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3,080 papers
DyMD, a DMD framework that adapts both teacher supervision and critic fitting to the evolving student with no auxiliary modules at inference is proposed, and a 14B teacher is distill into a four-step 1.3B student with no auxiliary modules at inference.
Ordinal acne severity grading requires distinguishing visually similar neighboring grades while jointly weighing holistic facial appearance and localized lesion burden - evidence that most existing approaches collapse into a single opaque representation. We introduce CG-HAF, a global-local fusion framework that instead...
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder for which early and accurate diagnosis is critical to improving long-term developmental outcomes. However, existing ASD recognition methods are often constrained by the scarcity of diagnostic text data, forcing them to rely mainly on visual analysi...
DeformView is introduced, a wide-baseline MV dataset with pixel-level annotations of geometric inconsistencies and DEFECt3R is proposed, a lightweight learning-based classifier that uses cross-view feature relationships to localize geometric inconsistencies at the pixel level.
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This paper proposes ReG-SAM, a SAM-based framework tailored to 2D vessel segmentation that leverages reference graph set for enhancing vascular representations and introduces two modality-aware representations derived from the reference masks.
Cross-site lung histopathology classification must account for stain variation, non-IID client data, missing classes, and the cost of adapting large pathology encoders. This study evaluates FedHisto-PAST v2 for three-way classification of adenocarcinoma (ACA), Normal, and squamous cell carcinoma (SCC). FedHisto-PAST v2...
Pocket-STVG is presented, a lightweight cascade architecture that addresses STVG by combining efficient pre-trained components instead of large end-to-end models and establishing a favorable performance-efficiency trade-off for STVG.
Spatial reasoning with metric constraints requires linking objects to geometric measurements and preserving their numerical content during language reasoning. We present DepthEvidence, a 4B model that uses its own dense metric predictions as object-grounded evidence for language generation. A camera-conditioned decoder...
A scalable hybrid Quantum Diffusion Model is presented, based on a Discrete-Time Quantum Walk algorithm, executed on a real quantum device, to model the forward dynamics of the diffusion model, and its use for medical image analysis is evaluated.
The exact best-case limit for any image-only verifier is given: the largest robust target-acceptance gap equals the minimum total-variation distance between the target distribution and the set of attacked source distributions.
FLIP is a validation step for intervention-based mechanistic interpretability, not a steering method, for testing whether a logit-facing intervention site in an open-weight vision-language model (VLM) supports structured, task-linked computation rather than generic perturbation.
Across generator swaps, including substitutions with similar model versions and model variants, FARE is effective at detecting swaps, consistently outperforming existing baselines at strict operating points, and remains effective under the exact-model and decision-only attacks evaluated in this work.
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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