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3,363 papers
The unique challenge of jointly integrating perception, understanding, and reasoning for hazard identification poses substantial headroom for further improvement on LogiScope-VQA, and reveals the pervasive security bias issue that impedes LLMs'practical deployment in real-world settings.
A novel framework, Distilling Image Prototype for Guided Test-Time Adaptation (DIPTTA), a compact set of synthetic images that serves as a dynamic and regenerative anchor of source knowledge that anchors a source-calibrated uncertainty estimation method.
A two-step unknown-object discovery mechanism is designed and a Hyperbolic Metric Learning (HML) strategy is developed that enhances inter-class separability, facilitating the incorporation of novel categories while mitigating catastrophic forgetting.
Neural radiance fields (NeRFs) and 3D Gaussian Splatting (3DGS) encode a scene with complementary inductive biases, but existing cross-representation distillation typically fixes one representation as teacher for the entire scene. A globally fixed teacher can propagate local reconstruction errors. We present RouteBridg...
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A state-of-the-art echocardiographic tracking method is extended with persistent memory tokens that share information across sliding windows over full cardiac cycles, supporting more reliable myocardial strain estimation in clinical practice and improving test-retest reproducibility.
As Vision-Language Models (VLMs) advance toward physical deployment, the focus has remained on action-oriented Embodied AI evaluated on subject-centric consumer video. This overlooks a pervasive class of Physical AI: Infrastructure AI, which relies on fixed cameras for open-loop insights like safety monitoring and oper...
Nine metrics that make a verifier claim checkable, and coordinates for the verifiers still to be built, are closed with nine metrics that make a verifier claim checkable.
High classification accuracy alone is insufficient for clinical image analysis, where calibrated confidence and reliable uncertainty estimates are essential. This study proposes a reliability-aware Hybrid-K ensemble selection framework for multiclass cervical cytology classification using the SIPaKMeD dataset. Nine dee...
Advertising video generation is not only a video synthesis task, but also a product-conditioned reasoning problem whose success is measured by online business metrics. Recent video foundation models can generate realistic clips from multimodal conditions, yet they do not optimize how a product should be transformed int...
This work systematically investigates the role of text in multimodal medical image segmentation and proposes an Evidence Decoupling Decoder (EDD) based on evidential deep learning and deep supervision, finding that text affects predictions mainly through global semantic modulation rather than independent spatial locali...
AMID is introduced, an autonomous multi-agent framework for medical imaging model development that outperformed evaluated general-purpose MLE systems and approached or matched strong human-designed challenge solutions across heterogeneous tasks.
Generative design interfaces increasingly expose semantic controls that let users steer output with concepts such as"more elegant"or"more minimalist,"typically encoded by a vision-language model (VLM). A practical question is whether state-of-the-art VLMs represent objects consistently in terms of the same concept. We...
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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