# TAG: A Simple Yet Effective Temporal-Aware Approach for Zero-Shot Video Temporal Grounding

arXiv: https://arxiv.org/abs/2508.07925 · HTML: https://mlsanity.com/paper/2508.07925/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 41 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 30 of 31)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://mlsanity.com/paper/2306.05424/) (2023)
- [VideoChat: Chat-Centric Video Understanding](https://mlsanity.com/paper/2305.06355/) (2023)
- [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017)
- [TALL: Temporal Activity Localization via Language Query](https://mlsanity.com/paper/1705.02101/) (2017)
- [TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding](https://mlsanity.com/paper/2312.02051/) (2023)
- [Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language](https://mlsanity.com/paper/1912.03590/) (2019)
- [VTimeLLM: Empower LLM to Grasp Video Moments](https://mlsanity.com/paper/2311.18445/) (2023)
- [UniVTG: Towards Unified Video-Language Temporal Grounding](https://mlsanity.com/paper/2307.16715/) (2023)
- [Local-Global Video-Text Interactions for Temporal Grounding](https://mlsanity.com/paper/2004.07514/) (2020)
- [Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos](https://mlsanity.com/paper/1910.14303/) (2019)
- [GroundingGPT:Language Enhanced Multi-modal Grounding Model](https://mlsanity.com/paper/2401.06071/) (2024)
- [Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding](https://mlsanity.com/paper/2109.04872/) (2021)
- [Deconfounded Video Moment Retrieval with Causal Intervention](https://mlsanity.com/paper/2106.01534/) (2021)
- [Knowing Where to Focus: Event-aware Transformer for Video Grounding](https://mlsanity.com/paper/2308.06947/) (2023)
- [Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning](https://mlsanity.com/paper/2203.13049/) (2022)
- [Weakly Supervised Dense Event Captioning in Videos](https://mlsanity.com/paper/1812.03849/) (2018)
- [Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation](https://mlsanity.com/paper/2103.11264/) (2021)
- [Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in Video](https://mlsanity.com/paper/2001.06680/) (2020)
- [Memory-Guided Semantic Learning Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2201.00454/) (2022)
- [Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation](https://mlsanity.com/paper/2107.11443/) (2021)
- [Zero-shot Natural Language Video Localization](https://mlsanity.com/paper/2110.00428/) (2021)
- [Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training](https://mlsanity.com/paper/2303.00040/) (2023)
- [Training-free Video Temporal Grounding using Large-scale Pre-trained Models](https://mlsanity.com/paper/2408.16219/) (2024)
- [Video Activity Localisation with Uncertainties in Temporal Boundary](https://mlsanity.com/paper/2206.12923/) (2022)
- [Language-free Training for Zero-shot Video Grounding](https://mlsanity.com/paper/2210.12977/) (2022)
- [Zero-Shot Video Moment Retrieval from Frozen Vision-Language Models](https://mlsanity.com/paper/2309.00661/) (2023)
- [VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT](https://mlsanity.com/paper/2403.02076/) (2024)

## Built on by (most cited first, 7 of 7)

- [Training-free Uncertainty Guidance for Complex Visual Tasks with MLLMs](https://mlsanity.com/paper/2510.00705/) (2025)
- [Learning to Refuse: Refusal-Aware Reinforcement Fine-Tuning for Hard-Irrelevant Queries in Video Temporal Grounding](https://mlsanity.com/paper/2511.23151/) (2025)
- [T2SGrid: Temporal-to-Spatial Gridification for Video Temporal Grounding](https://mlsanity.com/paper/2603.06973/) (2026)
- [DART: Difficulty-Adaptive Routing for Zero-Shot Video Temporal Grounding](https://mlsanity.com/paper/2607.00672/) (2026)
- [REZE: Recognition-Based Zero-Shot Extraction for Video Temporal Grounding](https://mlsanity.com/paper/2608.04480/) (2026)
- [Training-Free Temporal Abstraction for General Video Understanding](https://mlsanity.com/paper/2608.27929/) (2026)
- [DSE-VTG: Dual-Side Enhancement for Training-Free Video Temporal Grounding](https://mlsanity.com/paper/2609.08850/) (2026)

## Abstract (arXiv)

Video Temporal Grounding (VTG) aims to extract relevant video segments based on a given natural language query. Recently, zero-shot VTG methods have gained attention by leveraging pretrained vision-language models (VLMs) to localize target moments without additional training. However, existing approaches suffer from semantic fragmentation, where temporally continuous frames sharing the same semantics are split across multiple segments. When segments are fragmented, it becomes difficult to predict an accurate target moment that aligns with the text query. Also, they rely on skewed similarity distributions for localization, making it difficult to select the optimal segment. Furthermore, they heavily depend on the use of LLMs which require expensive inferences. To address these limitations, we propose a textitTAG, a simple yet effective Temporal-Aware approach for zero-shot video temporal Grounding, which incorporates temporal pooling, temporal coherence clustering, and similarity adjustment. Our proposed method effectively captures the temporal context of videos and addresses distorted similarity distributions without training. Our approach achieves state-of-the-art results on Charades-STA and ActivityNet Captions benchmark datasets without rely on LLMs. Our code is available at https://github.com/Nuetee/TAG