Video streaming dominates global internet traffic, yet conventional pipelines remain inefficient for structured, human-centric content such as sports, performance, or interactive media. Standard codecs re-encode entire frames, foreground and background alike, treating all pixels uniformly and ignoring the semantic stru...
Emanuele Artioli, Daniele Lorenzi, Shivi Vats et al.· 0 citations
Precise multi-camera tracking traditionally relies on rigorous 3D site calibration, yet this requirement is often operationally impossible in large-scale deployments. Privacy regulations frequently prohibit recording video for offline calibration; limited bandwidth precludes synchronizing high-resolution streams from h...
David Voihanski, Mor Sinai, B. Bobrovsky· 0 citations
This work introduces physical rank consistency (PRC) to measure how well tokenization preserves local physical distance rankings after reconstruction, and presents ActionPiece, which preserves physical action relationships through joint supervision of representation learning and quantization.
Shijie Lian, Bin Yu, Zhao-Long Shen et al.· 1 citation
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.
Tian-Bin Liu, Jian Zhu, Taiyi Su et al.· 0 citations
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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.
Chun-Pu Xu, Zhi-Xuan Liang, Yu-Hao Zhang et al.· 0 citations
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.
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.
Satyam Gaba, Krutiksinh Rana, Siva Sai et al.· IEEE Internet of Things Jour...· 6 citations· ⚡1
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.
Zhi-Xin Cheng, Jia-Cheng Deng, Xiao-Tian Yin et al.· IEEE transactions on multime...· 0 citations
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...
Sihao Ding, Santosh Vasa, Aditi Ramadwar et al.· 0 citations
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.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
