Palm: Predicting Actions through Language Models @ Ego4D Long-Term Action Anticipation Challenge 2023
This work addresses action prediction in videos, which is important for applications like robotics and surveillance, but it appears incremental as it combines existing models without a major breakthrough.
The paper tackles the Long-Term Action Anticipation task by using vision-language and large language models to predict future actions from video inputs, achieving the best performance in the EGO4D LTA challenge.
We present Palm, a solution to the Long-Term Action Anticipation (LTA) task utilizing vision-language and large language models. Given an input video with annotated action periods, the LTA task aims to predict possible future actions. We hypothesize that an optimal solution should capture the interdependency between past and future actions, and be able to infer future actions based on the structure and dependency encoded in the past actions. Large language models have demonstrated remarkable commonsense-based reasoning ability. Inspired by that, Palm chains an image captioning model and a large language model. It predicts future actions based on frame descriptions and action labels extracted from the input videos. Our method outperforms other participants in the EGO4D LTA challenge and achieves the best performance in terms of action prediction. Our code is available at https://github.com/DanDoge/Palm