# Learning Sample Importance for Cross-Scenario Video Temporal Grounding

arXiv: https://arxiv.org/abs/2201.02848 · HTML: https://mlsanity.com/paper/2201.02848/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 8 Jan 2022.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Unbiased Scene Graph Generation from Biased Training](https://mlsanity.com/paper/2002.11949/) (2020)
- [BMN: Boundary-Matching Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1907.09702/) (2019)
- [Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language](https://mlsanity.com/paper/1912.03590/) (2019)
- [RUBi: Reducing Unimodal Biases in Visual Question Answering](https://mlsanity.com/paper/1906.10169/) (2019)
- [Counterfactual Samples Synthesizing for Robust Visual Question Answering](https://mlsanity.com/paper/2003.06576/) (2020)
- [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)
- [Context-aware Biaffine Localizing Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2103.11555/) (2021)
- [Overcoming Language Priors in Visual Question Answering with Adversarial Regularization](https://mlsanity.com/paper/1810.03649/) (2018)
- [Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Prediction](https://mlsanity.com/paper/1909.05010/) (2019)
- [Localizing Moments in Video with Temporal Language](https://mlsanity.com/paper/1809.01337/) (2018)
- [ExCL: Extractive Clip Localization Using Natural Language Descriptions](https://mlsanity.com/paper/1904.02755/) (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)
- [Two Causal Principles for Improving Visual Dialog](https://mlsanity.com/paper/1911.10496/) (2019)
- [VLG-Net: Video-Language Graph Matching Network for Video Grounding](https://mlsanity.com/paper/2011.10132/) (2020)
- [Greedy Gradient Ensemble for Robust Visual Question Answering](https://mlsanity.com/paper/2107.12651/) (2021)
- [Exploiting Temporal Relationships in Video Moment Localization with Natural Language](https://mlsanity.com/paper/1908.03846/) (2019)
- [Compositional Temporal Visual Grounding of Natural Language Event Descriptions](https://mlsanity.com/paper/1912.02256/) (2019)
- [DORi: Discovering Object Relationship for Moment Localization of a Natural-Language Query in Video](https://mlsanity.com/paper/2010.06260/) (2020)

## Built on by (most cited first, 8 of 8)

- [Temporal Sentence Grounding in Videos: A Survey and Future Directions](https://mlsanity.com/paper/2201.08071/) (2022)
- [Training-free Video Temporal Grounding using Large-scale Pre-trained Models](https://mlsanity.com/paper/2408.16219/) (2024)
- [Vid-Morp: Video Moment Retrieval Pretraining from Unlabeled Videos in the Wild](https://mlsanity.com/paper/2412.00811/) (2024)
- [FedVMR: A New Federated Learning method for Video Moment Retrieval](https://mlsanity.com/paper/2210.15977/) (2022)
- [Hybrid-Learning Video Moment Retrieval across Multi-Domain Labels](https://mlsanity.com/paper/2406.01791/) (2024)
- [SlotVTG: Object-Centric Adapter for Generalizable Video Temporal Grounding](https://mlsanity.com/paper/2603.25733/) (2026)
- [EVIDENT: Routing MLLM Adaptation through Entity-Grounded Visual Evidence for Cross-Domain Video Temporal Grounding](https://mlsanity.com/paper/2605.26104/) (2026)
- [DART: Difficulty-Adaptive Routing for Zero-Shot Video Temporal Grounding](https://mlsanity.com/paper/2607.00672/) (2026)

## Abstract (arXiv)

The task of temporal grounding aims to locate video moment in an untrimmed video, with a given sentence query. This paper for the first time investigates some superficial biases that are specific to the temporal grounding task, and proposes a novel targeted solution. Most alarmingly, we observe that existing temporal ground models heavily rely on some biases (e.g., high preference on frequent concepts or certain temporal intervals) in the visual modal. This leads to inferior performance when generalizing the model in cross-scenario test setting. To this end, we propose a novel method called Debiased Temporal Language Localizer (DebiasTLL) to prevent the model from naively memorizing the biases and enforce it to ground the query sentence based on true inter-modal relationship. Debias-TLL simultaneously trains two models. By our design, a large discrepancy of these two models' predictions when judging a sample reveals higher probability of being a biased sample. Harnessing the informative discrepancy, we devise a data re-weighing scheme for mitigating the data biases. We evaluate the proposed model in cross-scenario temporal grounding, where the train / test data are heterogeneously sourced. Experiments show large-margin superiority of the proposed method in comparison with state-of-the-art competitors.