# Reducing the Vision and Language Bias for Temporal Sentence Grounding

arXiv: https://arxiv.org/abs/2207.13457 · HTML: https://mlsanity.com/paper/2207.13457/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 51 references that are in the map.
- First posted on arXiv 27 Jul 2022.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [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)
- [Temporal Action Detection with Structured Segment Networks](https://mlsanity.com/paper/1704.06228/) (2017)
- [Span-based Localizing Network for Natural Language Video Localization](https://mlsanity.com/paper/2004.13931/) (2020)
- [Local-Global Video-Text Interactions for Temporal Grounding](https://mlsanity.com/paper/2004.07514/) (2020)
- [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)
- [Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation](https://mlsanity.com/paper/2106.15326/) (2021)
- [Context-aware Biaffine Localizing Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2103.11555/) (2021)
- [Interventional Video Grounding with Dual Contrastive Learning](https://mlsanity.com/paper/2106.11013/) (2021)
- [MAC: Mining Activity Concepts for Language-based Temporal Localization](https://mlsanity.com/paper/1811.08925/) (2018)
- [Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Prediction](https://mlsanity.com/paper/1909.05010/) (2019)
- [Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention](https://mlsanity.com/paper/1908.07236/) (2019)
- [Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos](https://mlsanity.com/paper/1906.02497/) (2019)
- [Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization](https://mlsanity.com/paper/2008.01403/) (2020)
- [Learning Modality Interaction for Temporal Sentence Localization and Event Captioning in Videos](https://mlsanity.com/paper/2007.14164/) (2020)
- [Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning](https://mlsanity.com/paper/2102.06605/) (2021)
- [Fine-grained Iterative Attention Network for TemporalLanguage Localization in Videos](https://mlsanity.com/paper/2008.02448/) (2020)
- [Memory-Guided Semantic Learning Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2201.00454/) (2022)

## Built on by (most cited first, 30 of 35)

- [HawkEye: Training Video-Text LLMs for Grounding Text in Videos](https://mlsanity.com/paper/2403.10228/) (2024)
- [Knowing Where to Focus: Event-aware Transformer for Video Grounding](https://mlsanity.com/paper/2308.06947/) (2023)
- [Arondight: Red Teaming Large Vision Language Models with Auto-generated Multi-modal Jailbreak Prompts](https://mlsanity.com/paper/2407.15050/) (2024)
- [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)
- [AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering](https://mlsanity.com/paper/2311.14906/) (2023)
- [Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training](https://mlsanity.com/paper/2303.00040/) (2023)
- [Hypotheses Tree Building for One-Shot Temporal Sentence Localization](https://mlsanity.com/paper/2301.01871/) (2023)
- [FlashVTG: Feature Layering and Adaptive Score Handling Network for Video Temporal Grounding](https://mlsanity.com/paper/2412.13441/) (2024)
- [Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding](https://mlsanity.com/paper/2301.00514/) (2023)
- [FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations](https://mlsanity.com/paper/2304.14252/) (2023)
- [TempSamp-R1: Effective Temporal Sampling with Reinforcement Fine-Tuning for Video LLMs](https://mlsanity.com/paper/2509.18056/) (2025)
- [Constraint and Union for Partially-Supervised Temporal Sentence Grounding](https://mlsanity.com/paper/2302.09850/) (2023)
- [Learning Grounded Vision-Language Representation for Versatile Understanding in Untrimmed Videos](https://mlsanity.com/paper/2303.06378/) (2023)
- [Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective](https://mlsanity.com/paper/2605.26441/) (2026)
- [Bias-Conflict Sample Synthesis and Adversarial Removal Debias Strategy for Temporal Sentence Grounding in Video](https://mlsanity.com/paper/2401.07567/) (2024)
- [VideoTG-R1: Boosting Video Temporal Grounding via Curriculum Reinforcement Learning on Reflected Boundary Annotations](https://mlsanity.com/paper/2510.23397/) (2025)
- [Diversifying Query: Region-Guided Transformer for Temporal Sentence Grounding](https://mlsanity.com/paper/2406.00143/) (2024)
- [Not All Inputs Are Valid: Towards Open-Set Video Moment Retrieval Using Language](https://mlsanity.com/paper/2605.29812/) (2026)
- [Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Language](https://mlsanity.com/paper/2605.29793/) (2026)
- [Jointly Visual- and Semantic-Aware Graph Memory Networks for Temporal Sentence Localization in Videos](https://mlsanity.com/paper/2303.01046/) (2023)
- [Contrast-Unity for Partially-Supervised Temporal Sentence Grounding](https://mlsanity.com/paper/2502.12917/) (2025)
- [SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation](https://mlsanity.com/paper/2504.05925/) (2025)
- [Sim-DETR: Unlock DETR for Temporal Sentence Grounding](https://mlsanity.com/paper/2509.23867/) (2025)
- [Transform-Equivariant Consistency Learning for Temporal Sentence Grounding](https://mlsanity.com/paper/2305.04123/) (2023)
- [When One Moment Isn't Enough: Multi-Moment Retrieval with Cross-Moment Interactions](https://mlsanity.com/paper/2510.17218/) (2025)
- [Tracking Objects and Activities with Attention for Temporal Sentence Grounding](https://mlsanity.com/paper/2302.10813/) (2023)
- [Diversified Augmentation with Domain Adaptation for Debiased Video Temporal Grounding](https://mlsanity.com/paper/2501.06746/) (2025)
- [BiMa: Towards Biases Mitigation for Text-Video Retrieval via Scene Element Guidance](https://mlsanity.com/paper/2506.03589/) (2025)
- [Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding](https://mlsanity.com/paper/2605.30742/) (2026)
- [Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding](https://mlsanity.com/paper/2512.06673/) (2025)

## Abstract (arXiv)

Temporal sentence grounding (TSG) is an important yet challenging task in multimedia information retrieval. Although previous TSG methods have achieved decent performance, they tend to capture the selection biases of frequently appeared video-query pairs in the dataset rather than present robust multimodal reasoning abilities, especially for the rarely appeared pairs. In this paper, we study the above issue of selection biases and accordingly propose a Debiasing-TSG (D-TSG) model to filter and remove the negative biases in both vision and language modalities for enhancing the model generalization ability. Specifically, we propose to alleviate the issue from two perspectives: 1) Feature distillation. We built a multi-modal debiasing branch to firstly capture the vision and language biases, and then apply a bias identification module to explicitly recognize the true negative biases and remove them from the benign multi-modal representations. 2) Contrastive sample generation. We construct two types of negative samples to enforce the model to accurately learn the aligned multi-modal semantics and make complete semantic reasoning. We apply the proposed model to both commonly and rarely appeared TSG cases, and demonstrate its effectiveness by achieving the state-of-the-art performance on three benchmark datasets (ActivityNet Caption, TACoS, and Charades-STA).