# Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding

arXiv: https://arxiv.org/abs/2605.30742 · HTML: https://mlsanity.com/paper/2605.30742/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 99 references that are in the map.
- First posted on arXiv 29 May 2026.

## Builds on (most cited first, 30 of 52)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [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)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language](https://mlsanity.com/paper/1912.03590/) (2019)
- [Span-based Localizing Network for Natural Language Video Localization](https://mlsanity.com/paper/2004.13931/) (2020)
- [TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval](https://mlsanity.com/paper/2001.09099/) (2020)
- [Local-Global Video-Text Interactions for Temporal Grounding](https://mlsanity.com/paper/2004.07514/) (2020)
- [FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration](https://mlsanity.com/paper/2108.06428/) (2021)
- [Context-aware Biaffine Localizing Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2103.11555/) (2021)
- [Modeling Visual Context is Key to Augmenting Object Detection Datasets](https://mlsanity.com/paper/1807.07428/) (2018)
- [Weakly Supervised Video Moment Retrieval From Text Queries](https://mlsanity.com/paper/1904.03282/) (2019)
- [Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization](https://mlsanity.com/paper/2008.01403/) (2020)
- [Weakly-Supervised Video Moment Retrieval via Semantic Completion Network](https://mlsanity.com/paper/1911.08199/) (2019)
- [Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video](https://mlsanity.com/paper/1906.02549/) (2019)
- [Memory-Guided Semantic Learning Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2201.00454/) (2022)
- [Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval](https://mlsanity.com/paper/2209.11572/) (2022)
- [Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos](https://mlsanity.com/paper/2109.06398/) (2021)
- [VLANet: Video-Language Alignment Network for Weakly-Supervised Video Moment Retrieval](https://mlsanity.com/paper/2008.10238/) (2020)
- [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)
- [LoGAN: Latent Graph Co-Attention Network for Weakly-Supervised Video Moment Retrieval](https://mlsanity.com/paper/1909.13784/) (2019)
- [Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation](https://mlsanity.com/paper/2107.11443/) (2021)
- [Weakly Supervised Temporal Adjacent Network for Language Grounding](https://mlsanity.com/paper/2106.16136/) (2021)
- [Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding](https://mlsanity.com/paper/2203.02966/) (2022)
- [Reducing the Vision and Language Bias for Temporal Sentence Grounding](https://mlsanity.com/paper/2207.13457/) (2022)

## Built on by (most cited first, 2 of 2)

- [Hierarchical Semantic-Augmented Navigation: Optimal Transport and Graph-Driven Reasoning for Vision-Language Navigation](https://mlsanity.com/paper/2606.01565/) (2026)
- [Turing Patterns for Multimedia: Reaction-Diffusion Multi-Modal Fusion for Language-Guided Video Moment Retrieval](https://mlsanity.com/paper/2606.01615/) (2026)

## Abstract (arXiv)

This paper addresses the task of temporal sentence grounding (TSG). Although many respectable works have made decent achievements in this important topic, they severely rely on massive expensive video-query paired annotations, which require a tremendous amount of human effort to collect in real-world applications. To this end, in this paper, we target a more practical but challenging TSG setting: unsupervised temporal sentence grounding, where both paired video-query and segment boundary annotations are unavailable during the network training. Considering that some other cross-modal tasks provide many easily available yet cheap labels, we tend to collect and transfer their simple cross-modal alignment knowledge into our complex scenarios: 1) We first explore the entity-aware object-guided appearance knowledge from the paired Image-Noun task, and adapt them into each independent video frame; 2) Then, we extract the event-aware action representation from the paired Video-Verb task, and further refine the action representation into more practical but complicated real-world cases by a newly proposed copy-paste approach; 3) By modulating and transferring both appearance and action knowledge into our challenging unsupervised task, our model can directly utilize this general knowledge to correlate videos and queries, and accurately retrieve the relevant segment without training. Extensive experiments on two challenging datasets (ActivityNet Captions and Charades-STA) show our effectiveness, outperforming existing unsupervised methods and even competitively beating supervised works.