Distort Time to Improve Video Temporal Reasoning
Daiqing Qi, Dongliang Guo, Hanzhang Yuan, Handong Zhao, Mengxuan Hu, Lehan Yang, Sheng Li
A major distinction between video and image understanding is that the former requires reasoning over time.Existing Video Large Language Models (VLLMs) demonstrate promising performance in general video understanding, such as brief captioning or object recognition within individual frames. However, they often struggle with temporal reasoning such as understanding continuous actions or tracking object transformations over time—which typically demands the integration of multiple frames in a temporally coherent manner.We first explore and explain such failures in Video LLMs from the perspective of \textit{language and ``image'' priors.}While existing research has attempted to enhance the temporal understanding of VLLMs through various training strategies, the demand for expensive computational resources and training data often presents significant barriers.To this end, we further propose a simple yet novel idea for improving temporal reasoning in videos at no additional training cost.Specifically, to better capture the temporal structure across multiple frames—the key to effective temporal reasoning—we distort the temporal consistency in key frames \textit{during the decoding phase}. Such corruption induces time-insensitive wrong responses from the model, which are then contrastively avoided when generating the final correct output. In this way, the model is encouraged to perform more temporally coherent reasoning.Our method yields consistent improvements across both temporal-specific and general video understanding benchmarks, demonstrating its effectiveness and generalizability.