Chrono: A Simple Blueprint for Representing Time in MLLMs

Understand

The recent success of Large Language Models (LLMs) has prompted the extension to the multimodal domain, developing image-text Multimodal LLMs (MLLMs) and then video-text models.

• In this work, we investigate the challenge of contextual and temporal comprehension in video-language models by exploring the task of temporal localization in videos.
• To address this problem, prior works have developed complex task-specific architectures, novel modules to embed time into MLLMs, or leveraged additional input signals such as video transcripts to best encode contextual and temporal information.
• We find that most of these efforts are surpassed by a much simpler design.