Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning (2003.00392v1)

Abstract: Cross-modal retrieval between videos and texts has attracted growing attentions due to the rapid emergence of videos on the web. The current dominant approach for this problem is to learn a joint embedding space to measure cross-modal similarities. However, simple joint embeddings are insufficient to represent complicated visual and textual details, such as scenes, objects, actions and their compositions. To improve fine-grained video-text retrieval, we propose a Hierarchical Graph Reasoning (HGR) model, which decomposes video-text matching into global-to-local levels. To be specific, the model disentangles texts into hierarchical semantic graph including three levels of events, actions, entities and relationships across levels. Attention-based graph reasoning is utilized to generate hierarchical textual embeddings, which can guide the learning of diverse and hierarchical video representations. The HGR model aggregates matchings from different video-text levels to capture both global and local details. Experimental results on three video-text datasets demonstrate the advantages of our model. Such hierarchical decomposition also enables better generalization across datasets and improves the ability to distinguish fine-grained semantic differences.

Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning

The paper "Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning" presents an innovative approach to address the intricacies involved in cross-modal retrieval tasks between videos and textual descriptions. The proliferation of video content on platforms such as YouTube and TikTok necessitates more sophisticated retrieval systems that can handle the complexity and diversity of visual and textual content by facilitating efficient and precise retrieval.

Key Contributions

This research introduces the Hierarchical Graph Reasoning (HGR) model, which distinguishes itself from conventional joint embedding methods by employing a hierarchical structure for video-text alignment. The primary contributions of the HGR model include:

1. Hierarchical Semantic Graph Decomposition: Textual content is decomposed into semantic graphs through hierarchical levels, comprising events, actions, and entities. This decomposition allows for a structured representation that captures the inherent semantic hierarchy in natural language descriptions.
2. Attention-based Graph Reasoning: The use of attention-based graph reasoning within the hierarchical semantic graph facilitates the extraction of meaningful textual embeddings. This process enhances the ability to discern and match fine-grained semantic details in videos and texts.
3. Hierarchical Video Representation: Videos are mapped into corresponding hierarchical embeddings representing global events, local actions, and entities. This mapping ensures that both local and global semantic aspects of videos are retained.
4. Aggregated Matching Across Hierarchical Levels: By aggregating matching scores from various hierarchical levels, the model achieves comprehensive semantic coverage, improving performance in fine-grained video-text retrieval tasks.

Experimental Verification

The researchers conducted extensive experiments on video-text datasets: MSR-VTT, TGIF, and VATEX, obtaining significant performance improvements over existing state-of-the-art methods. The HGR model demonstrated enhanced retrieval accuracy, reflected in higher R@K scores and lower Median and Mean Rank scores, exemplifying its effectiveness in semantic coverage at both the global and local levels.

Generalization and Fine-grained Discrimination

One of the notable findings of this work is the model's robust generalization on the unseen Youtube2Text dataset. The results indicate that the HGR model effective adapts to datasets it was not explicitly trained on, a critical property for practical applications that handle diverse video contents. The proposed binary selection task further validated the model's capacity for fine-grained discrimination, showing its ability to distinguish subtle semantic nuances, such as role switching and entity replacements in video descriptions.

Implications and Future Directions

The implications of this research are manifold, impacting theoretical and practical aspects of AI and cross-modal retrieval systems:

• Enhanced Retrieval Systems: The ability to capture fine-grained semantics enables more accurate retrieval systems, leading to better user experiences and potentially personalized video content recommendation engines.
• Cross-modal Semantic Understanding: The hierarchical structure can be leveraged for other cross-modal understanding tasks, such as video captioning or question answering, where understanding granular semantic differences is crucial.
• Further Research on Graph-based Reasoning: The HGR model's use of attention-based graph reasoning opens avenues for further exploration into graph neural networks to enhance video understanding and retrieval tasks.

In conclusion, the paper advances the domain of video-text retrieval by proposing a more structured and semantically aware approach through hierarchical graph reasoning. This work not only addresses existing limitations but also lays the groundwork for future improvements and applications in AI-driven media content analysis and retrieval.