videos just like humans. The most
challenging area in computer vision is Object Detection which deals in
recognizing multiple objects in an image or video and classifying them
accordingly.
Automation hits the US property insurance industry. Inspecting a property will soon be done with nothing but a few photos.
A deep-learning-based algorithm that is able to detect and quantify floating garbage from aerial images of the ocean.
"Association in psychology refers to a mental connection between concepts, events, or mental states that usually stems from specific experiences." [1] Once the associative link between events A and B has been built, the appearance of event A naturally entails the appearance of event B. [2]
Quickly find common resources and/or assets for a given dataset and a specific task, in this case dataset=COCO, task=object detection
Style transfer is a computer vision-based technique combined with image processing. Learn about style transfer with Tensorflow, a prominent framework in AI & ML
A rudimentary article describing the concept behind the "CLIP" algorithm in deep learning, its approach, implementation, scope & limitations.
In this article, I would like to share my own experience of developing a smart camera for cyclists with an advanced computer vision algorithm
I worked on optimizing object detection for autonomous vehicles using Atrous Spatial Pyramid Pooling (ASPP) and Transfer Learning.
Were you ever annoyed when you had to pull a massive dataset (versioned using DVC) before training your model?
We are slowly but surely moving towards a world where autonomous drones will play a major role. In this article, I will show you what stopes them today.
StyleGANEX: Enhancing Image Manipulation with Dilated Convolutions
Edge detection is fundamental in computer vision, allowing us to identify object boundaries within images
How to use a Convolutional Neural Network to suggest visually similar products, just like Amazon or Netflix use to keep you coming back for more.
Migrating from YOLO to Grounding DINO exposed brutal CPU cache limits, ONNX traps, and why INT8 quantization beats “max optimization.
PyTorch has sort of became one of the de facto standard for creating Neural Networks now, and I love its interface. Yet, it is somehow a little difficult for beginners to get a hold of.
From self-driving cars and facial recognition to AI surveillance and GANs, computer vision tech has been the poster child of the AI industry in recent years. With such a collaborative global data science community, the advancements have come both from research teams, big tech, and computer vision startups alike.
Learn how data engineering supports autonomous driving perception through annotation workflows, dataset augmentation, synthetic data generation, and versioning.
Innovative Computer vision applications can be found in every industry these days. Here is the list of top 10 CV applications
SiaSearch is a Berlin-based AI startup on a mission to accelerate computer vision application development.
You can apply any design, lighting, or graphics style to your 4K image in real-time using this new machine learning-based approach
In this article, I will show how your dataset of human faces can be enriched by 3D geometry transformation to improve the performance of your model.
Have you ever dreamed of taking the style of a picture, like this cool TikTok drawing style on the left, and applying it to a new picture of your choice? Well, I did, and it has never been easier to do. In fact, you can even achieve that from only text and can try it right now with this new method and their Google Colab notebook available for everyone (see references).
In this article, we are going to look at different ways financial institutions can leverage computer vision technologies for more efficiency.
Last year we saw NeRF, NeRV, and other networks able to create 3D models and small scenes from images using artificial intelligence. Now, we are taking a small step and generating a bit more complex models: whole cities. Yes, you’ve heard that right, this week’s paper is about generating city-scale 3D scenes with high-quality details at any scale. It works from satellite view to ground-level with a single model. How amazing is that?! We went from one object that looked okay to a whole city in a year! What’s next!? I can’t even imagine.
A review of Face Recognition loss functions, exploring advancements from ArcFace to modern adaptive and prototype-based methods for improved accuracy.
This paper presents an opensource toolkit intended primarily for ecologists and computer-vision/machine-learning researchers for wildlife re-identification.
By bridging deep learning research in Python with safe, high-performance deployment in Rust, we could unlock the true potential of AI.
Let’s talk about what technologies are used in metaverse development and how businesses can create their own metaverse applications.
A practical 3-pillar framework for evaluating computer vision models in production.
Explore ultra-lightweight image classifiers using compact CNNs and handcrafted features, achieving strong accuracy with minimal parameters.
