AntGPT: Leveraging Large Language Models for Long-Term Action Anticipation from Videos

Core Concepts

Large language models can effectively infer high-level goals and model the temporal dynamics of human actions, enabling state-of-the-art performance on long-term action anticipation tasks.

Abstract

This paper proposes AntGPT, a framework that leverages large language models (LLMs) to address the long-term action anticipation (LTA) task from video observations. The key insights are: Top-down LTA (goal-conditioned) can outperform bottom-up approaches by utilizing LLMs to infer the high-level goals from observed actions. The goal inference is achieved via in-context learning, which requires few human-provided examples. The same action-based video representation allows LLMs to effectively model the temporal dynamics of human behaviors, achieving competitive performance without relying on explicitly inferred goals. This suggests LLMs can implicitly capture goal information when predicting future actions. The useful prior knowledge encoded by LLMs can be distilled into a very compact neural network (1.3% of the original LLM model size), enabling efficient inference while maintaining similar or even better LTA performance. The paper conducts extensive experiments on Ego4D, EPIC-Kitchens-55, and EGTEA GAZE+ benchmarks, demonstrating the effectiveness of leveraging LLMs for both goal inference and temporal dynamics modeling in the LTA task.

Stats

"can better anticipate an actor's future actions (e.g. mix eggs) by knowing what commonly happens after the current action (e.g. crack eggs)" "the long-term action anticipation (LTA) task aims to predict an actor's future behavior from video observations in the form of verb and noun sequences" "the LTA task is challenging due to noisy perception (e.g. action recognition), and the inherent ambiguity and uncertainty that reside in human behaviors"

Quotes

"Can we better anticipate an actor's future actions (e.g. mix eggs) by knowing what commonly happens after the current action (e.g. crack eggs)? What if the actor also shares the goal (e.g. make fried rice) with us?" "We hypothesize that large language models (LLMs), which have been pretrained on procedure text data (e.g. recipes, how-tos), have the potential to help LTA from both perspectives." "Ideally, the prior knowledge can help both bottom-up and top-down LTA approaches, as they can not only answer questions such as 'what are the most likely actions following this current action?', but also 'what is the actor trying to achieve, and what are the remaining steps to achieve the goal?'"

Key Insights Distilled From

AntGPT

by Qi Zhao,Shij... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2307.16368.pdf

Deeper Inquiries

How can the proposed AntGPT framework be extended to handle more complex video scenarios with multiple actors and interactions

To extend the AntGPT framework to handle more complex video scenarios with multiple actors and interactions, several modifications and enhancements can be implemented. One approach could involve incorporating multi-modal inputs, such as visual embeddings of different actors, their interactions, and the overall scene context. By including these additional visual features, the model can capture the dynamics and relationships between various elements in the video. This can be achieved by leveraging pre-trained models for object detection, pose estimation, or scene segmentation to extract detailed visual information. Furthermore, the framework can be adapted to include attention mechanisms that focus on different regions of the video based on the actors' actions and interactions. This can help the model understand the spatial and temporal dependencies between different elements in the video. Additionally, incorporating graph-based representations to model the relationships between actors and objects can enhance the model's ability to anticipate actions in complex scenarios. Moreover, introducing hierarchical structures in the model architecture can help capture the hierarchy of actions and interactions in the video. By incorporating multiple levels of abstraction, the model can better understand the sequence of events and anticipate future actions more accurately. Overall, by integrating these enhancements, the AntGPT framework can be extended to handle more complex video scenarios with multiple actors and interactions effectively.

What are the limitations of using action labels as the sole interface between videos and language models, and how can richer visual representations be incorporated to further improve performance

While using action labels as the interface between videos and language models is efficient, it has limitations in capturing the rich visual information present in videos. One limitation is the lack of spatial and contextual details that are crucial for understanding complex video scenarios. To address this limitation, richer visual representations, such as object embeddings, scene graphs, or spatial-temporal features, can be incorporated into the framework. By integrating these richer visual representations, the model can have a more comprehensive understanding of the video content, leading to improved performance in action anticipation tasks. Additionally, leveraging pre-trained models for video understanding, such as video transformers or graph neural networks, can enhance the model's ability to extract meaningful visual features from the videos. Furthermore, incorporating attention mechanisms that focus on different aspects of the video based on the visual representations can help the model capture relevant information for action anticipation. By combining these advanced visual representations with the language model's capabilities, the framework can achieve a more holistic understanding of the video content and improve its performance significantly.

Can the insights from this work on leveraging language models for video understanding be applied to other tasks beyond action anticipation, such as video summarization or video question answering

The insights gained from leveraging language models for video understanding in the context of action anticipation can be applied to other tasks beyond action anticipation, such as video summarization or video question answering. For video summarization, language models can be used to generate concise and informative summaries of videos by processing the visual content and extracting key information. By incorporating the ability to understand the context and content of videos, language models can generate coherent and relevant summaries that capture the essence of the video. In the case of video question answering, language models can be utilized to comprehend the questions posed about the video content and generate accurate responses based on the visual information. By integrating visual and textual modalities, the model can effectively answer questions about the video content, demonstrating a comprehensive understanding of the visual data. Overall, the insights from leveraging language models for video understanding can be adapted and applied to various video-related tasks, enhancing the capabilities of models in processing and interpreting visual content in a wide range of applications.