Tempo-R0: A Video-MLLM for Temporal Video Grounding through Efficient Temporal Sensing Reinforcement Learning

• URL: http://arxiv.org/abs/2507.04702v1
• Date: Mon, 07 Jul 2025 06:51:40 GMT
• Title: Tempo-R0: A Video-MLLM for Temporal Video Grounding through Efficient Temporal Sensing Reinforcement Learning
• Authors: Feng Yue, Zhaoxing Zhang, Junming Jiao, Zhengyu Liang, Shiwen Cao, Feifei Zhang, Rong Shen,
• Abstract summary: Temporal Video Grounding (TVG) requires pinpointing relevant temporal segments from video based on language query.<n>We propose Tempo-R0: a Video Multimodal Large Language Model (Video-MLLM) for the temporal video grounding task.<n>Our method accomplishes a notable advantage over SOTA solutions by around 3.5% on the original QVHighlights testbench.
• Score: 6.9627404612894335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal Video Grounding (TVG), which requires pinpointing relevant temporal segments from video based on language query, has always been a highly challenging task in the field of video understanding. Videos often have a larger volume of information and redundancy than texts or images. Models should present comprehensive understanding of the whole video to accurately retrieve query-relevant clips. We thus propose Tempo-R0: a Video Multimodal Large Language Model (Video-MLLM) for the temporal video grounding task via multimodal temporal sensing reinforcement. Specifically, during the preprocessing stage of our pipeline, we employ Self-adaptive Attention Allocation (SAA) method based on frame content variation to efficiently use the MLLM's limited attention. The Explicit Timestamp-modal Aligned (ETA) method is also utilized to strengthen our model's capability to perceive the boundaries of events in the video. In the fine-tuning part of our pipeline, we creatively apply Partial Irrelevance Refusing-based Group Relative Policy Optimization (PIR-GRPO) in TVG area to foster model's temporal reasoning from not only accepting relevant video-query pairs but also refusing irrelevant ones. Experiments demonstrate that our method accomplishes a notable advantage over SOTA solutions by around 3.5% on both the original QVHighlights testbench and its corrected version with more reasonable ground truth annotations.

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