How open-vocabulary vision-language object detectors overcome closed-set limits, with VOC/COCO/LVIS benchmarks and a hybrid recipe for fast edge deployment.
One of the known truths of the Machine Learning(ML) world is that it takes a lot longer to deploy ML models to production than to develop it.¹
Researchers have developed a text-to-image generation model called Kandinsky that uses a novel latent diffusion model to produce images that appear natural.
Computer vision now lives with us with exceptional AI capabilities. Learn how AI and computer vision is playing a key role in outsmarting human beings.
introduction to computer vision technologies, applications, use cases and key models.
Learn how to use Kalman filters to minimize uncertainty with multi-sensory arrays
TLDR:
Boosting Automation with Instance Segmentation: Accurate Object Localization for Industrial Robots
Pillow is Python Imaging Library that is free and open-source an additional library for the Python programming language that adds support for opening, manipulating, and saving in a variety of extension.
This article describes why privacy concerns should be top of mind while building or adopting computer vision based applications
This post is about creating your own custom dataset for Image Segmentation/Object Detection. It provides an end-to-end perspective on what goes on in a real-world image detection/segmentation project.
TLDR: They reconstruct sound using cameras and a laser beam on any vibrating surface, allowing them to isolate music instruments, focus on a specific speaker, remove ambient noises, and many more amazing applications.Watch the video to learn more and hear some crazy results!
In a letter to congress sent on June 8th, IBM’s CEO Arvind Krishna made a bold statement regarding the company’s policy toward facial recognition. “IBM no longer offers general purpose IBM facial recognition or analysis software,” says Krishna.
An open source multimedia framework to build and deploy computer vision apps in minutes without worrying about media pipelines.
LightCap: a tiny, fast image captioner using CLIP & distillation. 75% smaller, SOTA on COCO (136.6 CIDEr), 188ms/CPU. Ready for mobile!
Stability AI's most recent model Stable Video Diffusion (SVD) explained…
PAN is a new AI model that uses a Large Language Model as its autoregressive world model to predict the future, solving rapid time decay with a novel approach.
Building a facial recognition application with JavaScript is not a daunting task. In this blog post, I'll walk you through the journey of developing one.
In this article, we’ll dive into the importance of data curation for computer vision, as well as review the top data curation tools on the market.
Learn everything you need to know about Computer Vision via these 214 free HackerNoon stories.
Part II describes how to use Kalman filters to minimize uncertainty when using multi-sensor arrays
Artificial intelligence (AI) is the field of making computers able to act intelligently, to make decisions in real environments that will have favorable outcomes.
Researchers have developed a text-to-image generation model called Kandinsky that uses a novel latent diffusion model to produce images that appear natural.
The principles of stereoscopic imaging, its evolution, and impact on PC game development. Learn from the expertise of Konstantin Morshnev
Hi, my name is Prashant Kikani and in this blog post, I share some tricks and tips to compete in Kaggle competitions and some code snippets which help in achieving results in limited resources. Here is my Kaggle profile.
Scientists have dedicated centuries to studying our brain, trying to understand how this super-powerful computer is wired, how it comprehends the world, testing the limits of its capabilities.
In this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
Whether retailers like it or not, the future of retail is here, in the form of smart algorithms. Machine learning will change much of the industry's norms, often for the better. Retail trends point to the store of the future being automated using the latest technology. Brick & Mortar, physical retail… however you like to call it, your favourite real-world store is about to get a whole lot more digital. Whether that's the best idea remains to be seen.
‘Computer Vision’ (CV) refers to processing visual data as a human would with their eyes, so that we can make conclusions about what is in an image. Once we know what is in an image, we can make our application respond, much like a human would when processing visual data. This is what enables technology like self-driving cars.
This AI can transfer your hair to see how it would look like before committing to the change.
“Companies that failed to incorporate automation in their roadmap experienced a 25% drop in their customer retention,” concluded a survey by Gartner.
With LASR, you can generate 3D models of humans or animals moving using only a short video as input.
