2025-12-14 15:10:54
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## The Architecture of Collaboration: A Practical Framework for Human-AI Interaction 
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2025-12-14 04:00:04
Related Works
2.1. Vision-and-Language Navigation
3.2. Open-set Semantic Information from Images
Conclusion and Future Work, Disclosure statement, and References
\ 3.2.1. Open-set Semantic and Instance Masks Detection
\ The recently released Segment Anything model (SAM) [21] has gained significant popularity among researchers and industrial practitioners due to its cutting-edge segmentation capabilities. However, SAM tends to produce an excessive number of segmentation masks for the same object. We adopt the Grounded-SAM [32] model for our methodology to address this. This process involves generating a set of masks in three stages, as depicted in Figure 2. Initially, a set of text labels is created using the Recognizing Anything model (RAM) [33]. Subsequently, bounding boxes corresponding to these labels are created using the Grounding DINO model [25]. The image and the bounding boxes are then input into SAM to generate class-agnostic segmentation masks for the objects seen in the image. We provide a detailed explanation of this approach below, which effectively mitigates the problem of over-segmentation by incorporating semantic insights from RAM and Grounding-DINO.
\ The RAM model) [33] processes the input RGB image to produce semantic labelling of the object detected in the image. It is a robust foundational model for image tagging, showcasing remarkable zero-shot capability in accurately identifying various common categories. The output of this model associates every input image with a set of labels that describe the object categories in the image. The process begins with accessing the input image and converting it to the RGB colour space, then resized to fit the model’s input requirements, and finally transforming it into a tensor, making it compatible with the analysis by the model. Following this, the RAM model generates labels, or tags, that describe the various objects or features present within the image. A filtration process is employed to refine the generated labels, which involves the removal of unwanted classes from these labels. Specifically, irrelevant tags such as ”wall”, ”floor”, ”ceiling”, and ”office” are discarded, along with other predefined classes deemed unnecessary for the context of the study. Additionally, this stage allows for the augmentation of the label set with any required classes not initially detected by the RAM model. Finally, all pertinent information is aggregated into a structured format. Specifically, each image is catalogued within the img_dict dictionary, which records the image’s path alongside the set of generated labels, thus ensuring an accessible repository of data for subsequent analysis.
\ Following the tagging of the input image with generated labels, the workflow progresses by invoking the Grounding DINO model [25]. This model specializes in grounding textual phrases to specific regions within an image, effectively delineating target objects with bounding boxes. This process identifies and spatially localizes objects within the image, laying the groundwork for more granular analyses. After identifying and localising objects via bounding boxes, the Segment Anything Model (SAM) [21] is employed. The SAM model’s primary function is to generate segmentation masks for the objects within these bounding boxes. By doing so, SAM isolates individual objects, enabling a more detailed and object-specific analysis by effectively separating the objects from their background and each other within the image.
\ At this point, instances of objects have been identified, localized, and isolated. Each object is identified with various details, including the bounding box coordinates, a descriptive term for the object, the likelihood or confidence score of the object’s existence expressed in logits, and the segmentation mask. Furthermore, every object is associated with CLIP and DINOv2 embedding features, details of which are elaborated in the following subsection.
\ 3.2.2. The Semantic Embedding Extraction
\ To improve our comprehension of the semantic aspects of object instances that have been segmented and masked within our images, we employ two models, CLIP [9] and DINOv2 [10], to derive the feature representations from the cropped images of each object. A model trained exclusively with CLIP achieves a robust semantic understanding of images but cannot discern depth and intricate details within those images. On the other hand, DINOv2 demonstrates superior performance in depth perception and excels at identifying nuanced pixel-level relationships across images. As a self-supervised Vision Transformer, DINOv2 can extract nuanced feature details without relying on annotated data, making it particularly effective at identifying spatial relationships and hierarchies within images. For instance, while the CLIP model might struggle to differentiate between two chairs of different colours, such as red and green, DINOv2’s capabilities allow such distinctions to be made clearly. To conclude, these models capture both the semantic and visual features of the objects, which are later used for similarity comparisons in the 3D space.
\
\ A set of pre-processing steps is implemented for processing images with the DINOv2 model. These include resizing, centre cropping, converting the image to a tensor, and normalizing the cropped images delineated by the bounding boxes. The processed image is then fed into the DINOv2 model alongside labels identified by the RAM model to generate the DINOv2 embedding features. On the other hand, when dealing with the CLIP model, the pre-processing step involves transforming the cropped image into a tensor format compatible with CLIP, followed by the computation of embedding features. These embeddings are critical as they encapsulate the objects’ visual and semantic attributes, which are crucial for a comprehensive understanding of the objects in the scene. These embeddings undergo normalization based on their L2 norm, which adjusts the feature vector to a standardized unit length. This normalization step enables consistent and fair comparisons across different images.
\ In the implementation phase of this stage, we iterate over each image within our data and execute the subsequent procedures:
\ (1) The image is cropped to the region of interest using the bounding box coordinates provided by the Grounding DINO model, isolating the object for detailed analysis.
\ (2) Generate DINOv2 and CLIP embeddings for the cropped image.
\ (3) Finally, the embeddings are stored back along with the masks from the previous section.
\ With these steps completed, we now possess detailed feature representations for each object, enriching our dataset for further analysis and application.
