435 citations · 503 across the 62 of their papers we have counts for
17 papers · 1 filter
Training Multimodal Large Reasoning Models Needs Better Thoughts: A Three-Stage Framework for Long Chain-of-Thought Synthesis and Selection
Yizhi Wang, Linan Yue, Min-Ling Zhang
Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex reasoning tasks through long Chain-of-Thought (CoT) reasoning. Extending these successes to multim…
Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning
Wei Tang, Yin-Fang Yang, Weijia Zhang +1
Multi-instance partial-label learning (MIPL) is a weakly supervised framework that extends the principles of multi-instance learning (MIL) and partial-label learning (PLL) to addre…
Unleashing the Power of Vision-Language Models for Long-Tailed Multi-Label Visual Recognition
Wei Tang, Zuo-Zheng Wang, Kun Zhang +2
Long-tailed multi-label visual recognition poses a significant challenge, as images typically contain multiple labels with highly imbalanced class distributions, leading to biased…
OmniGround: A Comprehensive Spatio-Temporal Grounding Benchmark for Real-World Complex Scenarios
Hong Gao, Jingyu Wu, Xiangkai Xu +5
Spatio-Temporal Video Grounding (STVG) aims to localize target objects in videos based on natural language descriptions. Despite recent advances in Multimodal Large Language Models…
Agentic Video Intelligence: A Flexible Framework for Advanced Video Exploration and Understanding
Hong Gao, Yiming Bao, Xuezhen Tu +6
Video understanding requires not only visual recognition but also complex reasoning. While Vision-Language Models (VLMs) demonstrate impressive capabilities, they typically process…
MotionDuet: Dual-Conditioned 3D Human Motion Generation with Video-Regularized Text Learning
Yi-Yang Zhang, Tengjiao Sun, Pengcheng Fang +4
3D Human motion generation is pivotal across film, animation, gaming, and embodied intelligence. Traditional 3D motion synthesis relies on costly motion capture, while recent work…
Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation
Xiaofeng Cao, Mingwei Xu, Xin Yu +8
Learning with high-resource data has demonstrated substantial success in artificial intelligence (AI); however, the costs associated with data annotation and model training remain…
RobustFlow: Towards Robust Agentic Workflow Generation
Shengxiang Xu, Jiayi Zhang, Shimin Di +5
The automated generation of agentic workflows is a promising frontier for enabling large language models (LLMs) to solve complex tasks. However, the empirical study reveals that ex…
Code2MCP: Transforming Code Repositories into MCP Services
Chaoqian Ouyang, Ling Yue, Shimin Di +5
The Model Context Protocol (MCP) aims to create a standard for how Large Language Models use tools. However, most current research focuses on selecting tools from an existing pool.…
Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models
Linan Yue, Yichao Du, Yizhi Wang +8
Recently, Large Reasoning Models (LRMs) have gradually become a research hotspot due to their outstanding performance in handling complex tasks. Among them, DeepSeek R1 has garnere…
What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models?
Ming-Kun Xie, Jia-Hao Xiao, Gang Niu +4
Large Vision-Language Models (LVLMs), empowered by the success of Large Language Models (LLMs), have achieved impressive performance across domains. Despite the great advances in L…
Tuning the Right Foundation Models is What you Need for Partial Label Learning
Kuang He, Wei Tang, Tong Wei +1
Partial label learning (PLL) seeks to train generalizable classifiers from datasets with inexact supervision, a common challenge in real-world applications. Existing studies have d…
APVR: Hour-Level Long Video Understanding with Adaptive Pivot Visual Information Retrieval
Hong Gao, Yiming Bao, Xuezhen Tu +3
Current multimodal large language models (MLLMs) struggle with hour-level video understanding, facing significant challenges not only in modeling the substantial information volume…
LADA: Scalable Label-Specific CLIP Adapter for Continual Learning
Mao-Lin Luo, Zi-Hao Zhou, Tong Wei +1
Continual learning with vision-language models like CLIP offers a pathway toward scalable machine learning systems by leveraging its transferable representations. Existing CLIP-bas…
Weakly-Supervised Contrastive Learning for Imprecise Class Labels
Zi-Hao Zhou, Jun-Jie Wang, Tong Wei +1
Contrastive learning has achieved remarkable success in learning effective representations, with supervised contrastive learning often outperforming self-supervised approaches. How…
Realistic Evaluation of Deep Partial-Label Learning Algorithms
Wei Wang, Dong-Dong Wu, Jindong Wang +3
Partial-label learning (PLL) is a weakly supervised learning problem in which each example is associated with multiple candidate labels and only one is the true label. In recent ye…
Multi-Instance Partial-Label Learning with Margin Adjustment
Wei Tang, Yin-Fang Yang, Zhaofei Wang +2
Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label s…