# Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Prediction

arXiv: https://arxiv.org/abs/1909.05010 · HTML: https://mlsanity.com/paper/1909.05010/

## Key facts

- Cited by 84 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 30 of 84)

- [Query-Dependent Video Representation for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2303.13874/) (2023)
- [Context-aware Biaffine Localizing Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2103.11555/) (2021)
- [TubeDETR: Spatio-Temporal Video Grounding with Transformers](https://mlsanity.com/paper/2203.16434/) (2022)
- [Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding](https://mlsanity.com/paper/2109.04872/) (2021)
- [Boundary Proposal Network for Two-Stage Natural Language Video Localization](https://mlsanity.com/paper/2103.08109/) (2021)
- [Deconfounded Video Moment Retrieval with Causal Intervention](https://mlsanity.com/paper/2106.01534/) (2021)
- [UnLoc: A Unified Framework for Video Localization Tasks](https://mlsanity.com/paper/2308.11062/) (2023)
- [Video Corpus Moment Retrieval with Contrastive Learning](https://mlsanity.com/paper/2105.06247/) (2021)
- [Natural Language Video Localization: A Revisit in Span-based Question Answering Framework](https://mlsanity.com/paper/2102.13558/) (2021)
- [On Pursuit of Designing Multi-modal Transformer for Video Grounding](https://mlsanity.com/paper/2109.06085/) (2021)
- [Memory-Guided Semantic Learning Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2201.00454/) (2022)
- [Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval](https://mlsanity.com/paper/2209.11572/) (2022)
- [Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos](https://mlsanity.com/paper/2109.06398/) (2021)
- [Hierarchical Local-Global Transformer for Temporal Sentence Grounding](https://mlsanity.com/paper/2208.14882/) (2022)
- [Temporal Sentence Grounding in Videos: A Survey and Future Directions](https://mlsanity.com/paper/2201.08071/) (2022)
- [SnAG: Scalable and Accurate Video Grounding](https://mlsanity.com/paper/2404.02257/) (2024)
- [Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding](https://mlsanity.com/paper/2203.02966/) (2022)
- [Reducing the Vision and Language Bias for Temporal Sentence Grounding](https://mlsanity.com/paper/2207.13457/) (2022)
- [Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding](https://mlsanity.com/paper/2109.06400/) (2021)
- [Unsupervised Temporal Video Grounding with Deep Semantic Clustering](https://mlsanity.com/paper/2201.05307/) (2022)
- [Embracing Uncertainty: Decoupling and De-bias for Robust Temporal Grounding](https://mlsanity.com/paper/2103.16848/) (2021)
- [A Survey on Temporal Sentence Grounding in Videos](https://mlsanity.com/paper/2109.08039/) (2021)
- [Relation-aware Video Reading Comprehension for Temporal Language Grounding](https://mlsanity.com/paper/2110.05717/) (2021)
- [Step-Wise Hierarchical Alignment Network for Image-Text Matching](https://mlsanity.com/paper/2106.06509/) (2021)
- [Exploring Motion and Appearance Information for Temporal Sentence Grounding](https://mlsanity.com/paper/2201.00457/) (2022)
- [Context Understanding in Computer Vision: A Survey](https://mlsanity.com/paper/2302.05011/) (2023)
- [Scanning Only Once: An End-to-end Framework for Fast Temporal Grounding in Long Videos](https://mlsanity.com/paper/2303.08345/) (2023)
- [FlashVTG: Feature Layering and Adaptive Score Handling Network for Video Temporal Grounding](https://mlsanity.com/paper/2412.13441/) (2024)
- [Parallel Attention Network with Sequence Matching for Video Grounding](https://mlsanity.com/paper/2105.08481/) (2021)
- [Can Shuffling Video Benefit Temporal Bias Problem: A Novel Training Framework for Temporal Grounding](https://mlsanity.com/paper/2207.14698/) (2022)

## Abstract (arXiv)

The task of temporally grounding language queries in videos is to temporally localize the best matched video segment corresponding to a given language (sentence). It requires certain models to simultaneously perform visual and linguistic understandings. Previous work predominantly ignores the precision of segment localization. Sliding window based methods use predefined search window sizes, which suffer from redundant computation, while existing anchor-based approaches fail to yield precise localization. We address this issue by proposing an end-to-end boundary-aware model, which uses a lightweight branch to predict semantic boundaries corresponding to the given linguistic information. To better detect semantic boundaries, we propose to aggregate contextual information by explicitly modeling the relationship between the current element and its neighbors. The most confident segments are subsequently selected based on both anchor and boundary predictions at the testing stage. The proposed model, dubbed Contextual Boundary-aware Prediction (CBP), outperforms its competitors with a clear margin on three public datasets. All codes are available on https://github.com/JaywongWang/CBP .