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3,080 papers
The Causal Semantic World Action Model (CSWAM) is presented, which augments FastWAM with a causal semantic expert built on V-JEPA 2.1, which provides temporally grounded representations of semantic state changes and motion with less dependence on appearance-specific details.
A novel method is proposed for detecting meaningful single-pixel deviations in images that is free from user-specified thresholds and parameters, and can be applied to both binary and grayscale images, providing an effective, robust and efficient solution.
Experimental results demonstrate the proposed ${M}^2$Tok, a Multi-head Multi-codebook Action Tokenizer designed to minimize reconstruction error and enhance policy performance, achieves superior reconstruction fidelity and significantly boosts the success rate of VLA models.
Lighter than ViT, which is effective in capturing long-range dependencies, the proposed CNN-integrated CCT has also proven efficient in breast cancer classification by capturing long-range dependencies among breast tissue regions.
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CapMap-MS-TTA is presented, a training-free submission built on Microsoft Florence-2-base, combining caption keyword mapping with multi-scale flip test-time augmentation, and ranks 3rd on the final MUMU leaderboard.
This article comprehensively analyzes GPAI systems, focusing on their architectural foundations, current applications, and key limitations, and summarizes promising research directions in data-efficient learning, sim-to-real transfer, edge-compatible architectures, and safety frameworks.
The Intermodal Dual-MAE Framework with a Similarity-based RL Masking Strategy (SRLM) is proposed, which adaptively masks informative positions by leveraging cross-modal similarity and reinforcement learning, thus narrowing the modality gap.
Text-to-image generative models are widely used in professional and creative settings, yet how they represent gender across occupations -- and whether newer models are fairer -- remains poorly understood across multiple generations. We evaluate gender representation across 20 occupations, 5 prompt templates, and 4 Stab...
Vision-Language Models (VLMs) can produce Natural Language Explanations (NLEs) that sound plausible yet remain inconsistent with the visual evidence they cite. We present Explanation-Driven Counterfactual Testing (EDCT), an intervention-based protocol that extracts visual concepts cited in a model's explanation, applie...
While the proposed method outperforms many state-of-the-art competitors, all methods remain below a micro-averaged frame-level AUC threshold of 70% in the most challenging evaluation setup, confirming the difficulty of the proposed benchmark.
QT is introduced, a Quantum-inspired Transformer for vision tasks with three components: angle-inspired encoding that maps image tokens to learned trigonometric Hilbert-space features analogous to quantum rotation-based state encoding; self-attention over these periodic features, inducing a classical cosine kernel appr...
This work formalizes update promotion as certified paired risk-difference auditing as well as building DISCERN, a sequential two-tier protocol that proves finite-sample validity and matching label-complexity bounds of order rho^2/eps^2 at the rate level.
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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