# Weakly-Supervised Multi-Level Attentional Reconstruction Network for Grounding Textual Queries in Videos

arXiv: https://arxiv.org/abs/2003.07048 · HTML: https://mlsanity.com/paper/2003.07048/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 30 of 31)

- [Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval](https://mlsanity.com/paper/2209.11572/) (2022)
- [A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metric](https://mlsanity.com/paper/2101.09028/) (2021)
- [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)
- [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)
- [Hypotheses Tree Building for One-Shot Temporal Sentence Localization](https://mlsanity.com/paper/2301.01871/) (2023)
- [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)
- [Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding](https://mlsanity.com/paper/2301.09071/) (2023)
- [Constraint and Union for Partially-Supervised Temporal Sentence Grounding](https://mlsanity.com/paper/2302.09850/) (2023)
- [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)
- [Towards Debiasing Temporal Sentence Grounding in Video](https://mlsanity.com/paper/2111.04321/) (2021)
- [A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach](https://mlsanity.com/paper/2203.05243/) (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)
- [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)
- [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)
- [EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model](https://mlsanity.com/paper/2312.02483/) (2023)
- [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)
- [Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding](https://mlsanity.com/paper/2605.30742/) (2026)
- [Multi-Scale Self-Contrastive Learning with Hard Negative Mining for Weakly-Supervised Query-based Video Grounding](https://mlsanity.com/paper/2203.03838/) (2022)
- [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)
- [Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos](https://mlsanity.com/paper/2204.13548/) (2022)
- [Focusing On Targets For Improving Weakly Supervised Visual Grounding](https://mlsanity.com/paper/2302.11252/) (2023)

## Abstract (arXiv)

The task of temporally grounding textual queries in videos is to localize one video segment that semantically corresponds to the given query. Most of the existing approaches rely on segment-sentence pairs (temporal annotations) for training, which are usually unavailable in real-world scenarios. In this work we present an effective weakly-supervised model, named as Multi-Level Attentional Reconstruction Network (MARN), which only relies on video-sentence pairs during the training stage. The proposed method leverages the idea of attentional reconstruction and directly scores the candidate segments with the learnt proposal-level attentions. Moreover, another branch learning clip-level attention is exploited to refine the proposals at both the training and testing stage. We develop a novel proposal sampling mechanism to leverage intra-proposal information for learning better proposal representation and adopt 2D convolution to exploit inter-proposal clues for learning reliable attention map. Experiments on Charades-STA and ActivityNet-Captions datasets demonstrate the superiority of our MARN over the existing weakly-supervised methods.