Multi-organ segmentation using deep learning requires large amounts of annotated patient data; however, institutions often lack sufficiently large and diverse annotated datasets. Privacy constraints further prevent institutions from sharing patient data to overcome this limitation. Moreover, due to the labor-intensive...
This work presents a method for shape reconstruction and tracking from video via agentic analysis-by-synthesis that infers a structured 3D object model, including its geometry and kinematic structure, and uses this model to optimise object track estimates over time.
Kirill Mazur, Nikita Karaev, Matthew Chang et al.· 1 citation
Experiments on FastVLM-1.5B across multiple vision-language benchmarks demonstrate that VPRune achieves a favorable accuracy--compression trade-off, with particularly pronounced advantages under aggressive compression, demonstrating its practicality for resource-constrained LVLM deployment.
Recent 4D LiDAR language models aim to reason about objects and their evolving spatial relationships. Yet, in our evaluation, always selecting the same option nearly matches the multiple-choice accuracy of two B4DL-derived configurations. We introduce LiDAR-Hallu, a geometry-referenced benchmark and diagnostic protocol...
Run-Yi Yang, Murat Akkoyun, Di Wen et al.· 0 citations
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This framework proposes two key innovations: Transformation-Isomorphism Supervision for hand-depth information extraction and Perspective Information Embedding for resolving above coupling effect of local pose and wrist position, both integrated within the mainstream encoder-decoder architecture.
Kai-Wen Ren, Yi-Ran Jiang, Yong-Jing Ye et al.· 1 citation
Image forensics is increasingly an open-world problem: manipulations range from fully synthetic images to localized edits, splicing and swapping, while most forensic detectors remain specialized to a single manipulation family. Agentic AI has recently emerged as a promising solution. In principle, such systems can asse...
Xian-Long Li, Pietro Bongini, N. Pancino et al.· 0 citations
Visual search on large e-commerce catalogs must serve both"similarity"queries that ask for items resembling an uploaded image and"modifier"queries that comprise an image and text describing a desired modification (e.g. a color change or style swap). The latter is the setting known as composed image retrieval (CIR). Exi...
Anubhav Gupta, Hrushikesh Mohapatra, Prijith Chandra et al.· 0 citations
Four-dimensional (4D) Radar has emerged as a key sensor for environmental perception, providing range, azimuth, elevation, and Doppler measurements while remaining robust to illumination changes and adverse weather conditions. However, conventional Radar preprocessing methods, such as constant false alarm rate (CFAR) d...
Atomic activity understanding aims to recognize and localize structured traffic behaviors that jointly encode motion patterns and their grounding in road topology. Unlike conventional action recognition, atomic activities are multi-agent, multi-label, and topology-aware: multiple activities co-occur while many agents r...
Yu-Ho Chang, Chi-Hsi Kung, Yi-Hsuan Tsai et al.· 0 citations
InterHier utilizes a prepended learnable context to globally guide the interpretation of prompts containing hierarchical relationships and offers high versatility and allows for seamless integration into existing OVD models.
Yeong-Jin Kim, Ho-Joong Kim, Seong-Whan Lee· IEEE Access· 0 citations
Prefix-Steered Recurrent Memory (PREM), a memory-token-free framework for frozen vision-language models (VLMs) that separates video ingestion from query answering, consistently outperforms frozen baselines at every evaluated visual budget.
Si-Ru Zhong, Qiong-Yan Wang, Xiao-Hui Lv et al.· 0 citations
Prompt learning efficiently adapts vision-language models (VLMs) to downstream tasks, but gains on seen classes often come at the expense of generalization to unseen classes. To address this limitation, we propose prompt ensembling with training-free routing (PETR), whose key innovation is a carefully designed dual-pro...
Wei-Han Cai, Hao Tan, Xin-Ping Gao 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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