# LoGAN: Latent Graph Co-Attention Network for Weakly-Supervised Video Moment Retrieval

arXiv: https://arxiv.org/abs/1909.13784 · HTML: https://mlsanity.com/paper/1909.13784/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

## Built on by (most cited first, 30 of 40)

- [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)
- [Learning Long-Term Spatial-Temporal Graphs for Active Speaker Detection](https://mlsanity.com/paper/2207.07783/) (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)
- [Video Moment Retrieval from Text Queries via Single Frame Annotation](https://mlsanity.com/paper/2204.09409/) (2022)
- [A Survey on Video Moment Localization](https://mlsanity.com/paper/2306.07515/) (2023)
- [Progressive Localization Networks for Language-based Moment Localization](https://mlsanity.com/paper/2102.01282/) (2021)
- [Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos](https://mlsanity.com/paper/2312.17117/) (2023)
- [SCANet: Scene Complexity Aware Network for Weakly-Supervised Video Moment Retrieval](https://mlsanity.com/paper/2310.05241/) (2023)
- [Repetitive Action Counting with Hybrid Temporal Relation Modeling](https://mlsanity.com/paper/2412.07233/) (2024)
- [Towards Debiasing Temporal Sentence Grounding in Video](https://mlsanity.com/paper/2111.04321/) (2021)
- [Weakly-Supervised Temporal Article Grounding](https://mlsanity.com/paper/2210.12444/) (2022)
- [Video and Text Matching with Conditioned Embeddings](https://mlsanity.com/paper/2110.11298/) (2021)
- [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)
- [D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation](https://mlsanity.com/paper/2308.04197/) (2023)
- [Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective](https://mlsanity.com/paper/2605.26441/) (2026)
- [Counterfactual Cross-modality Reasoning for Weakly Supervised Video Moment Localization](https://mlsanity.com/paper/2308.05648/) (2023)
- [Learning Spatial-Temporal Graphs for Active Speaker Detection](https://mlsanity.com/paper/2112.01479/) (2021)
- [Transformers in Action: Weakly Supervised Action Segmentation](https://mlsanity.com/paper/2201.05675/) (2022)
- [Commonsense for Zero-Shot Natural Language Video Localization](https://mlsanity.com/paper/2312.17429/) (2023)
- [VideoSAGE: Video Summarization with Graph Representation Learning](https://mlsanity.com/paper/2404.10539/) (2024)
- [Encoder-Decoder Based Long Short-Term Memory (LSTM) Model for Video Captioning](https://mlsanity.com/paper/2401.02052/) (2023)
- [Multi-scale 2D Representation Learning for weakly-supervised moment retrieval](https://mlsanity.com/paper/2111.02741/) (2021)
- [Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining](https://mlsanity.com/paper/2505.06557/) (2025)
- [Finding Optimal Video Moment without Training: Gaussian Boundary Optimization for Weakly Supervised Video Grounding](https://mlsanity.com/paper/2602.03071/) (2026)

## Abstract (arXiv)

The goal of weakly-supervised video moment retrieval is to localize the video segment most relevant to the given natural language query without access to temporal annotations during training. Prior strongly- and weakly-supervised approaches often leverage co-attention mechanisms to learn visual-semantic representations for localization. However, while such approaches tend to focus on identifying relationships between elements of the video and language modalities, there is less emphasis on modeling relational context between video frames given the semantic context of the query. Consequently, the above-mentioned visual-semantic representations, built upon local frame features, do not contain much contextual information. To address this limitation, we propose a Latent Graph Co-Attention Network (LoGAN) that exploits fine-grained frame-by-word interactions to reason about correspondences between all possible pairs of frames, given the semantic context of the query. Comprehensive experiments across two datasets, DiDeMo and Charades-Sta, demonstrate the effectiveness of our proposed latent co-attention model where it outperforms current state-of-the-art (SOTA) weakly-supervised approaches by a significant margin. Notably, it even achieves a 11% improvement to Recall@1 accuracy over strongly-supervised SOTA methods on DiDeMo.