ActionLMM: captioning long-video actions with memory-augmented VLMs
TL;DR
This work introduces ActionLMM, a memory-augmented vision-language model for long-video action summarization that aligns visual and motion modalities through joint representation learning and leverages a novel dual-memory mechanism to retain both local motion details and global temporal structure.
Abstract
The success of large language models (LLMs) has inspired the development of foundation-level multimodal systems that integrate vision and language. However, current video-language models—such as Video-LLaMA and VideoChat—struggle with fine-grained human motion understanding and fail to summarize long videos effectively. Meanwhile, motion-focused models are limited to short clips and lack mechanisms to capture long-range spatiotemporal context. We introduce ActionLMM, a memory-augmented vision-language model for long-video action summarization. It aligns visual and motion modalities through joint representation learning and leverages a novel dual-memory mechanism to retain both local motion details and global temporal structure. To support evaluation, we propose a large-scale benchmark dataset with 33,887 longform action videos and 169,435 caption annotations across 1920 action categories. Experiments show that ActionLMM significantly outperforms prior methods, offering a robust and scalable solution for fine-grained human action understanding.