# A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metric

arXiv: https://arxiv.org/abs/2101.09028 · HTML: https://mlsanity.com/paper/2101.09028/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 39 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 23 of 23)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017)
- [Localizing Moments in Video with Natural Language](https://mlsanity.com/paper/1708.01641/) (2017)
- [TALL: Temporal Activity Localization via Language Query](https://mlsanity.com/paper/1705.02101/) (2017)
- [Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding](https://mlsanity.com/paper/1604.01753/) (2016)
- [Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language](https://mlsanity.com/paper/1912.03590/) (2019)
- [Dense Regression Network for Video Grounding](https://mlsanity.com/paper/2004.03545/) (2020)
- [To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression](https://mlsanity.com/paper/1804.07014/) (2018)
- [MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment](https://mlsanity.com/paper/1812.00087/) (2018)
- [Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos](https://mlsanity.com/paper/1910.14303/) (2019)
- [Multilevel Language and Vision Integration for Text-to-Clip Retrieval](https://mlsanity.com/paper/1804.05113/) (2018)
- [Weakly Supervised Video Moment Retrieval From Text Queries](https://mlsanity.com/paper/1904.03282/) (2019)
- [MAC: Mining Activity Concepts for Language-based Temporal Localization](https://mlsanity.com/paper/1811.08925/) (2018)
- [Localizing Moments in Video with Temporal Language](https://mlsanity.com/paper/1809.01337/) (2018)
- [Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos](https://mlsanity.com/paper/1906.02497/) (2019)
- [Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos](https://mlsanity.com/paper/1901.06829/) (2019)
- [Weakly Supervised Dense Event Captioning in Videos](https://mlsanity.com/paper/1812.03849/) (2018)
- [Uncovering Hidden Challenges in Query-Based Video Moment Retrieval](https://mlsanity.com/paper/2009.00325/) (2020)
- [Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in Video](https://mlsanity.com/paper/2001.06680/) (2020)
- [WSLLN: Weakly Supervised Natural Language Localization Networks](https://mlsanity.com/paper/1909.00239/) (2019)
- [Tripping through time: Efficient Localization of Activities in Videos](https://mlsanity.com/paper/1904.09936/) (2019)
- [Weakly-Supervised Multi-Level Attentional Reconstruction Network for Grounding Textual Queries in Videos](https://mlsanity.com/paper/2003.07048/) (2020)

## Built on by (most cited first, 30 of 42)

- [Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding](https://mlsanity.com/paper/2503.13377/) (2025)
- [MomentDiff: Generative Video Moment Retrieval from Random to Real](https://mlsanity.com/paper/2307.02869/) (2023)
- [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)
- [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)
- [Training-free Video Temporal Grounding using Large-scale Pre-trained Models](https://mlsanity.com/paper/2408.16219/) (2024)
- [Classification-Then-Grounding: Reformulating Video Scene Graphs as Temporal Bipartite Graphs](https://mlsanity.com/paper/2112.04222/) (2021)
- [NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation](https://mlsanity.com/paper/2207.13316/) (2022)
- [On the Consistency of Video Large Language Models in Temporal Comprehension](https://mlsanity.com/paper/2411.12951/) (2024)
- [Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos](https://mlsanity.com/paper/2312.17117/) (2023)
- [Background-aware Moment Detection for Video Moment Retrieval](https://mlsanity.com/paper/2306.02728/) (2023)
- [Enrich and Detect: Video Temporal Grounding with Multimodal LLMs](https://mlsanity.com/paper/2510.17023/) (2025)
- [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)
- [Weakly-Supervised Temporal Article Grounding](https://mlsanity.com/paper/2210.12444/) (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)
- [MLLM as Video Narrator: Mitigating Modality Imbalance in Video Moment Retrieval](https://mlsanity.com/paper/2406.17880/) (2024)
- [Temporal Sentence Grounding in Streaming Videos](https://mlsanity.com/paper/2308.07102/) (2023)
- [LLM4VG: Large Language Models Evaluation for Video Grounding](https://mlsanity.com/paper/2312.14206/) (2023)
- [Vid-Morp: Video Moment Retrieval Pretraining from Unlabeled Videos in the Wild](https://mlsanity.com/paper/2412.00811/) (2024)
- [TimeScope: Towards Task-Oriented Temporal Grounding In Long Videos](https://mlsanity.com/paper/2509.26360/) (2025)
- [Temporally Grounding Instructional Diagrams in Unconstrained Videos](https://mlsanity.com/paper/2407.12066/) (2024)
- [SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation](https://mlsanity.com/paper/2504.05925/) (2025)
- [EvoGround: Self-Evolving Video Agents for Video Temporal Grounding](https://mlsanity.com/paper/2605.13803/) (2026)
- [Transform-Equivariant Consistency Learning for Temporal Sentence Grounding](https://mlsanity.com/paper/2305.04123/) (2023)
- [Learning Temporal Sentence Grounding From Narrated EgoVideos](https://mlsanity.com/paper/2310.17395/) (2023)
- [UAL-Bench: The First Comprehensive Unusual Activity Localization Benchmark](https://mlsanity.com/paper/2410.01180/) (2024)
- [Boosting Temporal Sentence Grounding via Causal Inference](https://mlsanity.com/paper/2507.04958/) (2025)

## Abstract (arXiv)

Temporal Sentence Grounding in Videos (TSGV), i.e., grounding a natural language sentence which indicates complex human activities in a long and untrimmed video sequence, has received unprecedented attentions over the last few years. Although each newly proposed method plausibly can achieve better performance than previous ones, current TSGV models still tend to capture the moment annotation biases and fail to take full advantage of multi-modal inputs. Even more incredibly, several extremely simple baselines without training can also achieve state-of-the-art performance. In this paper, we take a closer look at the existing evaluation protocols for TSGV, and find that both the prevailing dataset splits and evaluation metrics are the devils to cause unreliable benchmarking. To this end, we propose to re-organize two widely-used TSGV benchmarks (ActivityNet Captions and Charades-STA). Specifically, we deliberately make the ground-truth moment distribution different in the training and test splits, i.e., out-of-distribution (OOD) testing. Meanwhile, we introduce a new evaluation metric dR@n,IoU@m to calibrate the basic IoU scores by penalizing on the bias-influenced moment predictions and alleviate the inflating evaluations caused by the dataset annotation biases such as overlong ground-truth moments. Under our new evaluation protocol, we conduct extensive experiments and ablation studies on eight state-of-the-art TSGV methods. All the results demonstrate that the re-organized dataset splits and new metric can better monitor the progress in TSGV. Our reorganized datsets are available at https://github.com/yytzsy/grounding_changing_distribution.