Papers by Md Mohaiminul Islam

2 papers
Video-RTS: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning (2025.emnlp-main)

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Challenge: Despite advances in reinforcement learning, data collection and fine-tuning remain costly and hard to scale.
Approach: They propose a video-adaptive test-time scaling strategy that combines RL with a supervised fine-tuning strategy to improve video reasoning capability.
Outcome: The proposed method surpasses existing models by 2.4% in accuracy using only 3.6% training samples.
A Simple LLM Framework for Long-Range Video Question-Answering (2024.emnlp-main)

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Challenge: a recent study has shown that short video understanding is not trivial due to the need for long-range temporal reasoning capabilities.
Approach: They propose a language-based short- and long-range question-answering framework LLoVi . they propose 'multi-round summarization prompt' that asks the LLM to summarize the captions .
Outcome: The proposed framework outperforms the state-of-the-art on the EgoSchema dataset and to grounded VideoQA.

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