Vamos: Versatile Action Models for Video Understanding
(2311.13627)Abstract
What makes good video 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 discrete action labels, or free-form 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 granularities. To this end, we propose versatile action models (Vamos), a learning framework powered by a large language model as the "reasoner", and can flexibly leverage visual embeddings, action labels, and free-form descriptions extracted from videos as its input. We evaluate Vamos on four complementary video understanding benchmarks, Ego4D, Next-QA, IntentQA, 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 perform extensive ablation study and qualitative analysis to support our observations, and achieve state-of-the-art performance on three benchmarks.
Overview
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Vamos introduces a novel approach to video understanding by prioritizing text-based representations over traditional visual embeddings, integrating action labels and free-form text with LLMs.
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Findings suggest that text-based representations may outperform or be as effective as visual embeddings across various tasks, indicating a potential shift towards textual data for future research.
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The model stands out for its interpretability and flexibility, allowing for corrections and adjustments in representations, which highlights the advantages of using text for video understanding.
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Future research directions include optimizing text-based representations, exploring the limits of LLMs in complex video data processing, and potentially integrating visual data without losing the benefits of text.
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:
- 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.
- 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.
- 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 study 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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