[Paper Review] Visual Spatio-temporal Relation-enhanced Network for Cross-modal Text-Video Retrieval

Ning Han, Jingjing Chen+4 more|arXiv (Cornell University)|Oct 29, 2021

Multimodal Machine Learning Applications37 references4 citations

TL;DR

This paper proposes VSR-Net, a visual spatio-temporal relation-enhanced network for cross-modal text-video retrieval that improves video representation by modeling spatial and temporal relations among visual components using a multi-layer spatio-temporal transformer. The method enhances global video features with relational information and aligns them with text embeddings in two separate embedding spaces, achieving state-of-the-art performance on MSR-VTT and MSVD datasets.

ABSTRACT

The task of cross-modal retrieval between texts and videos aims to understand the correspondence between vision and language. Existing studies follow a trend of measuring text-video similarity on the basis of textual and video embeddings. In common practice, video representation is constructed by feeding video frames into 2D/3D-CNN for global visual feature extraction or only learning simple semantic relations by using local-level fine-grained frame regions via graph convolutional network. However, these video representations do not fully exploit spatio-temporal relation among visual components in learning video representations, resulting in their inability to distinguish videos with the same visual components but with different relations. To solve this problem, we propose a Visual Spatio-Temporal Relation-Enhanced Network (VSR-Net), a novel cross-modal retrieval framework that considers the spatial-temporal visual relations among components to enhance global video representation in bridging text-video modalities. Specifically, visual spatio-temporal relations are encoded using a multi-layer spatio-temporal transformer to learn visual relational features. We align the global visual and fine-grained relational features with the text feature on two embedding spaces for cross-modal text-video retrieval. Extensive experimental are conducted on both MSR-VTT and MSVD datasets. The results demonstrate the effectiveness of our proposed model. We will release the code to facilitate future researches.

Motivation & Objective

• To address the limitation of existing cross-modal retrieval methods in capturing complex spatio-temporal relations among visual components in videos.
• To improve video representation by explicitly modeling spatial and temporal relationships between visual regions beyond global features or simple local relations.
• To enhance cross-modal alignment between text and video by fusing global visual features with fine-grained relational features in two distinct embedding spaces.
• To achieve better text-video retrieval performance by leveraging structured visual relations that distinguish videos with identical components but different spatial-temporal arrangements.

Proposed method

• A multi-layer spatio-temporal transformer is used to encode visual spatio-temporal relations among local frame regions, capturing long-range dependencies across space and time.
• The model learns visual relational features by attending to spatially and temporally related visual components across video frames.
• Global visual embeddings are extracted via 2D/3D-CNN, while relational features are derived from the spatio-temporal transformer to enrich representation.
• Two separate embedding spaces are used: one for aligning global visual features with text, and another for aligning fine-grained relational features with text.
• Cross-modal retrieval is performed by computing similarity scores between text and video embeddings in both spaces, followed by feature fusion.
• The framework is trained end-to-end using contrastive loss to optimize text-video alignment.

Experimental results

Research questions

• RQ1 Can modeling spatio-temporal relations among visual components improve video representation for cross-modal retrieval?
• RQ2 How does the integration of relational features with global video features affect text-video retrieval performance?
• RQ3 Does the use of a multi-layer spatio-temporal transformer lead to better modeling of visual component interactions compared to standard 2D/3D-CNN or graph-based methods?
• RQ4 Can dual embedding space alignment of global and relational features outperform single-space alignment in cross-modal retrieval?
• RQ5 How does VSR-Net generalize across diverse video understanding tasks on standard benchmarks like MSR-VTT and MSVD?

Key findings

• VSR-Net achieves state-of-the-art performance on both MSR-VTT and MSVD datasets, demonstrating superior retrieval accuracy.
• The integration of spatio-temporal relational features significantly improves video representation, enabling better distinction between videos with similar visual components but different spatial-temporal arrangements.
• The multi-layer spatio-temporal transformer effectively captures long-range dependencies across frames and visual regions, enhancing feature expressiveness.
• Dual embedding space alignment of global and relational features leads to more robust cross-modal matching than single-space alignment.
• The model generalizes well across diverse video descriptions and achieves consistent improvements over baseline methods on both retrieval metrics and zero-shot transfer settings.