Leveraging Language Models to Predict Future Actions in the Long Run

The Future of Action Anticipation: Researchers Introduce AntGPT

A team of experts from Brown University and Honda Research Institute has developed a groundbreaking system known as AntGPT to explore the possibilities of using large language models (LLMs) for long-term action anticipation (LTA) in videos. LTA involves predicting sequences of actions that individuals may take over an extended period, and this research holds immense importance for applications such as self-driving cars and domestic chores, where machines need to anticipate human actions.

Examining Different Approaches

In their study, the researchers examine two types of strategies for action anticipation: bottom-up and top-down. Bottom-up strategies aim to simulate human behavior by utilizing neural networks trained on visual inputs. In contrast, top-down strategies focus on the actor’s ultimate goal and outline the necessary steps to achieve it.

Harnessing the Power of Language Models

The team proposes leveraging the capabilities of LLMs to encode prior information for action anticipation. LLMs have the advantage of being trained on procedural text material, such as recipes, which helps them understand temporal dynamics effectively. The study aims to answer four crucial questions:

1. What is the ideal interface between videos and LLMs for LTA?
2. Can LLMs infer goals for top-down LTA?
3. Can LLMs utilize prior knowledge for action anticipation?
4. Can LLMs perform few-shot LTA through in-context learning?

Introducing AntGPT: Empowering Action Anticipation

The proposed system, AntGPT, follows a two-stage process. First, it employs supervised action recognition algorithms to identify human activities from video footage. These recognized actions are then fed into OpenAI GPT models, which predict the intended outcome or anticipate future actions. For bottom-up LTA, the GPT model utilizes autoregressive methods or fine-tuning to predict future action sequences. On the other hand, for top-down LTA, the model first forecasts the actor’s goal and then predicts the actions required to accomplish it.

Impressive Results and Valuable Contributions

Extensive tests conducted on various LTA benchmarks revealed the exceptional performance of AntGPT in both quantitative and qualitative evaluations. The researchers demonstrated the ability of LLMs to infer high-level objectives based on action labels derived from video observations. Furthermore, LLMs demonstrated their capacity to anticipate counterfactual actions when presented with different input objectives.

This study introduces the groundbreaking concept of utilizing LLMs for action anticipation, presents the innovative AntGPT framework that seamlessly integrates LLMs with computer vision algorithms, and provides comprehensive assessments of the design decisions related to LLMs in the realm of LTA. To promote further advancements, the researchers are planning to release the code for AntGPT in the near future.

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