Object-centric Video Representation for Long-term Action Anticipation (2311.00180v1)

Abstract: This paper focuses on building object-centric representations for long-term action anticipation in videos. Our key motivation is that objects provide important cues to recognize and predict human-object interactions, especially when the predictions are longer term, as an observed "background" object could be used by the human actor in the future. We observe that existing object-based video recognition frameworks either assume the existence of in-domain supervised object detectors or follow a fully weakly-supervised pipeline to infer object locations from action labels. We propose to build object-centric video representations by leveraging visual-language pretrained models. This is achieved by "object prompts", an approach to extract task-specific object-centric representations from general-purpose pretrained models without finetuning. To recognize and predict human-object interactions, we use a Transformer-based neural architecture which allows the "retrieval" of relevant objects for action anticipation at various time scales. We conduct extensive evaluations on the Ego4D, 50Salads, and EGTEA Gaze+ benchmarks. Both quantitative and qualitative results confirm the effectiveness of our proposed method.

Leveraging Visual-LLMs for Object-Centric Video Representation in Long-term Action Anticipation

Introduction to ObjectPrompt Framework

The landscape of video understanding and action anticipation within AI research is an ever-evolving field, marked by the continuous pursuit of models that can mimic human-level comprehension and predictive capabilities. Addressing the specific challenge of long-term action anticipation (LTA), a recent advancement termed ObjectPrompt offers a novel perspective on leveraging preexisting visual-LLMs to generate richer, object-centric representations of video content without the need for dataset-specific model finetuning.

Object-centric Representation in Video Understanding

Object-centric representations have been identified as a potent approach for enhancing the interpretability and performance of AI models tasked with video understanding. Specifically for action anticipation, where the objective is to predict future human-object interactions, focusing on objects provides a granular and informative view of the scene, allowing for more accurate future action predictions. Traditional methods have relied heavily on in-domain supervised object detectors or operated under a fully weakly supervised regime, both of which come with their constraints and limitations, such as high annotation costs or reduced efficacy due to a lack of object-specific focus.

The ObjectPrompt Approach

The ObjectPrompt methodology diverges from these traditional paths by extracting task-specific object representations from general-purpose, pre-trained visual-LLMs, specifically leveraging an innovative strategy known as "object prompts." This approach does not require the exhaustive task of model finetuning, thereby circumventing the resource-intensive processes associated with annotation and model training on large datasets. ObjectPrompt leverages pre-trained models like GLIP to dynamically extract and utilize object representations relevant to the anticipated actions, further integrating these representations with motion cues via a predictive transformer encoder (PTE) for action anticipation.

Predictive Transformer Encoder (PTE)

A key component in the ObjectPrompt framework, the PTE is designed to synergistically combine motion cues derived from video content with the extracted object-centric representations. The integration allows for dynamic association between motion and object evidence, crucial for predicting future actions. The PTE architecture facilitates the simultaneous encoding of video and object tokens, employing learnable tokens for future action prediction, enriched with both segment-level and frame-level positional encoding alongside modality-specific encoding to ensure comprehensive understanding and anticipation.

Empirical Validation

Extensive evaluations on benchmarks such as Ego4D, 50Salads, and EGTEA Gaze+ demonstrate the efficacy of ObjectPrompt. Results show a notable improvement in action anticipation performance, validating the hypothesis that object-centric video representations significantly bolster the model's predictive capabilities. Ablation studies further underline the importance of in-domain knowledge for object prompt design, object locations and categories, and the balanced selection of object quantity and quality in optimizing performance.

Future of AI in Video Understanding

ObjectPrompt's approach opens up new avenues for future developments in the field of AI-assisted video understanding and long-term action anticipation. By effectively utilizing pre-trained visual-LLMs, ObjectPrompt paves the way for more efficient and scalable solutions, reducing dependency on large annotated datasets and extensive model training. Future work could explore the potential of incorporating broader object vocabularies, refining object selection strategies, and enhancing modality fusion techniques to further boost model performance across diverse video understanding tasks.

Conclusion

The introduction of ObjectPrompt heralds a significant step forward in the domain of video understanding and action anticipation, illustrating the untapped potential of leveraging pre-trained visual-LLMs for object-centric representation. This approach not only streamlines the model training process but also significantly enhances the model’s ability to predict future actions, marking a pivotal advancement towards achieving human-like understanding and anticipation capabilities in AI models.