Infrared (IR) imaging is crucial for autonomous driving, remote sensing, and other perception tasks. However, adverse weather may introduce fake structural responses that are entangled with real thermal structures. Existing IR restoration methods are typically designed for a single degradation type or directly reconstr...
Xin-Yao Wang, Li-Jun He, Zhi-Han Ren et al.· 0 citations
Surgical procedures follow a phase-to-step hierarchy, yet the video-language models used to recognize them are evaluated with flat per-level metrics that ignore cross-level coherence and error structure. In this paper we make two contributions to address this problem, (i) we introduce SurgHiBench, the first hierarchy-a...
Ana Manzano Rodríguez, P. Mettes, M. Schijven et al.· 0 citations
This paper proposes a system to improve cricket coaching that takes raw video data, extracts batsmen from video frames using YOLO, and extracts 3D pose data from video frames using MeTRAbs, and proposes a deep learning ensemble for shot classification of four shots: flick, pull, defense, and drive.
Sourav Shome, M. D. Ashiquzzaman Rahad, Rameswar Debnath· 0 citations
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Background: Clear cell renal cell carcinoma (ccRCC) exhibits substantial clinical heterogeneity, and accurate grade assessment is essential for risk stratification and treatment planning. However, conventional grading requires invasive tissue sampling. We developed RCC-Align, a cross-modal contrastive learning framewor...
Amit Das, Tanmay Shukla, Naofumi Tomita et al.· 0 citations
Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as evidence a model is robust enough for deployment. Amateur team sport is a useful, largely untested place to check that assumption: over eight million students played a schoo...
Sai Varun Kodathala, Prashanth Pollishetty, J. Cargill· 0 citations
The NV-Reason-CT model, a generative vision--language model for chest and abdominal CT combining native 3D visual encoding with radiologist-guided reasoning, and the model and training code are released to support reproducible research on explainable AI for volumetric medical imaging.
Andriy Myronenko, Dong Yang, Yu-Cheng Tang et al.· 0 citations
A controlled behavioral study of rendered 3D avatars across a pristine condition and fourteen geometric, photometric, temporal, and combined distortions reveals a quality--accuracy dissociation where distortion families affect appearance and communication differently.
Ning-Hsuan Chang, Kai Ma, Yu-Chih Chen· 0 citations
This work systematically analyzes all 1,702 CHI 2026 full papers and identifies 125 that annotate videos, and derives a five-dimensional taxonomy spanning analytic purpose, viewpoint, phenomenon, reasoning requirement, and annotation authority to map the capabilities and limitations of a general-purpose VLM.
Xi-Yuan Shen, Jiuyang Lyu, Seokhyun Hwang et al.· 0 citations
Generative image models can produce highly realistic images, raising concerns about potential misuse in creating deceptive content. Current deepfake approaches face several challenges, including limited generalizability, lack of interpretability, and poor actionability. To help address these, we present ASAP, an intera...
Jinbin Huang, Yuki Ueno, Chen Chen et al.· Information Visualization· 0 citations
This work proposes DreamAvoid, a critical-phase test-time dreaming framework that enables VLA models to anticipate and avoid failures, and introduces an autonomous boundary learning paradigm to refine the system's understanding of the subtle boundary between success and failure.
Benchmarks for vision-language models offer their answer choices in some convention: a letter, a color name, a pixel coordinate. That convention is treated as neutral. We find it is not, and that the limits a benchmark reports can belong to the readout rather than to the model.
On 200 COCO photographs, Qwen3-VL-4B pi...
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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