\
:::info Authors:
(1) Laksh Nanwani, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work;
(2) Kumaraditya Gupta, International Institute of Information Technology, Hyderabad, India;
(3) Aditya Mathur, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work;
(4) Swayam Agrawal, International Institute of Information Technology, Hyderabad, India;
(5) A.H. Abdul Hafez, Hasan Kalyoncu University, Sahinbey, Gaziantep, Turkey;
(6) K. Madhava Krishna, International Institute of Information Technology, Hyderabad, India.
:::
:::info This paper is available on arxiv under CC by-SA 4.0 Deed (Attribution-Sharealike 4.0 International) license.
:::
\
2025-12-14 02:59:59
Warp is changing how it charges users, making it the latest in a string of coding-tool companies to revise their pricing models.
2025-12-14 01:29:59
2025-12-14 01:00:04
\ Every generation thinks it has reinvented learning. Slides replaced chalkboards. E learning replaced slides. Then came microlearning, gamification, bite-sized content, and apps that flood employees with “nudges.”
\ Yet, ask any HR team a simple question.
\ Are employees actually learning faster today?
\ The honest answer is usually no.
\ Workforces are drowning in content but starved for environments that actually change how people think, feel, and act. The problem is not a lack of information. It is a lack of immersion, a lack of presence, and a lack of emotional engagement. Human beings do not learn through text alone. They learn through states of attention. They learn through experience.
\ This is where virtual reality quietly steps onto the stage. Not as entertainment, not as a toy, and not as a gimmick. As a fundamentally different way to create learning states that the brain treats as real.
\ VR is not the next version of e learning.
\ VR is the first medium that lets us engineer experiences, not just deliver content.
Let us start by being blunt. Most workforce learning is built around the limitations of 20th-century classrooms. Linear, verbal, abstract, and detached from emotional consequence.
\ You can recognize the symptoms:
This is not a failure of motivation. It is a failure of design. The human nervous system simply does not treat passive content as signals worth updating. The amygdala, which filters for relevance and threat, sees no stakes. The hippocampus, which encodes memory, sees no novelty. The prefrontal cortex, which manages attention, is already tired from a full day of task switching.
\ Legacy learning is cognitively expensive and emotionally flat. Which means the brain invests little in it.
\ Workers are not disengaged from learning because they are lazy.
\ They are disengaged because their biology is boring.
Virtual reality breaks this pattern because it communicates with the brain on its own terms.
\ VR does three things that traditional learning cannot.
In VR, you cannot multitask. The browser tabs vanish. Slack notifications vanish. Visual and auditory channels are controlled, which means attention is controlled. The brain cannot treat the environment as background. It becomes the foreground.
\ For HR and L and D teams, attention is the rarest currency. VR gives you the ability to buy it.
People remember experiences, not lectures.
\ Fear, challenge, mastery, and empathy leave deep traces in memory.
\ VR can simulate:
These are “hot cognition” experiences. They activate the emotional circuitry that makes memory sticky and behavior change possible.
\ Traditional learning asks the brain to imagine.
\ VR lets the brain experience.
A well-designed VR module accomplishes in ten minutes what a classroom session takes an hour to convey. Why? Because immersion removes the need for long explanations. The environment teaches.
\ People do not decode instructions.
\ They feel the consequences of their decisions.
\ The nervous system updates faster because it is operating inside a simulated feedback loop, not a hypothetical one.
Aviation figured this out long ago. No one teaches pilots by giving them a PDF and a quiz. They put them in simulators where mistakes do not kill anyone, but the brain treats them as real events.
\ Work has reached the same level of complexity.
\ Modern employees navigate:
Yet, we train them with slideshows.
\ VR offers the workplace equivalent of a flight simulator.
\ It lets organizations stress test skills without real-world cost.
\ Imagine if:
This is training with stakes, and stakes are what make the brain care.
Most companies adopt VR for safety or technical skills.
\ The next frontier is emotional and cognitive performance.
\ In fast-changing environments, people do not fail because they lack knowledge. They fail because the nervous system becomes overwhelmed.
\ VR allows something unusual.
\ It can downregulate the nervous system in minutes.
\ Short reset environments calm the threat response, slow breathing, re-anchor attention, and restore cognitive capacity. This is not entertainment. It is the emotional version of clearing RAM so performance can continue.
\ Employees under pressure do not need more content.
\ They need the ability to reboot their internal operating system.
\ VR makes this possible at scale, inside the workday.
For years, VR was a promise waiting for hardware to catch up. Now, the pieces are aligned.
The question is no longer “Will VR enter the workforce?” but “How fast will it become standard?”
\ If PowerPoint was the language of 2000, and e learning was the language of 2010, VR will be the language of 2030.
\ Because it is the only medium that mimics the features humans learn from best.
\ Experience.
The shift to VR is not a technological trend. It is a philosophical correction.
\ We are finally building learning systems that respect the realities of human biology and cognition. Systems where people do not passively consume content but live through simulations that matter.
\ In the next decade, workforce learning will not be judged by how much content you produce.
\ It will be judged by how effectively you engineer states of attention, emotion, and presence.
\ Those who adopt VR early will gain a workforce that learns faster, adapts faster, and recovers faster.
\ The organizations that cling to old formats will still be asking why their people “don’t retain anything.”
\ Because the truth is simple.
\ The human brain evolved for immersive experience.
\ VR is the first technology that finally gives it one.
2025-12-14 00:02:05
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🪐 What’s happening in tech today, December 13, 2025?
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By @Go [ 11 Min read ] Flight recording is now available in Go 1.25, and it’s a powerful new tool in the Go diagnostics toolbox. Read More.
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