182 citations · 574 across the 34 of their papers we have counts for
36 papers
Doing More with Less Tokens: Hierarchical Reinforcement Learning for Efficient Coding Agents
Haobin Li, Liang Jiang, Zhenyu Huang +2
Recently, coding agents have emerged as a dominant paradigm for real-world software engineering (SWE) scenarios, which solve complex tasks through multi-turn interactions with deve…
NeuCME: Toward Dynamic Multimodal Continual Learning via Neural Combinatorics of Multiple Experts
Kai Guo, Chuanbin Liu, Peng Hu +2
Multimodal continual learning has recently shown great potential for developing agents with human-like intelligence by continuously learning new tasks across multiple modalities. H…
Imagine Before Acting: Prospective Skill Identification for LLM Agents
Shuo Liu, Yutong Yang, Haohao Xiao +2
Effective skill identification is crucial for LLM agents to reuse task-solving experience for complex task execution, which however remains challenging as agents must accurately id…
RA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generation
Shuhao Yan, Changhao He, Peng Hu +1
Text-to-CAD generation translates natural-language design intent into editable and executable parametric computer-aided design (CAD) codes, reducing the expertise and effort requir…
Active-SWE: Benchmarking Coding Agents for Proactive Bug Fixing without Issue Reports
Haobin Li, Ping Deng, Weizhong Qian +4
Coding agents powered by large language models (LLMs) are increasingly adopted in software engineering (SWE) scenarios, capable of fixing a specific bug in large-scale codebase. Ho…
Robust Multi-view Clustering against Imperfect Information
Zhichao Huang, Haochen Zhou, Hao Wang +2
Real-world multi-view data always suffer from imperfect information problem, where the view-specific observations are absent (i.e., Incomplete Views, IV) and cross-view corresponde…
Robust Fuzzy Multi-view Learning under View Conflict
Siyuan Duan, Yuan Sun, Dezhong Peng +3
Trusted multi-view classification aims to deliver reliable fusion for accurate predictions and has recently attracted substantial attention in both academia and industry. However,…
Learning Subspace-Preserving Sparse Attention Graphs from Heterogeneous Multiview Data
Jie Chen, Yuanbiao Gou, Chuanbin Liu +2
The high-dimensional features extracted from large-scale unlabeled data via various pretrained models with diverse architectures are referred to as heterogeneous multiview data. Mo…
Attention Transfer Is Not Universally Effective for Vision Transformers
Huaiyuan Qin, Muli Yang, Gabriel James Goenawan +4
A recent work shows that Attention Transfer, which transfers only the attention patterns from a pre-trained teacher Vision Transformer (ViT) to a randomly initialized standard stud…
Robust Duality Learning for Unsupervised Visible-Infrared Person Re-Identification
Yongxiang Li, Yuan Sun, Yang Qin +3
Unsupervised visible-infrared person re-identification (UVI-ReID) aims to retrieve pedestrian images across different modalities without costly annotations, but faces challenges du…
DiFiC: Your Diffusion Model Holds the Secret to Fine-Grained Clustering
Ruohong Yang, Peng Hu, Xi Peng +2
Fine-grained clustering is a practical yet challenging task, whose essence lies in capturing the subtle differences between instances of different classes. Such subtle differences…
A Survey on Deep Clustering: From the Prior Perspective
Yiding Lu, Haobin Li, Yunfan Li +2
Facilitated by the powerful feature extraction ability of neural networks, deep clustering has achieved great success in analyzing high-dimensional and complex real-world data. The…
Multimodal Fusion on Low-quality Data: A Comprehensive Survey
Qingyang Zhang, Yake Wei, Zongbo Han +8
Multimodal fusion focuses on integrating information from multiple modalities with the goal of more accurate prediction, which has achieved remarkable progress in a wide range of s…
PointCloud-Text Matching: Benchmark Datasets and a Baseline
Yanglin Feng, Yang Qin, Dezhong Peng +3
In this paper, we present and study a new instance-level retrieval task: PointCloud-Text Matching (PTM), which aims to identify the exact cross-modal instance that matches a given…
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model
Yuxin Tian, Mouxing Yang, Yunfan Li +4
Recent studies applied Parameter Efficient Fine-Tuning techniques (PEFTs) to efficiently narrow the performance gap between pre-training and downstream. There are two important fac…
Multi-granularity Correspondence Learning from Long-term Noisy Videos
Yijie Lin, Jie Zhang, Zhenyu Huang +3
Existing video-language studies mainly focus on learning short video clips, leaving long-term temporal dependencies rarely explored due to over-high computational cost of modeling…
Consistency Enhancement-Based Deep Multiview Clustering via Contrastive Learning
Hao Yang, Hua Mao, Wai Lok Woo +2
Multiview clustering (MVC) segregates data samples into meaningful clusters by synthesizing information across multiple views. Moreover, deep learning-based methods have demonstrat…
Test-Time Degradation Adaptation for Open-Set Image Restoration
Yuanbiao Gou, Haiyu Zhao, Boyun Li +2
In contrast to close-set scenarios that restore images from a predefined set of degradations, open-set image restoration aims to handle the unknown degradations that were unforesee…
Cross-View Graph Consistency Learning for Invariant Graph Representations
Jie Chen, Hua Mao, Wai Lok Woo +2
Graph representation learning is fundamental for analyzing graph-structured data. Exploring invariant graph representations remains a challenge for most existing graph representati…
Cross-modal Active Complementary Learning with Self-refining Correspondence
Yang Qin, Yuan Sun, Dezhong Peng +3
Recently, image-text matching has attracted more and more attention from academia and industry, which is fundamental to understanding the latent correspondence across visual and te…
Image Clustering with External Guidance
Yunfan Li, Peng Hu, Dezhong Peng +3
The core of clustering is incorporating prior knowledge to construct supervision signals. From classic k-means based on data compactness to recent contrastive clustering guided by…
Decoupled Contrastive Multi-View Clustering with High-Order Random Walks
Yiding Lu, Yijie Lin, Mouxing Yang +3
In recent, some robust contrastive multi-view clustering (MvC) methods have been proposed, which construct data pairs from neighborhoods to alleviate the false negative issue, i.e.…
Noisy-Correspondence Learning for Text-to-Image Person Re-identification
Yang Qin, Yingke Chen, Dezhong Peng +3
Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal community, which aims to retrieve the target person based on a textual query. Although nume…
Provable Dynamic Fusion for Low-Quality Multimodal Data
Qingyang Zhang, Haitao Wu, Changqing Zhang +4
The inherent challenge of multimodal fusion is to precisely capture the cross-modal correlation and flexibly conduct cross-modal interaction. To fully release the value of each mod…