VideoScout is proposed, a multi-turn reasoning agent that instantiates the SEA paradigm through adaptive reasoning pacing and enables efficient traversal of long videos within a bounded visual context window, allowing the agent to access more video content while balancing content analysis depth with reading efficiency.
Wei-Xin Xu, Zhen-Yu Yang, Bing Wang et al.· 0 citations
The model outperformed PET2REP and a CT-based baseline across standard report-generation metrics, improved performance across VQA question types, and achieved higher Dice and lesion-level overlap F1 than SegAnyPET and nnUNet.
Y. Xing, Jiong Wu, Savas Ozdemir et al.· 0 citations
PIVOT, a physics-grounded AIGC detector instantiated here for audio-video clips, that estimates physical quantities from video and audio, selects physical laws relevant to each clip, and verifies their measurable constraints, demonstrates the practical promise of physical-consistency verification as a structured and in...
Bo-Yuan Zheng, Kang-Ran Zhao, Xiao-Yu Zhang et al.· 0 citations
Using food package images to monitor sodium and salt content against South Africa's R214 sodium limits is challenging when screening decisions require product identity, nutrition facts panel evidence, reporting basis, and category-specific thresholds. This study presents a conservative image-based workflow that combine...
Mayimunah Nagayi, A. Khan, Tamryn Frank et al.· 1 citation
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This paper proposes an end-to-end framework for cell detection and classification that jointly models patch-level visual representations and instance-level interactions, which incorporates a dynamic graph construction module and an instance-aware graph network.
Ruo-Chen Liu, Ya-Lin Zheng, Jing-Xin Liu et al.· 0 citations
Experiments on BUS-CoT and IU X-ray datasets demonstrate consistent improvements in diagnostic accuracy, concept consistency, and report quality over strong general-purpose and medical MLLMs, indicating that concept-grounded reasoning better aligns generation with clinical decision processes.
Xin-Yue Xu, Hong-Bin Lin, Juan-Gui Xu et al.· 0 citations
We propose a pre-fine-tuning probing method for Parameter-Efficient Fine-Tuning (PEFT) layer selection, aiming to obtain more stable and higher gains with fewer trainable parameters when adapting large vision--language models (VLMs). Unlike the common practice of applying LoRA and other adapters to all layers at once--...
Qingtao Xia, Jiahua Bao, Siyao Cheng et al.· 0 citations
This paper proposes PACE, a two-stage framework that progressively expands both the representation and trainable parameter spaces, and introduces Focal Embedding Loss, a confidence-adaptive objective that downweights queries with high positive retrieval confidence while emphasizing ambiguous queries with competitive ne...
Yan-Ping Li, Wei Zhou, Ya-Wen Liu et al.· 1 citation
AdaVSkip is proposed, which equips each layer with two lightweight routers that independently determine whether visual tokens pass through by or skip the self-attention and MLP modules, and maintains strong task performance with substantially less computation.
Yu-Yao Sun, Tao Deng, Shuang-Hua Li et al.· 0 citations
The frozen collection of ChatGPT Images 2.5 supports analysis of reported model use during a product transition, with source and attribution evidence retained for interpretation, and is released at https://scam.ai/research.
A lightweight, prompt-free extractor that decomposes endpoint recovery into an image-like anchor and a noise-oriented residual, which increases the capability of the model to utilize GPU parallel computation, providing an efficient one-step extraction pipeline with a concise theoretical interpretation.
This is the first study to formulate image-grounded review assistance as a multi-agent vision-language reasoning problem, providing a practical step toward AI-assisted review authoring in e-commerce systems.
U. Nareti, Ayush Bansal, Kumari Priya 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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