Researchers have developed a text-to-image generation model called Kandinsky that uses a novel latent diffusion model to produce images that appear natural.
Since its introduction, computer vision and object detection algorithms have continuously advanced. They started out simple and have since evolved…
Researchers have developed a text-to-image generation model called Kandinsky that uses a novel latent diffusion model to produce images that appear natural.
HackerNoon good company with Tanay Dixit, co-founder, and CPO of Wobot.ai.
Today, we are going to discuss a method proposed by researchers from four institutions one of which is ByteDance AI Lab (known for their TikTok App).
Every day we are facing AI and neural network in some ways: from common phone use through face detection, speech or image recognition to more sophisticated — self-driving cars, gene-disease predictions, etc. We think it is time to finally sort out what AI consists of, what neural network is and how it works.
How I built a production-ready traffic violation detection system using YOLOv8, DeepSORT, OpenCV, and hybrid ML pipelines.
Multi-object Tracking using self-supervised deep learning
EditGAN allows you to control any feature from quick drafts, and it will only edit what you want keeping the rest of the image the same!
ArtLine is based on Deep-Learning algorithms that will take your image input and transform it into a line art. I started this project as fun project but was excited to see how it turned out. The results from this model are so good that it is almost equal to the line art by an artist.
I work as a Software Engineer at Endtest.
This R-CNN Summary breaks down the research into Object Detection and Image Segmentation done to develop Computer Vision and improve ML learning speeds.
Deep Learning gets a ton of traction from technology enthusiasts. But can it match the effectiveness standards that the public hold it to?
For people with vision problems.
A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code.
LightCap uses CLIP’s grid features, a visual concept extractor, cross-modal modulator, TinyBERT fusion, and ensemble heads for efficient captioning.
Say goodbye to complex GAN and transformer architectures for image generation. This new method can generate new images from any user-based inputs.
Data augmentation enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly.
A curated list of the latest breakthroughs in AI and Data Science by release date with a clear video explanation
In this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
Build a unified visual document index from multiple file formats—including PDFs, images, and slides—using CocoIndex and ColPali, No OCR needed.
Discover a framework that enhances GNN learning, enabling fair k-shot learning and fairness constraint for equitable predictive performance in structural groups
Masked attention and contrastive loss improve caption accuracy and reduce overlapping predictions.
Here's how you can use cognitive computing to automate media & entertainment workflows and stramline video production.
PerSense-D is a new benchmark dataset for personalized dense image segmentation, advancing AI accuracy in crowded visual environments.
This article introduces OW‑VISCap, a unified framework for open‑world video instance segmentation and object‑centric captioning.
This AI reads your brain to generate personally attractive faces. It generates images containing optimal values for personal attractive features.
The 3 Major Advantages of Annotating Video with the Innotescus Video Annotation Canvas.
Data augmentation enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly.
Stores are changing. We see it happening before our eyes, even if we don’t always realize it. Little by little, they are becoming just one extra step in an increasingly complex customer journey. Thanks to digitalisation and retail automation, the store is no longer an end in itself, but a mean of serving the needs of the brand at large. The quality of the experience, a feeling of belonging and recognition, the comfort of the purchase… all these parameters now matter as much as sales per square meter, and must therefore submit themselves to the optimizations prescribed by Data Science and its “intelligent algorithms” (aka artificial Intelligence in the form of machine learning and deep learning).
This article outlines the OW‑VISCap framework, which jointly detects, segments, and captions both seen and unseen objects within a video.
Computer vision techniques are developed to enable computers to “see” and draw analysis from digital images or streaming videos.
Here's how artificial intelligence can be used to reduce fire detection time from an average of 40 minutes to less than five minutes!
Data augmentation enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly.
Only 5% of autonomous driving sensor data is used for product development today. Better data infrastructure holds the keys to progress.
An image-based part inspection system that extracts geometric features directly from images and converts them into measurable, CAD-ready representations.
In this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
Interactive whiteboards are the evolution of classroom whiteboards and non-electronic whiteboards in the workplace. Their existence is not always new, but recently the benefits of their meetings and presentations have become the forefront of modern business.
