Multimodal Large Models Are Effective Action Anticipators

Abstract: The task of long-term action anticipation demands solutions that can effectively model temporal dynamics over extended periods while deeply understanding the inherent semantics of actions. Traditional approaches, which primarily rely on recurrent units or Transformer layers to capture long-term dependencies, often fall short in addressing these challenges. LLMs, with their robust sequential modeling capabilities and extensive commonsense knowledge, present new opportunities for long-term action anticipation. In this work, we introduce the ActionLLM framework, a novel approach that treats video sequences as successive tokens, leveraging LLMs to anticipate future actions. Our baseline model simplifies the LLM architecture by setting future tokens, incorporating an action tuning module, and reducing the textual decoder layer to a linear layer, enabling straightforward action prediction without the need for complex instructions or redundant descriptions. To further harness the commonsense reasoning of LLMs, we predict action categories for observed frames and use sequential textual clues to guide semantic understanding. In addition, we introduce a Cross-Modality Interaction Block, designed to explore the specificity within each modality and capture interactions between vision and textual modalities, thereby enhancing multimodal tuning. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed ActionLLM framework, encouraging a promising direction to explore LLMs in the context of action anticipation. Code is available at https://github.com/2tianyao1/ActionLLM.git.

• The paper introduces ActionLLM, a framework that converts video sequences into tokens for efficient long-term action prediction.
• It employs a Cross-Modality Interaction Block (CMIB) to fuse visual and textual data, streamlining the prediction process.
• Empirical tests show ActionLLM outperforms traditional RNN, LSTM, and Transformer models on benchmark datasets like 50 Salads and Breakfast.

Multimodal Large Models Are Effective Action Anticipators

This paper presents an innovative framework called ActionLLM, which explores the application of LLMs to the task of long-term action anticipation using multimodal data. The focus is on leveraging LLMs, traditionally used in language processing, to enhance the capability of anticipating actions over extended durations by integrating both visual and textual modalities.

Overview and Methodology

ActionLLM treats video sequences as successive tokens, a novel approach that aligns with the intrinsic design of LLMs suited for handling sequential data. The framework incorporates a baseline model where future tokens are simplified, using an action tuning module to streamline the LLM by converting the text decoder layer to a linear layer. This architectural choice facilitates straightforward action prediction, eliminating the need for complex instructions.

A critical component of the ActionLLM is the Cross-Modality Interaction Block (CMIB), which plays a pivotal role in fusing visual and textual information. The CMIB is designed to explore interdependencies between modalities, enhancing the model's multimodal tuning capabilities. The use of CMIB allows ActionLLM to address two main challenges in long-term action anticipation: capturing long-term dependencies and understanding the underlying semantics of actions.

Empirical Evaluation

The paper provides substantial empirical evidence of ActionLLM's effectiveness through extensive experimentation on benchmark datasets, namely the 50 Salads and Breakfast datasets. The framework consistently outperforms traditional approaches, such as those based on RNN and LSTM architectures, and more recent Transformer-based methods focused on long-term dependencies.

In particular, ActionLLM demonstrates significant performance improvements in Mean over Classes (MoC) metric, achieving superior results compared to existing state-of-the-art methods like FUTR, and methods employing cycle consistency and object-centric representations. The paper highlights specific scenarios where ActionLLM achieves markedly better accuracy, underscoring the benefits of harnessing LLMs for sequential and multimodal processing.

Implications and Future Directions

The successful application of LLMs in action anticipation opens new avenues for enhancing AI systems in augmented reality, intelligent surveillance, and human-computer interaction. The ability to effectively predict long-term actions has practical implications, particularly in environments requiring real-time decision-making and interaction.

Theoretically, the integration of visual and textual modalities through sophisticated models like ActionLLM could lead to advancements in understanding complex multimodal relationships. ActionLLM's design choices—such as the use of CMIB and parameter-efficient adaptation strategies—provide a foundation for further exploration of multimodal learning in other AI domains.

Moving forward, future research could explore the scalability of ActionLLM with larger and more diverse datasets, as well as its application to other complex tasks requiring multimodal integration. Additionally, investigating lightweight variations of the model could enhance its speed and adaptability, making it even more viable for practical, real-time applications.