# Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos

arXiv: https://arxiv.org/abs/2008.08257 · HTML: https://mlsanity.com/paper/2008.08257/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 23 of 23)

- [Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval](https://mlsanity.com/paper/2209.11572/) (2022)
- [You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos](https://mlsanity.com/paper/2303.07863/) (2023)
- [Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation](https://mlsanity.com/paper/2107.11443/) (2021)
- [Temporal Sentence Grounding in Videos: A Survey and Future Directions](https://mlsanity.com/paper/2201.08071/) (2022)
- [Unsupervised Temporal Video Grounding with Deep Semantic Clustering](https://mlsanity.com/paper/2201.05307/) (2022)
- [A Survey on Temporal Sentence Grounding in Videos](https://mlsanity.com/paper/2109.08039/) (2021)
- [Language-free Training for Zero-shot Video Grounding](https://mlsanity.com/paper/2210.12977/) (2022)
- [A Survey on Video Moment Localization](https://mlsanity.com/paper/2306.07515/) (2023)
- [SCANet: Scene Complexity Aware Network for Weakly-Supervised Video Moment Retrieval](https://mlsanity.com/paper/2310.05241/) (2023)
- [Weakly-Supervised Temporal Article Grounding](https://mlsanity.com/paper/2210.12444/) (2022)
- [Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding](https://mlsanity.com/paper/2312.16388/) (2023)
- [Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding](https://mlsanity.com/paper/2403.11463/) (2024)
- [Align and Aggregate: Compositional Reasoning with Video Alignment and Answer Aggregation for Video Question-Answering](https://mlsanity.com/paper/2407.03008/) (2024)
- [Self-supervised Learning for Semi-supervised Temporal Language Grounding](https://mlsanity.com/paper/2109.11475/) (2021)
- [Counterfactual Cross-modality Reasoning for Weakly Supervised Video Moment Localization](https://mlsanity.com/paper/2308.05648/) (2023)
- [A Survey on Natural Language Video Localization](https://mlsanity.com/paper/2104.00234/) (2021)
- [EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model](https://mlsanity.com/paper/2312.02483/) (2023)
- [Fine-grained Semantic Alignment Network for Weakly Supervised Temporal Language Grounding](https://mlsanity.com/paper/2210.11933/) (2022)
- [Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining](https://mlsanity.com/paper/2505.06557/) (2025)
- [Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding](https://mlsanity.com/paper/2605.30742/) (2026)
- [Hybrid-Learning Video Moment Retrieval across Multi-Domain Labels](https://mlsanity.com/paper/2406.01791/) (2024)
- [TubeRMC: Tube-conditioned Reconstruction with Mutual Constraints for Weakly-supervised Spatio-Temporal Video Grounding](https://mlsanity.com/paper/2511.10241/) (2025)
- [Multi-proposal Collaboration and Multi-task Training for Weakly-supervised Video Moment Retrieval](https://mlsanity.com/paper/2605.14838/) (2026)

## Abstract (arXiv)

Video moment retrieval aims to localize the target moment in an video according to the given sentence. The weak-supervised setting only provides the video-level sentence annotations during training. Most existing weak-supervised methods apply a MIL-based framework to develop inter-sample confrontment, but ignore the intra-sample confrontment between moments with semantically similar contents. Thus, these methods fail to distinguish the target moment from plausible negative moments. In this paper, we propose a novel Regularized Two-Branch Proposal Network to simultaneously consider the inter-sample and intra-sample confrontments. Concretely, we first devise a language-aware filter to generate an enhanced video stream and a suppressed video stream. We then design the sharable two-branch proposal module to generate positive proposals from the enhanced stream and plausible negative proposals from the suppressed one for sufficient confrontment. Further, we apply the proposal regularization to stabilize the training process and improve model performance. The extensive experiments show the effectiveness of our method. Our code is released at here.