# SSVMR: Saliency-based Self-training for Video-Music Retrieval

arXiv: https://arxiv.org/abs/2302.09328 · HTML: https://mlsanity.com/paper/2302.09328/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2023.

## Built on by (most cited first, 12 of 12)

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- [MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning](https://mlsanity.com/paper/2308.13218/) (2023)
- [Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning](https://mlsanity.com/paper/2305.18158/) (2023)
- [ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding](https://mlsanity.com/paper/2311.11375/) (2023)
- [CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction](https://mlsanity.com/paper/2305.10819/) (2023)
- [A Comprehensive Survey on Generative AI for Video-to-Music Generation](https://mlsanity.com/paper/2502.12489/) (2025)
- [FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation Learning](https://mlsanity.com/paper/2302.11814/) (2023)
- [I2KD-SLU: An Intra-Inter Knowledge Distillation Framework for Zero-Shot Cross-Lingual Spoken Language Understanding](https://mlsanity.com/paper/2310.02594/) (2023)

## Abstract (arXiv)

With the rise of short videos, the demand for selecting appropriate background music (BGM) for a video has increased significantly, video-music retrieval (VMR) task gradually draws much attention by research community. As other cross-modal learning tasks, existing VMR approaches usually attempt to measure the similarity between the video and music in the feature space. However, they (1) neglect the inevitable label noise; (2) neglect to enhance the ability to capture critical video clips. In this paper, we propose a novel saliency-based self-training framework, which is termed SSVMR. Specifically, we first explore to fully make use of the information containing in the training dataset by applying a semi-supervised method to suppress the adverse impact of label noise problem, where a self-training approach is adopted. In addition, we propose to capture the saliency of the video by mixing two videos at span level and preserving the locality of the two original videos. Inspired by back translation in NLP, we also conduct back retrieval to obtain more training data. Experimental results on MVD dataset show that our SSVMR achieves the state-of-the-art performance by a large margin, obtaining a relative improvement of 34.8% over the previous best model in terms of R@1.