Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training (2104.09411v1)

Abstract: The pre-trained neural models have recently achieved impressive performances in understanding multimodal content. However, it is still very challenging to pre-train neural models for video and language understanding, especially for Chinese video-language data, due to the following reasons. Firstly, existing video-language pre-training algorithms mainly focus on the co-occurrence of words and video frames, but ignore other valuable semantic and structure information of video-language content, e.g., sequential order and spatiotemporal relationships. Secondly, there exist conflicts between video sentence alignment and other proxy tasks. Thirdly, there is a lack of large-scale and high-quality Chinese video-language datasets (e.g., including 10 million unique videos), which are the fundamental success conditions for pre-training techniques. In this work, we propose a novel video-language understanding framework named VICTOR, which stands for VIdeo-language understanding via Contrastive mulTimOdal pRe-training. Besides general proxy tasks such as masked language modeling, VICTOR constructs several novel proxy tasks under the contrastive learning paradigm, making the model be more robust and able to capture more complex multimodal semantic and structural relationships from different perspectives. VICTOR is trained on a large-scale Chinese video-language dataset, including over 10 million complete videos with corresponding high-quality textual descriptions. We apply the pre-trained VICTOR model to a series of downstream applications and demonstrate its superior performances, comparing against the state-of-the-art pre-training methods such as VideoBERT and UniVL. The codes and trained checkpoints will be publicly available to nourish further developments of the research community.

• The paper presents a novel framework, Victor, which leverages contrastive multimodal pre-training to improve Chinese video-language integration.
• It employs an encoder-decoder architecture with tailored proxy tasks, including masked language and frame modeling, to capture both spatial and temporal semantics.
• Experimental results demonstrate enhanced video retrieval, classification, and captioning, underscoring Victor's practical impact on multimodal applications.

Understanding The Approach to Chinese Video and Language Integration

The paper "Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training" (2104.09411) presents a comprehensive framework named Victor designed to enhance video-language understanding, particularly focusing on Chinese datasets. Victor is developed to address challenges inherent in integrating video and language data, leveraging contrastive multimodal pre-training to refine comprehension in various applications. Below we explore the architecture, methodologies, and key findings as presented in the paper.

Victor: Framework, Architecture, and Datasets

Victor introduces an encoder-decoder framework specifically designed for pre-training with Chinese video-language datasets. The framework employs various novel proxy tasks to improve multimodal semantic representation:

• Encoder-Decoder Architecture: This core component employs transformers to derive shared understanding between video frames and text sequences, accommodating both generating and discriminant fine-tuning tasks.
• Large-Scale Dataset: Collected from an e-commerce platform, Alivol-10M provides more than 10 million curated Chinese video-text pairs, enhancing the model's exposure to varied semantic content.

  Figure 1: General proxy tasks that are widely used in existing video-language pre-training methods.

Proxy Tasks and Their Contributions

The paper emphasizes two categories of proxy tasks: reconstructive and contrastive, each serving distinct roles in improving model capabilities.

Reconstructive Proxy Tasks

• Masked Language and Sentence Modeling (MLM and MSG): Aimed at understanding and predicting missing textual information, facilitating comprehensive language handling.
• Masked Frame and Sentence Order Modeling (MFOM and MSOM): These tasks encourage the model to understand sequential order and structure within videos, refining comprehension of temporal relationships.

Contrastive Proxy Tasks

• Intra and Inter-Masked Frame Modeling (intra/inter-MFM): Designed to harness spatial and temporal consistencies within and across videos, enabling better object and event representation.
• Dual Video and Sentence Alignment (dual-VSA): Enhances the alignment of video sequences with corresponding textual descriptions, essential for accurate cross-modal retrieval.

  Figure 2: An example of the videos in Alivol-10M dataset. Besides the high-resolution video frames and human-created title, the video also contains a long-text abstract and a related e-commerce product with images. Each video has three types of categories: plot category, coarse-grained product category, and fine-grained product category (respectively denoted by Plot, TopCate, and LeafCate).

Experimentation and Results

Four downstream tasks illustrate Victor’s applicability and improvements over existing models like VideoBERT and UniVL.

• Video Retrieval: On text-based and image-based retrieval tasks, Victor demonstrated superior recall metrics due to effective dual-VSA execution.
• Classification and Recommendation: The model's architectural adaptations, particularly in leveraging sequential relationships, improved categorization tasks across varying classification granularity.
• Video Captioning: Here, Victor excelled at generating naturalistic language descriptions from video content, affirming its utility in applications requiring narrative generation.

  Figure 3: An example result of the text-based video retrieval task, which is fine-tuned based on the pre-trained Victor model.

Implications and Future Directions

Victor’s implementation underscores the potential of using contrastive learning techniques in multimodal scenarios, especially for resource-rich languages like Chinese. The model’s adaptability across distinct tasks proposes a significant advance in video-language integration frameworks. Future work could expand Victor's exploration into other languages or domain-specific applications, such as real-time video analysis in security.

Figure 4: An example result of the image-based video retrieval task, which is fine-tuned based on the pre-trained Victor model.

Conclusion

This paper delivers a substantial contribution to video-language comprehension, defining a path for future advancements leveraging large-scale, domain-specific datasets. The combination of sophisticated proxy tasks within the Victor framework opens new possibilities for enhancing AI systems' understandings of complex multimodal interactions.