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Tian Gan

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3 papers

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IJCAI Conference 2025 Conference Paper

Generative Agents for Multimodal Controversy Detection

Tianjiao Xu
Jinfei Gao
Keyi Kong
Jianhua Yin
Tian Gan
Liqiang Nie

Multimodal controversy detection, which involves determining whether a given video and its associated comments are controversial, plays a pivotal role in risk management on social video platforms. Existing methods typically provide only classification results, failing to identify what aspects are controversial and why, thereby lacking detailed explanations. To address this limitation, we propose a novel Agent-based Multimodal Controversy Detection architecture, termed AgentMCD. This architecture leverages Large Language Models (LLMs) as generative agents to simulate human behavior and improve explainability. AgentMCD employs a multi-aspect reasoning process, where multiple judges conduct evaluations from diverse perspectives to derive a final decision. Furthermore, a multi-agent simulation process is incorporated, wherein agents act as audiences, offering opinions and engaging in free discussions after watching videos. This hybrid framework enables comprehensive controversy evaluation and significantly enhances explainability. Experiments conducted on the MMCD dataset demonstrate that our proposed architecture outperforms existing LLM-based baselines in both high-resource and low-resource comment scenarios, while maintaining superior explainability.

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IJCAI Conference 2024 Conference Paper

EVE: Efficient Zero-Shot Text-Based Video Editing With Depth Map Guidance and Temporal Consistency Constraints

Yutao Chen
Xingning Dong
Tian Gan
Chunluan Zhou
Ming Yang
Qingpei Guo

Motivated by the superior performance of image diffusion models, more and more researchers strive to extend these models to the text-based video editing task. Nevertheless, current video editing tasks mainly suffer from the dilemma between the high fine-tuning cost and the limited generation capacity. Compared with images, we conjecture that videos necessitate more constraints to preserve the temporal consistency during editing. Towards this end, we propose EVE, a robust and Efficient zero-shot Video Editing method. Under the guidance of depth maps and temporal consistency constraints, EVE derives satisfactory video editing results with an affordable computational and time cost. Moreover, recognizing the absence of a publicly available video editing dataset for fair comparisons, we construct a new benchmark named ZVE-50 dataset. Through comprehensive experimentation, we validate that EVE achieves a satisfactory trade-off between performance and efficiency. Codebase, datasets, and video editing demos are available at https: //github. com/alipay/Ant-Multi-Modal-Framework/blob/main/prj/EVE.

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AAAI Conference 2019 Conference Paper

Explicit Interaction Model towards Text Classification

Cunxiao Du
Zhaozheng Chen
Fuli Feng
Lei Zhu
Tian Gan
Liqiang Nie

Text classification is one of the fundamental tasks in natural language processing. Recently, deep neural networks have achieved promising performance in the text classification task compared to shallow models. Despite of the significance of deep models, they ignore the fine-grained (matching signals between words and classes) classification clues since their classifications mainly rely on the text-level representations. To address this problem, we introduce the interaction mechanism to incorporate word-level matching signals into the text classification task. In particular, we design a novel framework, EXplicit interAction Model (dubbed as EXAM), equipped with the interaction mechanism. We justified the proposed approach on several benchmark datasets including both multilabel and multi-class text classification tasks. Extensive experimental results demonstrate the superiority of the proposed method. As a byproduct, we have released the codes and parameter settings to facilitate other researches.

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