# Exploiting Visual Semantic Reasoning for Video-Text Retrieval

arXiv: https://arxiv.org/abs/2006.08889 · HTML: https://mlsanity.com/paper/2006.08889/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval](https://mlsanity.com/paper/2103.15049/) (2021)
- [Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval](https://mlsanity.com/paper/2201.09168/) (2022)
- [HANet: Hierarchical Alignment Networks for Video-Text Retrieval](https://mlsanity.com/paper/2107.12059/) (2021)
- [BiC-Net: Learning Efficient Spatio-Temporal Relation for Text-Video Retrieval](https://mlsanity.com/paper/2110.15609/) (2021)
- [Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval](https://mlsanity.com/paper/2202.03384/) (2022)
- [PRVR: Partially Relevant Video Retrieval](https://mlsanity.com/paper/2208.12510/) (2022)
- [Mask to reconstruct: Cooperative Semantics Completion for Video-text Retrieval](https://mlsanity.com/paper/2305.07910/) (2023)
- [Dual Learning with Dynamic Knowledge Distillation and Soft Alignment for Partially Relevant Video Retrieval](https://mlsanity.com/paper/2510.12283/) (2025)
- [Leveraging Auxiliary Information in Text-to-Video Retrieval: A Review](https://mlsanity.com/paper/2505.23952/) (2025)
- [An Empirical Study of Excitation and Aggregation Design Adaptions in CLIP4Clip for Video-Text Retrieval](https://mlsanity.com/paper/2406.01604/) (2024)

## Abstract (arXiv)

Video retrieval is a challenging research topic bridging the vision and language areas and has attracted broad attention in recent years. Previous works have been devoted to representing videos by directly encoding from frame-level features. In fact, videos consist of various and abundant semantic relations to which existing methods pay less attention. To address this issue, we propose a Visual Semantic Enhanced Reasoning Network (ViSERN) to exploit reasoning between frame regions. Specifically, we consider frame regions as vertices and construct a fully-connected semantic correlation graph. Then, we perform reasoning by novel random walk rule-based graph convolutional networks to generate region features involved with semantic relations. With the benefit of reasoning, semantic interactions between regions are considered, while the impact of redundancy is suppressed. Finally, the region features are aggregated to form frame-level features for further encoding to measure video-text similarity. Extensive experiments on two public benchmark datasets validate the effectiveness of our method by achieving state-of-the-art performance due to the powerful semantic reasoning.