Papers by Huihui Zhang
iMOVE : Instance-Motion-Aware Video Understanding (2025.findings-acl)
Copied to clipboard
Jiaze Li, Yaya Shi, Zongyang Ma, Haoran Xu, Yandong.bai Yandong.bai, Huihui Xiao, Ruiwen Kang, Fan Yang, Tingting Gao, Di Zhang
| Challenge: | Recent advances in Video Large Language Models have led to rapid development, significantly enhancing the capture of overall video semantics and achieving remarkable performance in general video understanding tasks. |
| Approach: | They propose a large-scale instance-motion-aware video instruction-tuning dataset iMOVE that utilizes Event-awful Spatiotemporal Efficient Modeling to retain informative instance spatiotemporal motion details while maintaining computational efficiency. |
| Outcome: | The proposed model excels in video temporal understanding and general video understanding. |
Exploiting Reasoning Chains for Multi-hop Science Question Answering (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing frameworks for multi-hop Science question answering do not require corpus-specific annotations. |
| Approach: | They propose a chain-guided retriever-reader framework that performs explainable reasoning without corpus annotations. |
| Outcome: | The proposed framework performs explainable reasoning without corpus-specific annotations . it is shown to be effective on OpenBookQA and ARC-Challenge . |
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches suffer from low confidence when retrieving evidence facts to fill the knowledge gap and lack transparent reasoning process. |
| Approach: | They propose a framework to exploit more valid facts while obtaining explainability for multi-hop question answering at web scale by dynamically constructing a semantic graph and reasoning over it. |
| Outcome: | The proposed framework surpasses existing approaches while maintaining high explainability on OpenBookQA and ARC-Challenge. |
Knowledge Graph Embedding with Atrous Convolution and Residual Learning (2020.coling-main)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding methods are complex and require time for training and inference. |
| Approach: | They propose an atrous convolution based knowledge graph embedding method that increases feature interactions by using atrous . they evaluate method on six benchmark datasets with different evaluation metrics . |
| Outcome: | The proposed method achieves better results on six benchmark datasets than state-of-the-art methods on most evaluation metrics. |
Inter-sentence Context Modeling and Structure-aware Representation Enhancement for Conversational Sentiment Quadruple Extraction (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies struggle to capture complete dialogue semantics due to inadequate inter-utterance modeling and the underutilization of dialogue structure. |
| Approach: | They propose a model to extract dialogue aspect sentiment quadruples from dialogues using a sentence-by-sentence encoding module. |
| Outcome: | The proposed model extracts quadruples of target-aspect-opinion-sentiment from dialogues. |
SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios (2026.acl-long)
Copied to clipboard
Junkai Chen, Huihui Huang, Yunbo Lyu, Junwen An, Jieke Shi, Chengran Yang, Ting Zhang, Haoye Tian, Yikun Li, Zhenhao Li, Xin Zhou, Xing Hu, David Lo
| Challenge: | Existing benchmarks fail to capture scenarios in which vulnerabilities are introduced by humans . we evaluate 5 popular code agents supported by 5 LLMs on SecureVibeBench . |
| Approach: | They propose a benchmarking tool that compares 105 C/C++ secure coding tasks . they use real-world open-source vulnerabilities and a comprehensive evaluation tool . |
| Outcome: | The proposed benchmarks show that code agents struggle to produce correct and secure code . the best performing agent produces merely 23.8% correct and secured solutions . |