# Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video

arXiv: https://arxiv.org/abs/2001.09308 · HTML: https://mlsanity.com/paper/2001.09308/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2020.

## Built on by (most cited first, 29 of 29)

- [Masked Motion Encoding for Self-Supervised Video Representation Learning](https://mlsanity.com/paper/2210.06096/) (2022)
- [Temporal Sentence Grounding in Videos: A Survey and Future Directions](https://mlsanity.com/paper/2201.08071/) (2022)
- [Zero-shot Natural Language Video Localization](https://mlsanity.com/paper/2110.00428/) (2021)
- [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)
- [TimeRefine: Temporal Grounding with Time Refining Video LLM](https://mlsanity.com/paper/2412.09601/) (2024)
- [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)
- [Cross-modal Causal Relation Alignment for Video Question Grounding](https://mlsanity.com/paper/2503.07635/) (2025)
- [Weakly-Supervised Temporal Article Grounding](https://mlsanity.com/paper/2210.12444/) (2022)
- [Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos](https://mlsanity.com/paper/2208.01954/) (2022)
- [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)
- [Not All Inputs Are Valid: Towards Open-Set Video Moment Retrieval Using Language](https://mlsanity.com/paper/2605.29812/) (2026)
- [EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model](https://mlsanity.com/paper/2312.02483/) (2023)
- [Commonsense for Zero-Shot Natural Language Video Localization](https://mlsanity.com/paper/2312.17429/) (2023)
- [Explore-And-Match: Bridging Proposal-Based and Proposal-Free With Transformer for Sentence Grounding in Videos](https://mlsanity.com/paper/2201.10168/) (2022)
- [Learning Temporal Sentence Grounding From Narrated EgoVideos](https://mlsanity.com/paper/2310.17395/) (2023)
- [Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining](https://mlsanity.com/paper/2505.06557/) (2025)
- [ResidualViT for Efficient Temporally Dense Video Encoding](https://mlsanity.com/paper/2509.13255/) (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)
- [Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos](https://mlsanity.com/paper/2204.13548/) (2022)
- [Multi-Modal Domain Adaptation Across Video Scenes for Temporal Video Grounding](https://mlsanity.com/paper/2312.13633/) (2023)
- [Multi-proposal Collaboration and Multi-task Training for Weakly-supervised Video Moment Retrieval](https://mlsanity.com/paper/2605.14838/) (2026)

## Abstract (arXiv)

In this paper, we study the problem of weakly-supervised temporal grounding of sentence in video. Specifically, given an untrimmed video and a query sentence, our goal is to localize a temporal segment in the video that semantically corresponds to the query sentence, with no reliance on any temporal annotation during training. We propose a two-stage model to tackle this problem in a coarse-to-fine manner. In the coarse stage, we first generate a set of fixed-length temporal proposals using multi-scale sliding windows, and match their visual features against the sentence features to identify the best-matched proposal as a coarse grounding result. In the fine stage, we perform a fine-grained matching between the visual features of the frames in the best-matched proposal and the sentence features to locate the precise frame boundary of the fine grounding result. Comprehensive experiments on the ActivityNet Captions dataset and the Charades-STA dataset demonstrate that our two-stage model achieves compelling performance.