Papers by Zongyang Ma

3 papers
iMOVE : Instance-Motion-Aware Video Understanding (2025.findings-acl)

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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.
Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation (2026.findings-acl)

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Challenge: Multimodal Large Language Models (MLLMs) are increasingly being deployed as content moderators . however, they exploit the Human-AI capability gap and create adversarial environments . smuggling attacks exploit the human-AI gap and exploit the vulnerability .
Approach: They construct a benchmark to evaluate the vulnerability of MLLMs as content moderators . they identify three root causes: limited capabilities of vision encoders, robustness gap in OCR .
Outcome: The proposed model exploits the Human-AI capability gap and is vulnerable to smuggling attacks.
Are Large Language Models Chronically Online Surfers? A Dataset for Chinese Internet Meme Explanation (2025.emnlp-main)

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Challenge: Large language models (LLMs) are trained on vast amounts of text from the Internet, but do they understand the viral content that rapidly spreads online?
Approach: They introduce a dataset for CHinese Internet Meme Explanation that includes popular phrase-based memes from the Chinese Internet.
Outcome: The proposed dataset includes popular phrase-based memes from the Chinese Internet, annotated with detailed information on their meaning, origin, example sentences, types, etc.

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