From the 1 of 27 papers with an AI index.
12 citations
- Peking UniversityCN7 papers
- Shanghai Artificial Intelligence LaboratoryCN6 papers
- Shanghai Jiao Tong UniversityCN4 papers
- Institute of AutomationCN3 papers
- University of Chinese Academy of SciencesCN3 papers
- Center for Excellence in Brain Science and Intelligence TechnologyCN2 papers
- Chinese Academy of SciencesCN2 papers
- Communication University of ChinaCN2 papers
- Fudan UniversityCN2 papers
- Harvard UniversityUS2 papers
- Shandong Institute of AutomationCN2 papers
- Tsinghua UniversityCN2 papers
31 papers
IntentQA: Intent Question Answering in Videos by Cognitive Context Reasoning
Jiapeng Li, Ping Wei, Wenjuan Han +2
Video understanding requires intelligent agents to transcend mere recognition of visual facts and comprehend the underlying intents behind human actions (often termed the "dark mat…
Understanding Cognition-Induced Risks in Agentic AI Systems
Guanchu Wang, Qinuo Li, Mengnan Du +2
Frontier agentic systems powered by large language models (LLMs) exhibit human-like patterns of cognition. As these systems become deeply integrated across different domains, their…
Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges
Yisong Chen, Yifan Gao, Sijing Yu +2
We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy support tools, prompt engine…
Miles: Metric Learning with Expandable Subspace for Pre-Trained Model-Based Class-Incremental Learning
Kai Jiang, Zisong Lin, Hongyuan Zhang +2
Class Incremental Learning (CIL) aims to learn new concepts consistently from a data stream without forgetting. Unlike typical CIL methods which need to learn a model from scratch,…
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Kaiyuan Yang, Fabio Musio, Yihui Ma +112
The paper introduces the TopCoW Challenge, a benchmark for automatically segmenting the Circle of Willis in CT and MR angiography using deep learning, and provides a new annotated…
Revisiting Shape and Texture Reliance with Category-Separability-Calibrated Suppression
Ning Jiang, Tianyi Luo, Zhengyong Huang +1
Feature-suppression evaluations infer model reliance on shape or texture from the accuracy loss caused by attenuating each type of information. Such losses, however, conflate featu…