Fifty years ago, computers couldn't do much other than mathematical calculations - they just weren't powerful enough. Today, they can do just about anything. Even your mobile phone is powerful enough to process video in real-time to track objects. I'm talking about computer vision, and we've only begun to find applications for this technology.
This paper presents an opensource toolkit intended primarily for ecologists and computer-vision/machine-learning researchers for wildlife re-identification.
This article evaluates OW‑VISCap on open‑ and closed‑world segmentation and dense video object captioning, setting new benchmarks on multiple datasets.
In this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
This is an article on Canny Edge Detection, starting with the theoretical background, to the custom implementation of the algorithm.
In this guide, we'll go over everything you need to know about Automatic Number Plate Recognition (ANPR) solutions, such as how they work, how they're used etc
Explore the rise of multimodal AI, a new frontier in artificial intelligence that integrates text, images, audio, and video for a more holistic approach.
This paper presents an opensource toolkit intended primarily for ecologists and computer-vision/machine-learning researchers for wildlife re-identification.
We are Warden AI Lab, a Latvia-based start-up studio focusing on gesture recognition and behavioral video analytics.
This paper explores the critical safety risk of typographic attacks against Vision-Large-Language-Models integrated into autonomous driving systems.
A generative approach towards synthesizing images of marine plastic using DCGANs
Veneers have become one of the biggest crazes in cosmetic dentistry, with celebrity adopters inspiring many to take the procedure in the pursuit of the perfect smile.
The Stereo Vision Process is the foundation of stereoscopic vision and depth perception.
TM-CNN detects defects in magnetic patterns with 98.8% accuracy, blending template matching and CNNs for efficient material analysis!
This is the third piece in a series on developing XR applications and experiences using Oracle and focuses on XR applications of computer vision AI and ML and i
Learn how GNNs exhibit unfair generalization when comparing test groups with significant structural distance differences.
In this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
Computer vision applications have become ubiquitous nowadays. It’s hard to think of a domain where the ability of computers to “see” what’s going on around them has not yet been leveraged.
This appendix details the experimental setup, hardware, hyperparameters, and additional results confirming GNN performance and findings across datasets.
In this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
Computer Vision is a fascinating discipline/category/subdivision of ML that combines artificial intelligence, image processing, and machine learning techniques.
Learn everything you need to know about Image Recognition via these 33 free HackerNoon stories.
Why most AI vision models fail in production and how better data annotation—not new architectures—can boost accuracy from 4% to 72%.
This conclusion summarizes insights on how topology awareness affects GNN generalization performance.
Data augmentation enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly.
Explore the relationship between topology awareness and generalization performance in GNNs using metric distortion.
Discover the top AI trends that are increasing in 2022 and will determine how companies can leverage the AI technology in the future.
AI-enhanced retail holds the promise to eliminate operational inefficiencies and provide shoppers with frictionless in-store experiences.
Tests in blurry microfluidic images show YOLOv8 more adaptable, while YOLOv5 may need task-specific tweaks for hard marine debris.
Data augmentation enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly.
Neural networks gave us a powerful and cheap-to-use tool for solving problems of forecasting, computer vision, and text analysis. However, at the same time, they brought the problem of inaccuracy, which is presented as the “norm” and “black box” for deep networks, the derivation of which is difficult to understand and improve.
Noonies 2021 nominee, Paran Sonthalia, is still a Berkley student. But that didn't stop him on the mission of reducing food waste. Hear more from him here.
Fine-tune DeepLabV3-MobileNetV2 with TensorFlow Model Garden: prepare TFRecords, configure training on Oxford-IIIT Pets, and export a ready-to-use model.
In this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
This article surveys prior open‑world and closed‑world video segmentation frameworks, situating OW‑VISCap’s innovations in object queries and captioning.
This paper presents an opensource toolkit intended primarily for ecologists and computer-vision/machine-learning researchers for wildlife re-identification.
