CLIP2Video: Mastering Video-Text Retrieval via Image CLIP (2106.11097v1)

Abstract: We present CLIP2Video network to transfer the image-language pre-training model to video-text retrieval in an end-to-end manner. Leading approaches in the domain of video-and-language learning try to distill the spatio-temporal video features and multi-modal interaction between videos and languages from a large-scale video-text dataset. Different from them, we leverage pretrained image-LLM, simplify it as a two-stage framework with co-learning of image-text and enhancing temporal relations between video frames and video-text respectively, make it able to train on comparatively small datasets. Specifically, based on the spatial semantics captured by Contrastive Language-Image Pretraining (CLIP) model, our model involves a Temporal Difference Block to capture motions at fine temporal video frames, and a Temporal Alignment Block to re-align the tokens of video clips and phrases and enhance the multi-modal correlation. We conduct thorough ablation studies, and achieve state-of-the-art performance on major text-to-video and video-to-text retrieval benchmarks, including new records of retrieval accuracy on MSR-VTT, MSVD and VATEX.

Overview of CLIP2Video: Mastering Video-Text Retrieval via Image CLIP

The paper "CLIP2Video: Mastering Video-Text Retrieval via Image CLIP" presents an innovative approach to video-text retrieval by leveraging the powerful pretrained image-LLM CLIP. The authors aim to address the limitations in multi-modal video-and-language learning, which traditionally requires large-scale video-text datasets to extract spatio-temporal features for effective retrieval. The proposed CLIP2Video model facilitates efficient training on smaller datasets while achieving state-of-the-art results on major benchmarks such as MSR-VTT, MSVD, and VATEX.

Methodology

The CLIP2Video framework is designed to efficiently transfer image-language pretraining knowledge to video-text tasks using a two-stage approach that primarily involves two novel components: the Temporal Difference Block (TDB) and the Temporal Alignment Block (TAB).

1. Temporal Difference Block (TDB):
   • The TDB is crucial for capturing motion within video sequences. It utilizes frame differences to enhance the temporal representation of video content, thereby identifying motion-related features that are often pivotal for video semantics. This block integrates a layer of transformation that enables fine-grained motion capture, contributing to the enriched modeling of video frames over time.
2. Temporal Alignment Block (TAB):
   • To address multi-modal correlations within the joint embedding space, the TAB is introduced. This block emphasizes aligning video frames with textual content by using shared semantic centers. By aligning tokens from both video and text modalities, the TAB effectively bridges the gap between visual and linguistic representations, improving the retrieval performance significantly.

Experimental Results

The paper presents extensive experiments and ablation studies to validate the effectiveness of the CLIP2Video network. The model exhibits superior retrieval accuracy and outperforms existing state-of-the-art methods in both video-to-text and text-to-video retrieval scenarios. Tabulated results illustrate CLIP2Video's advantage, with notable improvements in recall metrics across several datasets, underscoring the robustness and adaptability of the approach.

Implications and Future Work

The introduction of CLIP2Video demonstrates that leveraging robust image-LLMs like CLIP with specialized components for video contexts can significantly enhance retrieval tasks. While this work primarily focuses on video-text retrieval, the methodologies presented here open avenues for future research in other video-based understanding tasks.

Practical applications could extend beyond retrieval to video summarization, clustering, and captioning tasks that can benefit from the interpretability and efficiency of the proposed model. Future research may investigate the integration of additional contextual information and scaling the model to handle even more complex video datasets, thus broadening the scope and applicability of video-and-language tasks in AI.

In summary, the paper contributes a technically significant advancement in leveraging pretrained image-LLMs in the video domain, and its implications could shape future methodologies in multi-modal AI research.