3 citations · 8 across the 19 of their papers we have counts for
25 papers
SkillCAT: Contrastive, Assessment-Augmented and Topology-AwareSkill Self-Evolution for LLM Agents
Kunfeng Chen, Qihuang Zhong, Juhua Liu +1
Skill self-evolution methods for LLM agents aim to turn execution trajectories into reusable skill documents. However, current pipelines typically derive skill patches from a singl…
ConRAG: Consensus-Driven Evidence Flow for Multi-Hop Question Answering
Yikai Zhu, Kunfeng Chen, Qihuang Zhong +2
Retrieval-augmented generation (RAG) has emerged as a promising paradigm for enhancing large language models (LLMs) on multi-hop question answering (QA), which requires reasoning o…
Better, Faster: Harnessing Self-Improvement in Large Reasoning Models
Qihuang Zhong, Liang Ding, Juhua Liu +3
Self-improvement training enables the large reasoning models (LRMs) to improve themselves by self-generating reasoning trajectories as training data without external supervision. H…
UHR-Micro: Diagnosing and Mitigating the Resolution Illusion in Earth Observation VLMs
Shuo Ni, Tong Wang, Jing Zhang +5
Vision-Language Models (VLMs) are increasingly used to analyze ultra-high-resolution (UHR) Earth observation imagery, yet they face a severe scale mismatch between broad scene cont…
Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training
Qihuang Zhong, Liang Ding, Wenjie Xuan +3
Post-training with explicit reasoning traces is common to improve the reasoning capabilities of Multimodal Large Language Models (MLLMs). However, acquiring high-quality reasoning…
VTAgent: Agentic Keyframe Anchoring for Evidence-Aware Video TextVQA
Haibin He, Maoyuan Ye, Jing Zhang +2
Video text-based visual question answering (Video TextVQA) aims to answer questions by reasoning over visual textual content appearing in videos. Despite the strong multimodal vide…
Tool Retrieval Bridge: Aligning Vague Instructions with Retriever Preferences via Bridge Model
Kunfeng Chen, Luyao Zhuang, Fei Liao +3
Tool learning has emerged as a promising paradigm for large language models (LLMs) to address real-world challenges. Due to the extensive and irregularly updated number of tools, t…
Try, Check and Retry: A Divide-and-Conquer Framework for Boosting Long-context Tool-Calling Performance of LLMs
Kunfeng Chen, Qihuang Zhong, Juhua Liu +2
Tool-calling empowers Large Language Models (LLMs) to interact with external environments. However, current methods often struggle to handle massive and noisy candidate tools in lo…
ET-SAM: Efficient Point Prompt Prediction in SAM for Unified Scene Text Detection and Layout Analysis
Xike Zhang, Maoyuan Ye, Juhua Liu +1
Previous works based on Segment Anything Model (SAM) have achieved promising performance in unified scene text detection and layout analysis. However, the typical reliance on pixel…
SFA: Scan, Focus, and Amplify toward Guidance-aware Answering for Video TextVQA
Haibin He, Qihuang Zhong, Juhua Liu +3
Video text-based visual question answering (Video TextVQA) task aims to answer questions about videos by leveraging the visual text appearing within the videos. This task poses sig…
Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation
Xiao He, Huangxuan Zhao, Guojia Wan +8
Recent medical vision-language models have shown promise on tasks such as VQA, report generation, and anomaly detection. However, most are adapted to structured adult imaging and u…
Rethink Sparse Signals for Pose-guided Text-to-image Generation
Wenjie Xuan, Jing Zhang, Juhua Liu +2
Recent works favored dense signals (e.g., depth, DensePose), as an alternative to sparse signals (e.g., OpenPose), to provide detailed spatial guidance for pose-guided text-to-imag…
Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning
Qihuang Zhong, Liang Ding, Fei Liao +3
Domain-specific instruction-tuning has become the defacto standard for improving the performance of large language models (LLMs) in specialized applications, e.g., medical question…
KaFT: Knowledge-aware Fine-tuning for Boosting LLMs' Domain-specific Question-Answering Performance
Qihuang Zhong, Liang Ding, Xiantao Cai +3
Supervised fine-tuning (SFT) is a common approach to improve the domain-specific question-answering (QA) performance of large language models (LLMs). However, recent literature rev…
Reasoning-OCR: Can Large Multimodal Models Solve Complex Logical Reasoning Problems from OCR Cues?
Haibin He, Maoyuan Ye, Jing Zhang +4
Large Multimodal Models (LMMs) have become increasingly versatile, accompanied by impressive Optical Character Recognition (OCR) related capabilities. Existing OCR-related benchmar…
GoMatching++: Parameter- and Data-Efficient Arbitrary-Shaped Video Text Spotting and Benchmarking
Haibin He, Jing Zhang, Maoyuan Ye +3
Video text spotting (VTS) extends image text spotting (ITS) by adding text tracking, significantly increasing task complexity. Despite progress in VTS, existing methods still fall…
Adapting Segment Anything Model for Power Transmission Corridor Hazard Segmentation
Hang Chen, Maoyuan Ye, Peng Yang +3
Power transmission corridor hazard segmentation (PTCHS) aims to separate transmission equipment and surrounding hazards from complex background, conveying great significance to mai…
LogicOCR: Do Your Large Multimodal Models Excel at Logical Reasoning on Text-Rich Images?
Maoyuan Ye, Haibin He, Qihuang Zhong +3
Recent advances in Large Multimodal Models (LMMs) have revolutionized their reasoning and Optical Character Recognition (OCR) capabilities. However, their complex logical reasoning…
Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL
Qihuang Zhong, Kunfeng Chen, Liang Ding +3
Large Language Models (LLMs) have shown promising performance in text-to-SQL, which involves translating natural language questions into SQL queries. However, current text-to-SQL L…
Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis
Qihuang Zhong, Haiyun Li, Luyao Zhuang +2
Aspect-based Sentiment Analysis (ABSA) is an important sentiment analysis task, which aims to determine the sentiment polarity towards an aspect in a sentence. Due to the expensive…
RFL-CDNet: Towards Accurate Change Detection via Richer Feature Learning
Yuhang Gan, Wenjie Xuan, Hang Chen +2
Change Detection is a crucial but extremely challenging task of remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However,…
Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Qihuang Zhong, Kang Wang, Ziyang Xu +3
Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks. However, CoT still falls short in dealing with complex…
When ControlNet Meets Inexplicit Masks: A Case Study of ControlNet on its Contour-following Ability
Wenjie Xuan, Yufei Xu, Shanshan Zhao +4
ControlNet excels at creating content that closely matches precise contours in user-provided masks. However, when these masks contain noise, as a frequent occurrence with non-exper…
PICO: Accelerating All k-Core Paradigms on GPU
Chen Zhao, Ting Yu, Zhigao Zheng +4
Core decomposition is a well-established graph mining problem with various applications that involves partitioning the graph into hierarchical subgraphs. Solutions to this problem…