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3,080 papers
MUSE decouples image annotation from question generation, enabling diverse tasks with controllable difficulty while reducing annotation effort, and it is hoped MUSE will serve as a standardized benchmark for advancing multi-modal understanding in situated educational applications.
ERPBench is introduced, a benchmark that evaluates screenshot-only agents on a live and reproducible system and scores each task against ground-truth values in its database and presents a production-grade harness that gates agent actions behind human approval for safe deployment.
Results support the effectiveness of caption memory for episodic reasoning over long egocentric video in wearable assistants with bounded frame budgets, growing visual-token costs, and long-context retrieval failures.
UAV on-board vision systems are widely used for different activities, including monitoring in no-fly zones. In this case, the vision-equipped UAV streams a video to a ground server where an operator assists its activities. The latency of video transmission has a profound impact on the effectiveness of the operator assi...
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Visual in-context learning (VICL) solves visual tasks by conditioning on a few input-output demonstrations without any model training. Recent advances in large vision-language models (VLMs) have shown promising VICL capability when the demonstration pair and the query belong to the same vision task, but real use cases...
A novel edge-aware transformer-based network, named RoofSeg, is developed for segmenting roof planes from LiDAR point clouds in a truly end-to-end manner and an Edge-Aware Mask Module (EAMM) is designed that sufficiently incorporates planar geometric prior of edges to enhance its discriminability for plane instance mas...
This work investigates whether CLIP embeddings inherently contain information indicative of AI generation, and proposes a proposed pipeline that extracts visual embeddings using a frozen CLIP model, feeds its embeddings to lightweight networks, and fine-tunes only the final classifier.
A representation-aware and frequency-aware evaluation framework that measures internal embedding drift, spectral sensitivity, and structural smoothness (spatial consistency of vision tokens), alongside standard label-based metrics is introduced.
PhysStream is an autoregressive model for physics-grounded image-to-video synthesis that incorporates structured scene memory and supports fine-grained motion control via sparse velocity-increment signals that encode physical quantities, letting the model learn the underlying dynamics.
Det‐LIME is proposed, a detector‐aware, multi‐instance adaptation of Local Interpretable Model Agnostic Explanations that produced instance‐specific, box‐aligned explanations by combining per‐detection weighting, a proximity kernel that emphasizes regions near each box, and Intersection‐over‐Union‐based matching to tra...
An occlusion-robust target tracking framework that maintains target identity and trajectory continuity through the integration of YOLOv11n object detection, Kalman Filter motion prediction, and occlusion-aware appearance-based re-identification is proposed.
Oracle bone script (OBS), one of the earliest Chinese writing systems, plays an important role in the study of Chinese etymology. Traditional decipherment relies heavily on domain experts who analyze characters through semantic context and structural evolution. To assist this labor-intensive process, we formulate OBS d...
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
A Blog post by H company on Hugging Face
A Blog post by Liquid AI on Hugging Face
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
Human-Computer Interaction and Visualization
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