Learning video embedding space with Natural Language Supervision

Abstract: The recent success of the CLIP model has shown its potential to be applied to a wide range of vision and language tasks. However this only establishes embedding space relationship of language to images, not to the video domain. In this paper, we propose a novel approach to map video embedding space to natural langugage. We propose a two-stage approach that first extracts visual features from each frame of a video using a pre-trained CNN, and then uses the CLIP model to encode the visual features for the video domain, along with the corresponding text descriptions. We evaluate our method on two benchmark datasets, UCF101 and HMDB51, and achieve state-of-the-art performance on both tasks.

• The paper proposes a two-stage approach that first extracts video frame features using a pre-trained CNN and then aligns these with corresponding text using the CLIP model.
• It achieves state-of-the-art performance on benchmark datasets UCF101 and HMDB51, highlighting effective video-to-text mapping.
• The method extends CLIP's capabilities to video analysis, enabling improved applications in video search, classification, and summarization.

The paper "Learning video embedding space with Natural Language Supervision" explores an innovative method for aligning video data with natural language descriptions. This research builds on the success of the CLIP model, which effectively pairs images with text, and extends this capability to the video domain.

Key Contributions:

1. Novel Approach: The authors propose a two-stage approach:
   • Visual Feature Extraction: Utilizes a pre-trained Convolutional Neural Network (CNN) to extract visual features from each video frame.
   • Video-Text Alignment: Implements the CLIP model to encode these visual features and map them to corresponding text descriptions, effectively translating techniques from the image domain to video.
2. Benchmark Evaluation: The proposed method was evaluated on two well-known benchmark datasets, UCF101 and HMDB51. The results demonstrate that the approach achieves state-of-the-art performance, suggesting that the video embedding space can be effectively mapped to natural language using this method.

Implications:

• Expansion of CLIP’s Capabilities: By extending the CLIP model to handle video data, this research opens new possibilities for video analysis tasks, such as video classification and retrieval, using natural language queries.
• Real-world Applications: The method can impact various domains, including video content moderation, automated video summarization, and enhanced video search engines.

Conclusion:

This paper successfully demonstrates the potential of natural language supervision in video embedding, proving that CLIP's powerful language-image paradigm can be extended to videos. This advancement could significantly influence future research on integrating vision and language for more complex media types.