In this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
The Internet of Things is a paradoxical technology: despite its simplicity, it can dramatically improve people’s daily lives and make businesses more profitable and less risky. Yet the majority of companies still hesitate when it comes to the implementation of IoT in business operations.
Explore the groundbreaking AI technology of Panoptic Scene Graph Generation with Transformers for a deeper understanding of visual scenes.
Image recognition and annotation technologies are evolving. New techniques that allow you to solve a wide variety of tasks quickly appear. We are happy to present five major trends in image recognition and annotation.
How to build a system that automatically detects and categorizes objects in media as soon as users upload it, storing the results in a database for future use.
This paper presents an opensource toolkit intended primarily for ecologists and computer-vision/machine-learning researchers for wildlife re-identification.
Data augmentation enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly.
Data augmentation enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly.
In this video, I will openly share everything about deep nets for computer vision applications, their successes, and the limitations we have yet to address.
Data augmentation enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly.
Fashion image tagging is infamously tedious for eCommerce. But, how can AI help create accurate tags--and go a step beyond in understanding fashion information?
In your smart home, you must have equipped lots of smart devices that streamline your life. At first glance, it seems attractive that your smart home provides tons of benefits. But, have you thought about its security? Without securing your smart home, it is not possible to attain its benefits for the long-term. Therefore, you need to invest in certain decent quality security products that can protect your smart home. They are capable to save your time and money. They only focus on providing exceptional security to your smart home. Let’s take a look at these useful security products:
Revolutionize defect analysis with TM-CNN! Spot tiny flaws in magnetic structures. Automates annotations for faster results.
In this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
Navigating the Nuances: The Relationship and Differences Between AI and Machine Learning
Paran Sonthalia, DeWaste CEO and a college student, shares his experience of what it's like working on a food waste solution in the middle of the pandemic.
In this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
Researchers have developed a text-to-image generation model called Kandinsky that uses a novel latent diffusion model to produce images that appear natural.
Researchers have developed a text-to-image generation model called Kandinsky that uses a novel latent diffusion model to produce images that appear natural.
This case study demonstrates the application of shortest-path distance to evaluate GNN performance on structural subgroups.
This paper presents an opensource toolkit intended primarily for ecologists and computer-vision/machine-learning researchers for wildlife re-identification.
Fabio Manganiello writes about solutions he's discovered while building a platform, library of plugins and an API to connect/manage any device and service through any backend, allowing users to easily set up any kind of automation. Fabio is based in Amsterdam, the Netherlands, and has been nominated for a 2020 #Noonie for exceptional contributions to the IoT tag category on Hacker Noon.
Researchers have developed a text-to-image generation model called Kandinsky that uses a novel latent diffusion model to produce images that appear natural.
Researchers have developed a text-to-image generation model called Kandinsky that uses a novel latent diffusion model to produce images that appear natural.
Part of the broader artificial intelligence and computer vision realms, human pose estimation (HPE) technology has been gradually making its presence seen in all kinds of software apps and hardware solutions. Still, human pose estimation seemed to be stuck at the edge, failing to cross into mainstream adoption.
Entering data and moving it from one place to another is a time-consuming, repetitive task.
The 3 most interesting research papers of October 2021!
In this paper, I present methods for generating synthetic images for face augmentation using recently presented GANs.
Explore the main results on GNN generalization performance, highlighting how structural distance affects accuracy disparities across subgroups.
In this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
9/23/2023: Top 5 stories on the Hackernoon homepage!
Visualize TensorFlow object detection data with TFRecords, bounding boxes, and masks while evaluating accuracy using mAP metrics.
Emil Bogomolov has been nominated as the Hackernoon Contributor of the Year - Computer Vision.
This research introduces Open-YOLO 3D, a novel method using 2D object detectors for high-speed, open-vocabulary 3D instance segmentation.
OpenPose is an open-source multi-person detection system supporting the body, hand, foot, and facial key points. The system uses a multi-stage CNN.
