Historical manuscript illustrations preserve rich visual evidence of past cultures. They depict people, animals, plants, diagrams, music notations, and decorative forms. Although large digitization projects have made many manuscripts available online, the material itself remains difficult to explore at scale. Extractio...
Y. Evron, Michal Bar-Asher Siegal, Michael Fire· 0 citations
It is shown, on both LiTS and a 2D endoscopy benchmark, that a large fraction of the network's learnable spatial filters can be replaced by fixed shifts at no cost in accuracy, but that replacing all of them is measurably worse, the placement of spatial capacity matters more than its total amount.
A. Alkhadrawi, Mohammed A. B. Mahmoud· 0 citations
This work introduces feature-space routing: a topology-aware state-space operator embedded directly in the forecasting dynamics, which preserves the physical connectivity of the river system while allowing the propagated state itself to be learned end-to-end.
Mohamad Hakam Shams Eddin, M. L. Taccari, Yi-Kui Zhang et al.· 0 citations
Decomposing MSE into slide-level and within-slide components, it is found that the within-slide component has lower residual-normalized parameter sensitivity in controlled neural experiments, which motivates Component-Guided Loss (CGL), which increases supervision of the within-slide component.
Duc-Tai Nguyen, Thanh Ha Do, Phuong-Thao Cao et al.· 0 citations
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Multimodal contrastive learning is a dominant paradigm for learning transferable representations from unlabeled data, but standard objectives primarily capture information that is redundant between modalities. Partial Information Decomposition (PID) shows that task-relevant information in multimodal data decomposes int...
Accurate assessment of Schistosoma japonicum-associated liver fibrosis is essential for disease management and long-term follow-up in endemic regions. Ultrasound provides non-invasive imaging, but complex local echogenic patterns and anatomical structures make fine-grained grading challenging. Existing deep learning me...
The USAI-Quant benchmark is developed, the first benchmark designed to quantitatively evaluate VLM's reasoning capabilities on built environment metrics via remote sensing imagery, and reveals that current state-of-the-art models consistently fall short on numeric reasoning tasks.
Dong-Dong Wang, Q. Song, Yu-Zhou Chen et al.· 0 citations
Counterfactual Trace On-Policy Distillation (CT-OPD), which combines completed teacher responses with trajectory masks from the current student, consistently enhances multimodal understanding and reasoning capabilities, and improves both visual understanding and image generation.
Long Qian, Bing-Ke Zhu, Jia-Qi Wei et al.· 0 citations
OmniMoE-VL, a sparse VLM with a coupled visual-depth routed projector, enables question-dependent visual access while preserving the native visual-token sequence, and complements token-level expert routing in the vision and language stacks.
Long Qian, Bing-Ke Zhu, Jia-Qi Wei et al.· 0 citations
A controlled empirical study of which design choices allow visuomotor policies to generalize across viewpoints finds that viewpoint generalization improves when dense visual tokens are retained and the action head participates in geometric reasoning.
Mino Nakura, Sriram Krishna, Yu-Fei Wang et al.· 1 citation
A measurement account of how joint-embedding predictive architectures are unusually sensitive to how the input is masked, finding that coarse-scale content the mask leaves unrecoverable, provided enough context stays within reach of each target is given.
LT-OPD, a training framework for extreme visual-token reduction, is proposed and it is shown that on-policy learning can substantially recover capabilities lost to extreme visual-token reduction.
Junxian Li, Rui-Xuan Yang, Tian-Ao Zhang et al.· 0 citations
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
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.
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