# Contrast-Unity for Partially-Supervised Temporal Sentence Grounding

arXiv: https://arxiv.org/abs/2502.12917 · HTML: https://mlsanity.com/paper/2502.12917/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 47 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 30 of 32)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Localizing Moments in Video with Natural Language](https://mlsanity.com/paper/1708.01641/) (2017)
- [Prompting Visual-Language Models for Efficient Video Understanding](https://mlsanity.com/paper/2112.04478/) (2021)
- [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)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Context-aware Biaffine Localizing Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2103.11555/) (2021)
- [Weakly Supervised Video Moment Retrieval From Text Queries](https://mlsanity.com/paper/1904.03282/) (2019)
- [Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention](https://mlsanity.com/paper/1908.07236/) (2019)
- [Weakly-Supervised Video Moment Retrieval via Semantic Completion Network](https://mlsanity.com/paper/1911.08199/) (2019)
- [Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning](https://mlsanity.com/paper/2203.13049/) (2022)
- [Annotation-free Audio-Visual Segmentation](https://mlsanity.com/paper/2305.11019/) (2023)
- [Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation](https://mlsanity.com/paper/2206.12772/) (2022)
- [Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation](https://mlsanity.com/paper/2107.11443/) (2021)
- [Reducing the Vision and Language Bias for Temporal Sentence Grounding](https://mlsanity.com/paper/2207.13457/) (2022)
- [Action Recognition from Single Timestamp Supervision in Untrimmed Videos](https://mlsanity.com/paper/1904.04689/) (2019)
- [Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding](https://mlsanity.com/paper/2109.06400/) (2021)
- [Joint Inference of Groups, Events and Human Roles in Aerial Videos](https://mlsanity.com/paper/1505.05957/) (2015)
- [Audio-Visual Segmentation via Unlabeled Frame Exploitation](https://mlsanity.com/paper/2403.11074/) (2024)
- [G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory](https://mlsanity.com/paper/2307.14277/) (2023)
- [Point-Level Temporal Action Localization: Bridging Fully-supervised Proposals to Weakly-supervised Losses](https://mlsanity.com/paper/2012.08236/) (2020)
- [Video Moment Retrieval from Text Queries via Single Frame Annotation](https://mlsanity.com/paper/2204.09409/) (2022)
- [Multi-modal Prompting for Low-Shot Temporal Action Localization](https://mlsanity.com/paper/2303.11732/) (2023)
- [Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2104.02357/) (2021)
- [SCANet: Scene Complexity Aware Network for Weakly-Supervised Video Moment Retrieval](https://mlsanity.com/paper/2310.05241/) (2023)
- [Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Large Models](https://mlsanity.com/paper/2407.11717/) (2024)
- [Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-training](https://mlsanity.com/paper/2412.00440/) (2024)
- [Distilling Vision-Language Pre-training to Collaborate with Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2212.09335/) (2022)
- [Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Models](https://mlsanity.com/paper/2312.07408/) (2023)

## Built on by (most cited first, 4 of 4)

- [Explore More, Learn Better: Parallel MLLM Embeddings under Mutual Information Minimization](https://mlsanity.com/paper/2511.01588/) (2025)
- [Squeeze Out Tokens from Sample for Finer-Grained Data Governance](https://mlsanity.com/paper/2503.14559/) (2025)
- [Wave-Particle (Continuous-Discrete) Dualistic Visual Tokenization for Unified Understanding and Generation](https://mlsanity.com/paper/2511.01593/) (2025)
- [TRACE-Bench: Decomposing and Diagnosing Multi-Reference Image Generation](https://mlsanity.com/paper/2608.16765/) (2026)

## Abstract (arXiv)

Temporal sentence grounding aims to detect event timestamps described by the natural language query from given untrimmed videos. The existing fully-supervised setting achieves great results but requires expensive annotation costs; while the weakly-supervised setting adopts cheap labels but performs poorly. To pursue high performance with less annotation costs, this paper introduces an intermediate partially-supervised setting, i.e., only short-clip is available during training. To make full use of partial labels, we specially design one contrast-unity framework, with the two-stage goal of implicit-explicit progressive grounding. In the implicit stage, we align event-query representations at fine granularity using comprehensive quadruple contrastive learning: event-query gather, event-background separation, intra-cluster compactness and inter-cluster separability. Then, high-quality representations bring acceptable grounding pseudo-labels. In the explicit stage, to explicitly optimize grounding objectives, we train one fully-supervised model using obtained pseudo-labels for grounding refinement and denoising. Extensive experiments and thoroughly ablations on Charades-STA and ActivityNet Captions demonstrate the significance of partial supervision, as well as our superior performance.