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Dense Regression Network for Video Grounding

arXiv 2004.03545 ↗First posted 7 Apr 2020cs.CVcited by 176

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Abstract (arXiv)

We address the problem of video grounding from natural language queries. The key challenge in this task is that one training video might only contain a few annotated starting/ending frames that can be used as positive examples for model training. Most conventional approaches directly train a binary classifier using such imbalance data, thus achieving inferior results. The key idea of this paper is to use the distances between the frame within the ground truth and the starting (ending) frame as dense supervisions to improve the video grounding accuracy. Specifically, we design a novel dense regression network (DRN) to regress the distances from each frame to the starting (ending) frame of the video segment described by the query. We also propose a simple but effective IoU regression head module to explicitly consider the localization quality of the grounding results (i.e., the IoU between the predicted location and the ground truth). Experimental results show that our approach significantly outperforms state-of-the-arts on three datasets (i.e., Charades-STA, ActivityNet-Captions, and TACoS).

Key facts. Cited by 176 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2020.

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Citing papers per year: 2021: 36; 2022: 34; 2023: 44; 2024: 23; 2025: 24; 2026: 15.

176 papers.

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  1. 2022Efficient Test-Time Model Adaptation without Forgettingcited by 467
  2. 2022ActionFormer: Localizing Moments of Actions with Transformerscited by 308
  3. 2023UniVTG: Towards Unified Video-Language Temporal Groundingcited by 189
  4. 2021Relaxed Transformer Decoders for Direct Action Proposal Generationcited by 116
  5. 2021Context-aware Biaffine Localizing Network for Temporal Sentence Groundingcited by 107
  6. 2021MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptionscited by 107
  7. 2022TubeDETR: Spatio-Temporal Video Grounding with Transformerscited by 97
  8. 2021Interventional Video Grounding with Dual Contrastive Learningcited by 94
  9. 2021Negative Sample Matters: A Renaissance of Metric Learning for Temporal Groundingcited by 90
  10. 2022Language Models Can See: Plugging Visual Controls in Text Generationcited by 89
  11. 2021Boundary Proposal Network for Two-Stage Natural Language Video Localizationcited by 88
  12. 2021Deconfounded Video Moment Retrieval with Causal Interventioncited by 84
38 more
  1. 2023Knowing Where to Focus: Event-aware Transformer for Video Groundingcited by 79
  2. 2023UnLoc: A Unified Framework for Video Localization Taskscited by 66
  3. 2021Video Corpus Moment Retrieval with Contrastive Learningcited by 60
  4. 2023MomentDiff: Generative Video Moment Retrieval from Random to Realcited by 58
  5. 2021Natural Language Video Localization: A Revisit in Span-based Question Answering Frameworkcited by 55
  6. 2021On Pursuit of Designing Multi-modal Transformer for Video Groundingcited by 54
  7. 2021End-to-end Multi-modal Video Temporal Groundingcited by 47
  8. 2022Memory-Guided Semantic Learning Network for Temporal Sentence Groundingcited by 46
  9. 2021A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metriccited by 42
  10. 2021Adaptive Proposal Generation Network for Temporal Sentence Localization in Videoscited by 42
  11. 2023You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videoscited by 41
  12. 2023MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformercited by 40
  13. 2021Cross-Sentence Temporal and Semantic Relations in Video Activity Localisationcited by 39
  14. 2022Masked Motion Encoding for Self-Supervised Video Representation Learningcited by 39
  15. 2022Hierarchical Local-Global Transformer for Temporal Sentence Groundingcited by 38
  16. 2022Temporal Sentence Grounding in Videos: A Survey and Future Directionscited by 37
  17. 2024SnAG: Scalable and Accurate Video Groundingcited by 36
  18. 2022Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Groundingcited by 35
  19. 2022Reducing the Vision and Language Bias for Temporal Sentence Groundingcited by 35
  20. 2021Zero-shot Natural Language Video Localizationcited by 34
  21. 2021Contrastive Neural Architecture Search with Neural Architecture Comparatorscited by 33
  22. 2023Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Trainingcited by 32
  23. 2024Enhancing Video-Language Representations with Structural Spatio-Temporal Alignmentcited by 32
  24. 2021Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Groundingcited by 31
  25. 2023Correlation-Guided Query-Dependency Calibration for Video Temporal Groundingcited by 31
  26. 2022Unsupervised Temporal Video Grounding with Deep Semantic Clusteringcited by 30
  27. 2021Embracing Uncertainty: Decoupling and De-bias for Robust Temporal Groundingcited by 29
  28. 2021A Survey on Temporal Sentence Grounding in Videoscited by 29
  29. 2023BAM-DETR: Boundary-Aligned Moment Detection Transformer for Temporal Sentence Grounding in Videoscited by 29
  30. 2021Support-Set Based Cross-Supervision for Video Groundingcited by 28
  31. 2021Natural Language Video Localization with Learnable Moment Proposalscited by 27
  32. 2021Relation-aware Video Reading Comprehension for Temporal Language Groundingcited by 27
  33. 2022CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Groundingcited by 27
  34. 2021Aligning Subtitles in Sign Language Videoscited by 26
  35. 2021Towards Accurate Text-based Image Captioning with Content Diversity Explorationcited by 25
  36. 2023Hypotheses Tree Building for One-Shot Temporal Sentence Localizationcited by 25
  37. 2024SpikeMba: Multi-Modal Spiking Saliency Mamba for Temporal Video Groundingcited by 25
  38. 2024Task-Driven Exploration: Decoupling and Inter-Task Feedback for Joint Moment Retrieval and Highlight Detectioncited by 25

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