Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification (1711.09550v1)

Abstract: Recently, substantial research effort has focused on how to apply CNNs or RNNs to better extract temporal patterns from videos, so as to improve the accuracy of video classification. In this paper, however, we show that temporal information, especially longer-term patterns, may not be necessary to achieve competitive results on common video classification datasets. We investigate the potential of a purely attention based local feature integration. Accounting for the characteristics of such features in video classification, we propose a local feature integration framework based on attention clusters, and introduce a shifting operation to capture more diverse signals. We carefully analyze and compare the effect of different attention mechanisms, cluster sizes, and the use of the shifting operation, and also investigate the combination of attention clusters for multimodal integration. We demonstrate the effectiveness of our framework on three real-world video classification datasets. Our model achieves competitive results across all of these. In particular, on the large-scale Kinetics dataset, our framework obtains an excellent single model accuracy of 79.4% in terms of the top-1 and 94.0% in terms of the top-5 accuracy on the validation set. The attention clusters are the backbone of our winner solution at ActivityNet Kinetics Challenge 2017. Code and models will be released soon.

• The paper introduces an innovative attention-based framework that aggregates local features without heavy reliance on temporal patterns.
• The paper employs a shifting operation to diversify attention clusters, thereby enhancing training efficiency and overall accuracy.
• The paper demonstrates multimodal integration with a top-1 accuracy of 79.4% on Kinetics, underscoring its scalability and robustness.

Overview of "Attention Clusters: Purely Attention-Based Local Feature Integration for Video Classification"

This paper by Xiang Long et al. presents a novel approach in the domain of video classification by focusing on attention mechanisms as the primary method for local feature integration. The research challenges the prevailing assumption that temporal patterns are indispensable for effective video classification, suggesting instead that local features often suffice. The authors introduce a framework based on attention clusters, emphasizing the integration of local features to produce competitive results without extensive reliance on temporal dynamics.

Key Contributions

1. Novel Attention-Based Framework: The central contribution is an attention-based framework that aggregates local features through attention clusters. The authors argue that while CNNs and RNNs have historically been used to capture temporal interactions, these may not be crucial for standard video classification tasks.
2. Shifting Operation: To enhance the diversity and effectiveness of the attention units within the cluster, a shifting operation is introduced. This operation facilitates different attention units to focus on diverse aspects of the video, which, in turn, improves training efficiency and classification accuracy.
3. Multimodal Integration: The research extends the model to handle multiple modalities (like RGB, flow, and audio) independently but simultaneously, integrating the outputs into a comprehensive video representation. This multimodal extension is crucial, considering the varied nature of video data.

Numerical Results

The experimental results are compelling, particularly on the large-scale Kinetics dataset, achieving a top-1 accuracy of 79.4% and a top-5 accuracy of 94.0% on the validation set. These results underscore the model's competitiveness, reinforcing the efficacy of attention-based local feature integration over traditional methods emphasizing temporal dynamics.

Implications and Future Directions

The findings have significant implications for video classification:

• Model Simplification: By demonstrating that temporal cue reliance can be minimized, this approach simplifies the video classification pipeline, potentially reducing computational load and training time.
• Scalability: The model is well-suited to scale across different datasets and retains the robustness needed for large-scale applications like Kinetics.
• Generalization to Other Domains: While the paper focuses on video data, the underlying principles of attention-based local feature integration could generalize to other domains where local patterns are pivotal, such as image or audio classification.

Looking forward, the paper hints at the possibility of integrating such attention mechanisms directly into end-to-end trainable networks. This could result in even more efficient architectures capable of learning diverse and context-rich features from raw data inputs.

Conclusion

The paper "Attention Clusters: Purely Attention-Based Local Feature Integration for Video Classification" offers a fresh perspective on video classification by emphasizing attention-driven methodologies. While the results are promising, future research could investigate deeper into integrating this approach within more complex video understanding tasks, as well as exploring its application to other domains requiring intricate feature learning.