# ExCL: Extractive Clip Localization Using Natural Language Descriptions

arXiv: https://arxiv.org/abs/1904.02755 · HTML: https://mlsanity.com/paper/1904.02755/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 30 of 82)

- [VTimeLLM: Empower LLM to Grasp Video Moments](https://mlsanity.com/paper/2311.18445/) (2023)
- [UniVTG: Towards Unified Video-Language Temporal Grounding](https://mlsanity.com/paper/2307.16715/) (2023)
- [Interventional Video Grounding with Dual Contrastive Learning](https://mlsanity.com/paper/2106.11013/) (2021)
- [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)
- [Knowing Where to Focus: Event-aware Transformer for Video Grounding](https://mlsanity.com/paper/2308.06947/) (2023)
- [QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries](https://mlsanity.com/paper/2107.09609/) (2021)
- [Video Corpus Moment Retrieval with Contrastive Learning](https://mlsanity.com/paper/2105.06247/) (2021)
- [TR-DETR: Task-Reciprocal Transformer for Joint Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2401.02309/) (2024)
- [Natural Language Video Localization: A Revisit in Span-based Question Answering Framework](https://mlsanity.com/paper/2102.13558/) (2021)
- [End-to-end Multi-modal Video Temporal Grounding](https://mlsanity.com/paper/2107.05624/) (2021)
- [Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video](https://mlsanity.com/paper/2203.06667/) (2022)
- [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)
- [SnAG: Scalable and Accurate Video Grounding](https://mlsanity.com/paper/2404.02257/) (2024)
- [CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval](https://mlsanity.com/paper/2109.10016/) (2021)
- [Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training](https://mlsanity.com/paper/2303.00040/) (2023)
- [Unsupervised Pre-training for Temporal Action Localization Tasks](https://mlsanity.com/paper/2203.13609/) (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)
- [BAM-DETR: Boundary-Aligned Moment Detection Transformer for Temporal Sentence Grounding in Videos](https://mlsanity.com/paper/2312.00083/) (2023)
- [Natural Language Video Localization with Learnable Moment Proposals](https://mlsanity.com/paper/2109.10678/) (2021)
- [Relation-aware Video Reading Comprehension for Temporal Language Grounding](https://mlsanity.com/paper/2110.05717/) (2021)
- [CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding](https://mlsanity.com/paper/2209.10918/) (2022)
- [Aligning Subtitles in Sign Language Videos](https://mlsanity.com/paper/2105.02877/) (2021)
- [Video Activity Localisation with Uncertainties in Temporal Boundary](https://mlsanity.com/paper/2206.12923/) (2022)
- [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)
- [Text-Visual Prompting for Efficient 2D Temporal Video Grounding](https://mlsanity.com/paper/2303.04995/) (2023)
- [A Survey on Video Moment Localization](https://mlsanity.com/paper/2306.07515/) (2023)

## Abstract (arXiv)

The task of retrieving clips within videos based on a given natural language query requires cross-modal reasoning over multiple frames. Prior approaches such as sliding window classifiers are inefficient, while text-clip similarity driven ranking-based approaches such as segment proposal networks are far more complicated. In order to select the most relevant video clip corresponding to the given text description, we propose a novel extractive approach that predicts the start and end frames by leveraging cross-modal interactions between the text and video - this removes the need to retrieve and re-rank multiple proposal segments. Using recurrent networks we encode the two modalities into a joint representation which is then used in different variants of start-end frame predictor networks. Through extensive experimentation and ablative analysis, we demonstrate that our simple and elegant approach significantly outperforms state of the art on two datasets and has comparable performance on a third.