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
Reach audiences
Advertise in front of researchers, engineers, and readers.
The SYNTAX score is a clinically established tool for assessing anatomical lesion complexity in coronary artery disease and guiding subsequent treatment. However, automated SYNTAX scoring is commonly formulated as a direct regression problem from coronary angiography videos to patient-level scores. In this work, we ref...
Suzhong Fu, Jingqi Dong, Xuan Ding et al.· 0 citations
PhysReflect is presented, a geometry and perception guided diffusion framework that decodes the predicted clean latent into pixel space at each training step and applies annealed supervision through two complementary differentiable objectives.
Shu-Heng Ge, Hongwei Ren, Li Zhang et al.· 0 citations
This work contributes the first application of Vision Transformers (ViT) and Self-Supervised Learning (SSL) to the shrimp farming domain, addressing both performance bottlenecks and data labeling challenges.
Vinh Thị Thành Trương, H. Pham, Ngoc Hong Tran· 0 citations
GrapeSplat amalgamates multi-view cues into a voxel-aligned scene representation and decodes Gaussians directly from the learned grid, requiring no per-scene optimization or post-processing.
Si-Yu Lu, Yung-Yao Chen, Yi-Jan Chen et al.· 0 citations
QwenVLConnector is presented, a Qwen2.5-VL-based medical chatbot that unifies classification, multi-label classification, textualized detection, counting, regression, and free-form report generation under a single next-token objective.
Le Thien Phuc Nguyen, T. Nguyen, Thanh-Huy Nguyen et al.· 0 citations
MM-ContextFold is proposed, a training-free framework that loads raw images only when needed and maintains a persistent, text-only main context for high-level planning and spawns ephemeral branch contexts for image-dependent subtasks.
Yang Tian, Fan Liu, Jing-Yuan Zhang et al.· 0 citations
This review offers a unified synthesis of collaboration architectures and topologies, neural-communication co-design that treats the channel as a differentiable pipeline component, embodied action-perception loops via multi-agent reinforcement learning, and resilience mechanisms for synchronization, uncertainty quantif...
Lei Zhang, Chun-Lu Ye, Le Yang et al.· Research· 0 citations
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.
