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3,462 papers
ViSAR (Visual Semantic Activation Retrieval), a training-free adaptive-$k$ retrieval method for late-interaction visual document retrieval, is introduced and it is shown that the similarity matrix structure correlates with answer accuracy, suggesting future directions for retrieval quality-aware document understanding.
The findings suggest that the integration of localized feature representation and optimized classification provides a reliable and resource efficient alternative for medical image analysis, offering a structured solution that maintains performance without the need for extensive computational overhead.
A Video Compression framework built upon a foundational flow model that enables the compressor to harness generative video flow priors effectively, which reduces bit consumption by 58\% and achieves one-step decoding and reconstructions with high perceptual fidelity.
Mineral image classification is important for geological exploration and resource development, but it remains challenging due to substantial intra-class variations in appearance and high inter-class visual similarity. Multi-cognitive Visual Adapter (Mona) is a vision-oriented parameter-efficient adapter that adapts pre...
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A self-referential retrosynthesis framework for explainable AI provenance forensics under a fixed-generator setting that leverages a jointly optimized encoder-decoder pair to implement a self-embedding mechanism that enables round-trip consistency verification.
SAUF-Net is proposed, a Structure--Appearance Representation Learning with Uncertainty Feedback Network for semi-supervised medical image segmentation that outperforms state-of-the-art semi-supervised methods, especially under low-label settings.
Results show that small grayscale patches can inject chosen target semantics across IR-VLM families under a controlled digital threat model, motivating stronger robustness evaluation for infrared multimodal systems.
Post-hoc saliency maps such as Grad-CAM are increasingly used to audit why a deployed vision model made a decision, yet the heatmap drifts when the input is rotated, even when the prediction is unchanged. In domains with no canonical orientation, such as histopathology and aerial imagery, this undermines using saliency...
Dermatology artificial intelligence (AI) models are predominantly trained on light-skinned, cancer-focused image collections, yet they are increasingly proposed for deployment in resource-constrained settings where patients differ from training populations along two confounded axes: skin tone and disease distribution....
Federated LoRA adaptation improves shared-class AUC on all four cohorts over the unadapted BiomedCLIP backbone over the unadapted BiomedCLIP backbone, showing that the gains come from federated adaptation rather than from the pretrained model's zero-shot ability.
InstEditSeg is proposed, a unified generative framework that reformulates medical segmentation as an instruction-driven image editing problem, and achieves accuracy competitive with strong discriminative baselines, and demonstrates concrete advantages of the generative formulation.
InsightSeg is introduced, an episodic memory mechanism that converts successful correction episodes into reusable, visually grounded insights that improves both first-pass and final guideline-consistent segmentation performance while requiring fewer refinement steps, demonstrating that multi-agent refinement can become...
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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