CenterCLIP: Token Clustering for Efficient Text-Video Retrieval (2205.00823v1)

Abstract: Recently, large-scale pre-training methods like CLIP have made great progress in multi-modal research such as text-video retrieval. In CLIP, transformers are vital for modeling complex multi-modal relations. However, in the vision transformer of CLIP, the essential visual tokenization process, which produces discrete visual token sequences, generates many homogeneous tokens due to the redundancy nature of consecutive and similar frames in videos. This significantly increases computation costs and hinders the deployment of video retrieval models in web applications. In this paper, to reduce the number of redundant video tokens, we design a multi-segment token clustering algorithm to find the most representative tokens and drop the non-essential ones. As the frame redundancy occurs mostly in consecutive frames, we divide videos into multiple segments and conduct segment-level clustering. Center tokens from each segment are later concatenated into a new sequence, while their original spatial-temporal relations are well maintained. We instantiate two clustering algorithms to efficiently find deterministic medoids and iteratively partition groups in high dimensional space. Through this token clustering and center selection procedure, we successfully reduce computation costs by removing redundant visual tokens. This method further enhances segment-level semantic alignment between video and text representations, enforcing the spatio-temporal interactions of tokens from within-segment frames. Our method, coined as CenterCLIP, surpasses existing state-of-the-art by a large margin on typical text-video benchmarks, while reducing the training memory cost by 35\% and accelerating the inference speed by 14\% at the best case. The code is available at \href{{https://github.com/mzhaoshuai/CenterCLIP}}{{https://github.com/mzhaoshuai/CenterCLIP}}.

Overview of "CenterCLIP: Token Clustering for Efficient Text-Video Retrieval"

The paper "CenterCLIP: Token Clustering for Efficient Text-Video Retrieval" presents a novel methodology centered on optimizing token usage to reduce computational overhead in text-video retrieval systems. The authors address a specific limitation in the protocol embodied by CLIP (Contrastive Language-Image Pre-training), a renowned multi-modal framework, focusing on the verbosity and redundancy of visual tokens generated when processing video inputs.

Technical Contributions

The authors present an innovative framework called CenterCLIP, which employs a token clustering technique. This approach specifically targets the redundancy inherent in visual token sequences extracted from consecutive video frames. These sequences, due to their high similarity across frames, dramatically contribute to increased computational costs without proportionate gains in retrieval performance.

1. Token Clustering and Reduction: CenterCLIP utilizes a multi-segment token clustering algorithm to identify and retain the most representative tokens from each video segment while discarding those deemed non-essential. The innovation here lies in segment-level clustering, which divides videos into multiple segments, conducts segment-wise clustering, and selects center tokens from each cluster.
2. Efficiency Improvements: Through this token reduction approach, CenterCLIP significantly decreases the computational load, as evidenced by a notable reduction in memory costs by 35% and improvement in inference speed by 14% in optimal cases.
3. Algorithmic Instantiation: The paper implements two clustering algorithms to identify the most deterministic medoids: k-medoids equipped with KKZ initialization for consistent clustering outcomes and spectral clustering apt for high-dimensional data spaces.

Experimental Results

CenterCLIP demonstrates its efficacy across several benchmarks, including MSR-VTT, MSVD, LSMDC, and ActivityNet. It achieves superior performance in recall metrics compared to prior methods, reinforcing its positional prominence in text-video retrieval tasks. Specifically, the efficiency of the proposed model is underscored by enhancements in R@1, R@5, and R@10 across these datasets.

Implications and Future Directions

The results and methodologies put forward in CenterCLIP suggest a number of significant implications for both practical applications and theoretical advancements in AI:

• Practical Deployment: The reduced computational costs make CenterCLIP a viable candidate for integration into real-time applications, particularly those reliant on continuous video processing, such as video surveillance and live stream analysis.
• Theoretical Insights: These findings open avenues for further investigations into alternative clustering mechanisms that can be integrated into multi-modal retrieval frameworks to balance trade-offs between computational efficiency and retrieval accuracy.
• Algorithmic Adjustments: There is room to explore the interplay between clustering positions within the transformer models and the impact of different initialization methods, as well as to examine how hybrid or dynamic clustering approaches might further enhance model performance.

Conclusion

CenterCLIP stands as a seminal contribution to the area of efficient text-video retrieval, leveraging thoughtful token reduction techniques to enhance computational efficiency while maintaining high retrieval performance. Through this work, the authors not only advance the state-of-the-art but also provide a clear pathway for further research in optimizing multi-modal frameworks in the AI domain. As the field moves towards processing increasingly large datasets, methodologies like CenterCLIP that emphasize efficiency will become increasingly critical.