Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models (2301.00182v2)

Abstract: Vision-LLMs (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such powerful VLMs is a promising direction for building effective video recognition models. However, current exploration in this field is still limited. We believe that the greatest value of pre-trained VLMs lies in building a bridge between visual and textual domains. In this paper, we propose a novel framework called BIKE, which utilizes the cross-modal bridge to explore bidirectional knowledge: i) We introduce the Video Attribute Association mechanism, which leverages the Video-to-Text knowledge to generate textual auxiliary attributes for complementing video recognition. ii) We also present a Temporal Concept Spotting mechanism that uses the Text-to-Video expertise to capture temporal saliency in a parameter-free manner, leading to enhanced video representation. Extensive studies on six popular video datasets, including Kinetics-400 & 600, UCF-101, HMDB-51, ActivityNet and Charades, show that our method achieves state-of-the-art performance in various recognition scenarios, such as general, zero-shot, and few-shot video recognition. Our best model achieves a state-of-the-art accuracy of 88.6% on the challenging Kinetics-400 using the released CLIP model. The code is available at https://github.com/whwu95/BIKE .

Analysis of "Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-LLMs"

This academic article introduces a novel framework entitled BIKE (Bidirectional Cross-Modal Knowledge Exploration), aiming at enhancing video recognition capabilities of pre-trained Vision-LLMs (VLMs) by extracting and utilizing bidirectional cross-modal knowledge.

Overview and Methodology

The core innovation of the BIKE framework is its dual mechanism exploring both Video-to-Text (V2T) and Text-to-Video (T2V) directions for improved video recognition.

• Video-to-Text Direction: The paper presents the Video Attribute Association mechanism that capitalizes on the zero-shot capabilities of VLMs. It utilizes video data to generate textual auxiliary attributes that complement the video recognition process. The extracted attributes provide additional descriptors related to the video content, which are then fused into a textual format for classification. This approach capitalizes on CLIP's pre-existing alignment between visual frames and text features to enhance the semantic understanding of the video content.
• Text-to-Video Direction: The Text-to-Video mechanism integrates temporal saliency by applying the Video Concept Spotting technique. This mechanism assesses the importance of different video frames relative to specific textual inputs (such as category names), emphasizing frames of higher relevance and aggregating these into a more robust video representation. This technique enriches the frame importance modeling factor, leveraging the natural temporal dynamics observed in video data.

Numerical Results and Contributions

BIKE demonstrates significant improvements in performance across several video benchmark datasets, such as Kinetics-400, UCF-101, and HMDB-51. Noteworthy results include achieving a state-of-the-art top-1 accuracy of 88.6% on the Kinetics-400 dataset when leveraging the CLIP model, marking an improvement over existing approaches. These outcomes are compelling, particularly given the model's efficiency in parameter and computational overhead compared to other state-of-the-art methodologies.

The paper posits that through bidirectional exploration, VLMs pre-trained on large-scale image-text pairs can substantially enhance video recognition performance by addressing both video classification and temporal aspects of video input data.

Implications and Future Directions

The bidirectional knowledge exploration as proposed in BIKE could reshape the use of pre-trained VLMs in video analysis by augmenting their understanding of video data through enhanced semantic and temporal modeling. This paper highlights a critical insight into the potential of vision-language adaptations beyond static image datasets, paving the way for more sophisticated, contextually-aware video models.

Future research could expand upon this framework by integrating BIKE into larger, more varied datasets to assess its scalability and robustness. Furthermore, exploring different modalities and types of textual descriptions could enhance the attribute generation process and temporal analysis, potentially leading to even richer video understanding models.

In summary, this paper provides a compelling demonstration of the potential benefits achievable through cross-modal synergy in video recognition tasks and opens new avenues for research in leveraging vision-language pre-trained models for complex video data.