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3,462 papers
This paper introduces SafeAtlas-VL, a dataset of 1.5M training instances that places image-, request-, and response-level judgments on a five-level ordered scale, and trains the SafeAtlas Guard series of models via target-conditioned tuning for multimodal safety detection.
EviAnchor is proposed, a training-free and single-branch inference framework that preserves and reactivates visual evidence throughout generation in large vision-language models and demonstrates consistent improvements in visual grounding.
A disentangled representation learning (DisenRL) framework, named the Attributes-based Gaussian Estimation for Disentangled Representation (AGEDR), which incorporates Attribute Mapping Embedding modules designed to map attributes into vectors and align them with a subset of the latent vectors in a Variational AutoEncod...
This review aims to formalize XAI methods in CompPath through the introduction of a pathology-centric vocabulary comprising seven core terms and establishment of a task-driven framework that maps five clinical questions to recommended methods, method evaluation, and deployment context.
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Inspired by diagnostic practice synergizing numerical assessment, visual inspection and clinical context, MedTVL is introduced, a text-guided dual-pathway architecture tailored for MedTS classification that synergizes a convolution-based temporal pathway for fine-grained temporal dynamics from raw numerical sequences a...
A comparative system using artificial intelligence to detect Parkinsons disease by analyzing the motion and tremor data captured by an inertial measurement unit IMU shows that the proposed machine learning approach offers an accurate and effective predictive capability in the classification of PD severity.
Hyper3-CLIP is presented, a hierarchy-conditioned hyperbolic VLM that combines global, local, and global-local contrastive learning with query-conditioned visual pooling and improves R@5 and R@10 retrieval on COCO and Flickr, as well as multi-label classification on VOC and COCO, while remaining competitive on hierarch...
The proposed MaskFlow, a training framework for precise localization, consistent background preservation, and seamless boundary transitions, incorporates the mask into the probability path and flow-matching objective, coordinating generation within the editable region with source preservation outside it.
This work builds a controlled AD benchmark of 17 realistic QA-relevant anomaly types from six public datasets and proposes a taxonomy of radiological image anomalies based on human visual perception, enabling fine-grained analysis of AD failure modes, establishing a foundation and practical guidance on scalable unsuper...
Multimodal information systems increasingly route generated visual content back through the same vision-language index that informed its production, so the output must remain retrievable by the queries it was meant to serve. When the scene contains entities at vastly different scales, existing language-guided generator...
The empirical gap between these method families is identified as a variance-reduction effect rather than a difference in RL principle, and a multi-sample KDE value-gradient estimator that reuses rollout groups, together with scale-bounded weight families that retain stable existing recipes while excluding singular ones...
The proposed Deep Evidential Regression for forest height estimation on the TreeUQ benchmark, a large-scale dataset designed for the joint estimation of tree count and average tree height at 10 m resolution, achieves predictive performance comparable to a deterministic U-Net while additionally providing well-calibrated...
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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