Joint lesion segmentation and tissue classification in breast ultrasound are usually trained with a shared encoder, so the two branches stop exchanging information once their decoders separate. That is exactly where boundary detail and semantic evidence are most complementary. The proposed method restores this exchange...
Abdullah Al Shafi, Md Kawsar Mahmud Khan Zunayed, Safin Ahmmed et al.· 0 citations
It is found that though continual learning methods help, fine-tuning reduces robustness across pretrained models, suggesting that starting with the strongest foundation model is not necessarily the best approach for performance on specialist tasks.
Jaedong Hwang, Brian Cheung, Zhang-Wei Hong et al.· 0 citations
Ego2World is introduced, an executable benchmark that turns egocentric cooking videos into executable symbolic worlds governed by graph-transition rules and shows that persistent belief memory improves task completion while reducing repeated visual exploration, suggesting that belief maintenance should be a first-class...
Qin-Chuan Cheng, Zhan Gong, Peng-Zhan Sun et al.· arXiv.org· 1 citation
Existing methods for 4D hand-object reconstruction often rely on costly per-sequence optimization, while generative approaches typically synthesize interactions from random noise, which can lead to unstable interaction prediction. We introduce 4D-HOF, a feed-forward framework that reconstructs 4D hand-object interactio...
Shi-Qi Li, Sean Cho, Yi-Jie Li et al.· 0 citations
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World models offer a data-driven alternative to traditional simulators for robotics, with applications spanning policy evaluation, improvement, and planning. All of these uses depend on faithful 3D geometry, yet current video-based world models are trained on RGB alone and produce rollouts that look correct frame-by-fr...
Jai Bardhan, Josef Sivic, Vladimír Petrík· 0 citations
WorldSonus, an interactive video-to-audio framework designed for real-time spatial sound synthesis in world models, generalizes effectively to open-domain video-to-audio benchmarks, matching or outperforming state-of-the-art bidirectional models in both acoustic quality and spatial alignment.
Peng Fang, Jing-Yi Fa, Kam-Man Wu et al.· 0 citations
This work isolates the RL-stage update, retains its dominant matrix-wise directions with magnitude preservation, and transfers them to the language modules of a VLM, which improves full-update interpolation in 12 of 15 comparisons, including an 8.55 percentage-point MathVision gain on the Qwen recipient.
Su-Xin Ji, Hung-Tao Wan, Ming-Jun Liu et al.· 0 citations
Clinical diagnosis is inherently a structured reasoning process, yet existing deep learning models often bypass this structure by mapping image features directly to disease labels without explicitly interrogating the morphological and textural criteria that clinicians systematically evaluate. This limits diagnostic tra...
Asim Khan, Samee Ullah Khan, Dwarikanath Mahapatra· 0 citations
Vision-language models (VLMs) are increasingly being applied to three-dimensional medical imaging, but their application to knee MRI remains limited, particularly for interpreting the complementary sequences used in clinical practice. We introduce Knee3DVLM, a sequence-aware VLM that uses full-volume DESS and fluid-sen...
Maryam Baizhigitova, A. Yu, Po-Hao Chen et al.· 0 citations
While recent vision-language models (VLMs) have shown outstanding performance across diverse applications, they tend to under-use visual information and over-rely on textual context. In this work, we propose \textsc{GeoPID}, a training-free framework that analyzes multimodal information within VLMs from a geometric per...
Seulgi Kim, Zhixiong Zhang, Xin-Wei Zhang et al.· 0 citations
The rapid integration of Vision Language Models (VLMs) into sensitive systems introduces critical safety vulnerabilities that remain unexplored in exist studies. While adversarial attack robustness has been extensively studied for image-based models, the susceptibility of VLMs to temporally-aware adversarial attacks ag...
Heyam Bin Jahlan Areej Alhothali Abeer Alhothali· 0 citations
Product Identification has sprung up to become one of the most challenging problems in the automation of the retail industry. With the new industry 5.0 standards, automated inventory management, and catalog creation tasks are vitally important. Object identification models have emerged as a viable answer with their unp...
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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