Photographic evidence is becoming increasingly vulnerable to forms of alteration and fabrication that existing legal and technical workflows are not well equipped to evaluate. Surveillance frames, dashcam stills, and phone photographs may be used to establish presence, sequence, causation, damage, or identity, yet cont...
Kelly McConvey, Sajad Ebrahimi, Nima Jamali et al.· ACM International Conference...· 0 citations
This work introduces HiRAE (Hierarchical Representation Autoencoder), which learns a hierarchical fusion framework over the full encoder hierarchy to improve reconstruction fidelity while maintaining compatibility with generative modeling.
Xuan-Yu Zhu, Yan Bai, Yang Shi et al.· 0 citations
Pulmonary embolism (PE) is a leading cause of cardiovascular mortality, yet the real-world performance of FDA-cleared AI detection models remains incompletely characterized. We retrospectively evaluated two FDA-cleared AI algorithms from a single commercial platform (Aidoc Medical BriefCase), one for PE triage on dedic...
Aawez Mansuri, Mohammadreza Chavoshi, T. Dapamede et al.· 0 citations
This report presents PolyOCR, a family of unified OCR foundation models of varying scales that combines a shared instruction-following framework with a large-scale data engine that converts heterogeneous visual resources into quality-verified OCR supervision.
Guang-Zhan Huang, Yong-Shuo Zhang, Bing-Tao Fu et al.· 0 citations
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VIF-Bench is introduced, a benchmark of 1,241 tasks designed to assess the edge of model capabilities in this joint setting in which multiple references must be composed under multiple and heterogeneous visual-instruction images, and uncover three findings.
Yuta Oshima, Masakazu Yoshimura, Masahiro Suzuki et al.· 0 citations
FedSocket makes exchanged knowledge directly usable from federated training to recipient inference, demonstrating the value of combining local and exchanged predictions.
TReVS is proposed, a training-free framework that combines textual relevance with vision-encoder saliency for pre-LLM pruning and leverages high-variance attention heads to remove task-irrelevant tokens at shallow-to-intermediate layers of the LLM.
Jing Wang, Zhi-Ping Wu, Dong-Dong Ren et al.· 0 citations
The analyses show how VLM evaluation behaves as model panels grow and evolve, providing guidance for designing future benchmarks that are more efficient, robust to model turnover, and explicit about the limits of evaluation-side pruning.
Modern vision-language models (VLMs) have shown promising results in long-video understanding due to the rich semantic information they can capture. However, most methods focus on coarse captioning of extracted image frames that are computationally inefficient and require models with large context windows. While past w...
Arka Mukherjee, Kaleen Shrestha, Larissa Zhu et al.· 0 citations
This work introduces ENCORE, which gives the agent a few demonstrations as evidence to read rather than as training data, and shows that on RoboDojo tasks whose instructions leave the goal unstated, no program succeeds without demonstrations.
Yi-Fan Kang, Zihan Wang, Zhi-Wen Fan et al.· 1 citation
Trajectory-Aware Evidence Coordination (TAEC), a training-free framework that coordinates evidence use around unresolved answer requirements, achieves the best overall performance against leading training-free visual RAG baselines, with the highest average accuracy across multiple proprietary vision-language models.
Ya-Lun Wu, Bing-Zhou Wang, Bo-Yang Wang et al.· 1 citation
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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