In the rise of robotics, computer vision and image processing cameras, image annotation comes as the first step to get the right AI training data for Deep Learning models. Whether you build an app to allow users to snap fashion items at the store as a new omni-channel sales or use machine vision installed at edge device at the industrial facility to monitor anomalies: it starts with training massive image data sets.
Review the nature of topology awareness in GNNs, including its impact on generalization, and active learning challenges.
This paper presents an opensource toolkit intended primarily for ecologists and computer-vision/machine-learning researchers for wildlife re-identification.
Facial recognition is everywhere. What once started as an attribute specific to sci-fi movies is now a part of everyday life: we rely on facial recognition every time we unlock our phones, tag friends in a Facebook post, or go through customs.
This paper explores using machine learning and LSTM for visual localization in underwater environments, achieving accurate positioning with underwater datasets.
Towards a generalized object detector capable of identifying and quantifying sub-surface plastic around the world
TimeLens can understand the movement of the particles in-between the frames of a video to reconstruct what really happened at a speed even our eyes cannot see.
Class imbalance (80% Artemia) and SSIM/MSE checks ensure quality across 50 mg, 100 mg, and control images before YOLO training.
Across the world, more than 1.3 million people die in car accidents, and over 50 million people are seriously injured every year. That’s nearly 4,000 people each day. Drivers in developing nations are most at risk. Only 54% of the world’s motor vehicles are in developing countries, but 90% of the world’s fatal car accidents occur in those countries. Even within the wealthiest countries vehicle-related injury and death are directly correlated to personal and neighborhood incomes.
Credit : Emmanuel Chaligné
YOLOv5 hits 97% precision on zooplankton; YOLOv8’s DFL handles class imbalance, boosting excrement hits despite scant labels.
An interview with Louis, an AI YouTuber known as What’s AI, and a research scientist at designstripe.
Open‑YOLO 3D replaces costly SAM/CLIP steps with 2D detection, LG label‑maps, and parallelized visibility, enabling fast and accurate 3D OV segmentation.
This paper explores using machine learning and LSTM for visual localization in underwater environments, achieving accurate positioning with underwater datasets.
Modified greedy and sampling algorithms solve Eq. (6) in GNNs for the k-center problem, with a running time complexity of O(k) and O(kn) respectively.
Multi-order stats enrich embeddings, avert neural collapse, and boost cross-domain accuracy while supporting lifelong instance updates.
LightCap excels on Nocaps across domains. Future work includes efficient CLIP, end-to-end training, and more pre-training data for better results.
Enter the world of machine vision software, where tech mimics human sight. Explore its mechanics, real-world applications, and ethical challenges it presents
Open‑YOLO 3D uses 2D object detection instead of heavy SAM/CLIP for open‑vocabulary 3D segmentation, achieving SOTA results with up to 16× faster inference.
This section reviews closed‑vocabulary 3D methods, open‑vocabulary 2D recognition, and emerging open‑vocabulary 3D segmentation approaches using SAM/CLIP.
Rethinking the future we want not the one that will befall us. We are in charge of our destiny.
Next Generation Emergency Recognition Technology of Brave New World!
There is nothing more precious than having a second chance to live!
This paper explores using machine learning and LSTM for visual localization in underwater environments, achieving accurate positioning with underwater datasets.
This paper explores using machine learning and LSTM for visual localization in underwater environments, achieving accurate positioning with underwater datasets.
TimeLens can understand the movement of the particles in-between the frames of a video to reconstruct what really happened at a speed even our eyes cannot see.
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2026-04-29 21:00:00
“On track” is not a real status - it hides uncertainty, dependencies, and early risks. Projects don’t fail suddenly; they become unobservable first. Clear, focused reporting helps teams catch problems early and avoid surprises.
2026-04-29 20:48:17
This article explores how SaaS upgrades in pharmaceutical revenue systems can introduce subtle but significant financial drift without causing visible failures. It highlights the limitations of traditional regression testing and introduces the concept of invariants—rules that must remain stable across system evolution. The key takeaway is that organizations need structured replay, traceability, and invariant-based validation to preserve financial accuracy during continuous updates.
