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Vamos: Versatile Action Models for Video Understanding (2311.13627v3)

Published 22 Nov 2023 in cs.CV and cs.AI

Abstract: What makes good representations for video understanding, such as anticipating future activities, or answering video-conditioned questions? While earlier approaches focus on end-to-end learning directly from video pixels, we propose to revisit text-based representations, such as general-purpose video captions, which are interpretable and can be directly consumed by LLMs. Intuitively, different video understanding tasks may require representations that are complementary and at different granularity. To this end, we propose versatile action models (Vamos), a learning framework powered by a LLM as the ``reasoner'', and can flexibly leverage visual embedding and free-form text descriptions as its input. To interpret the important text evidence for question answering, we generalize the concept bottleneck model to work with tokens and nonlinear models, which uses hard attention to select a small subset of tokens from the free-form text as inputs to the LLM reasoner. We evaluate Vamos on five complementary benchmarks, Ego4D, NeXT-QA, IntentQA, Spacewalk-18, and EgoSchema, on its capability to model temporal dynamics, encode visual history, and perform reasoning. Surprisingly, we observe that text-based representations consistently achieve competitive performance on all benchmarks, and that visual embeddings provide marginal or no performance improvement, demonstrating the effectiveness of text-based video representation in the LLM era. We also demonstrate that our token bottleneck model is able to select relevant evidence from free-form text, support test-time intervention, and achieves nearly 5 times inference speedup while keeping a competitive question answering performance. Code and models are publicly released at https://brown-palm.github.io/Vamos/

PDF HTML Abstract

Versatile Action Models for Video Understanding: A Text-Based Representation Approach

Introduction to Versatile Action Models (Vamos)

The quest for enhanced video understanding capabilities has led to the conceptualization of Versatile Action Models (Vamos). This framework diverges from traditional methodologies that rely heavily on visual embeddings, by reintroducing the concept of text-based representations. Through integrating discrete action labels and free-form text descriptions with LLMs, Vamos presents a novel pathway to action modeling. Essentially, it leverages the interpretability and flexibility of textual information, assessing its effectiveness across various tasks like activity anticipation and video question answering.

Theoretical and Practical Implications

Vamos operates on the hypothesis that different video understanding tasks could benefit from representations of varying granularity and form. The model caters to this need by accommodating visual embeddings, action labels, and textual descriptions within a unified framework. This multifaceted approach posits several implications:

  1. Textual Representation's Competitiveness: Across benchmarks, text-based representations not only held their ground but demonstrated superior or competitive performance to visual embeddings. This finding raises questions about the relative efficiency and utility of directly interpretable representations in harnessing LLMs for video understanding.
  2. Marginal Utility of Visual Embeddings: The incremental benefit provided by visual embeddings was documented as marginal. This observation could potentially shift the focus of future research towards optimizing text-based video representations, exploring their limits and capabilities.
  3. Interpretability and Intervention Capabilities: The readability of text-based representations provides an added advantage of interpretability. Vamos showcases the capability to intervene and correct representations, emphasizing the model's flexibility and adaptability.

Future Directions in AI Research

The insights drawn from Vamos open multiple avenues for further exploration:

  • Optimizing Text-Based Representations: The effectiveness of text descriptions prompts an investigation into refining these representations. Future work could explore the granularity of descriptions, the optimal combination of action labels and free-text, and methods to enhance their descriptive accuracy.
  • LLMs as Reasoners for Complex Tasks: Vamos demonstrates the prowess of LLMs in understanding and processing complex video data through text. This capability could be extended to more nuanced tasks, examining the outer limits of text-based reasoning in video understanding.
  • Visual and Textual Fusion Models: Despite the highlighted efficiency of text-based representations, integrating visual information could enrich model understanding. Exploring innovative methods to blend these modalities without compromising the benefits of interpretability and flexibility warrants investigation.
  • Efficiency in Representation: The paper also touches upon the compression of text descriptions and the selective emphasis on crucial tokens, indicating potential for efficiency improvements. Future research could delve into methods for optimizing input representation without loss of essential information, directly contributing to computational efficiency.

Concluding Thoughts

Vamos presents a compelling case for re-evaluating text-based representations in video understanding tasks. By demonstrating the competitiveness of free-form text descriptions and leveraging the reasoning capabilities of LLMs, it sets a foundational step towards a new direction in video understanding research. The blend of interpretability, flexibility, and performance underscores the potential of text-based approaches, inviting a renaissance in how we model and interpret complex visual data. As we stand on this threshold, the future of video understanding seems poised to embrace the versatility and depth offered by textual representations, heralding a new era in generative AI research.

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Authors (6)
  1. Shijie Wang (62 papers)
  2. Qi Zhao (181 papers)
  3. Minh Quan Do (2 papers)
  4. Nakul Agarwal (16 papers)
  5. Kwonjoon Lee (23 papers)
  6. Chen Sun (187 papers)
Citations (12)
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  1. Vamos: Versatile Action Models for Video Understanding