# REZE: Recognition-Based Zero-Shot Extraction for Video Temporal Grounding

arXiv: https://arxiv.org/abs/2608.04480 · HTML: https://mlsanity.com/paper/2608.04480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 42 references that are in the map.
- First posted on arXiv 5 Aug 2026.

## Builds on (most cited first, 27 of 27)

- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [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)
- [Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding](https://mlsanity.com/paper/1604.01753/) (2016)
- [Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language](https://mlsanity.com/paper/1912.03590/) (2019)
- [Local-Global Video-Text Interactions for Temporal Grounding](https://mlsanity.com/paper/2004.07514/) (2020)
- [Dense Regression Network for Video Grounding](https://mlsanity.com/paper/2004.03545/) (2020)
- [Query-Dependent Video Representation for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2303.13874/) (2023)
- [Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding](https://mlsanity.com/paper/2503.13377/) (2025)
- [TRACE: Temporal Grounding Video LLM via Causal Event Modeling](https://mlsanity.com/paper/2410.05643/) (2024)
- [QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries](https://mlsanity.com/paper/2107.09609/) (2021)
- [Number it: Temporal Grounding Videos like Flipping Manga](https://mlsanity.com/paper/2411.10332/) (2024)
- [Uncovering Hidden Challenges in Query-Based Video Moment Retrieval](https://mlsanity.com/paper/2009.00325/) (2020)
- [Test of Time: Instilling Video-Language Models with a Sense of Time](https://mlsanity.com/paper/2301.02074/) (2023)
- [Correlation-Guided Query-Dependency Calibration for Video Temporal Grounding](https://mlsanity.com/paper/2311.08835/) (2023)
- [Saliency-Guided DETR for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2410.01615/) (2024)
- [On the Consistency of Video Large Language Models in Temporal Comprehension](https://mlsanity.com/paper/2411.12951/) (2024)
- [TAG: A Simple Yet Effective Temporal-Aware Approach for Zero-Shot Video Temporal Grounding](https://mlsanity.com/paper/2508.07925/) (2025)
- [GranAlign: Granularity-Aware Alignment Framework for Zero-Shot Video Moment Retrieval](https://mlsanity.com/paper/2601.00584/) (2026)
- [CVA: Context-aware Video-text Alignment for Video Temporal Grounding](https://mlsanity.com/paper/2603.24934/) (2026)
- [Measure Twice, Cut Once: A Semantic-Oriented Approach to Video Temporal Localization with Video LLMs](https://mlsanity.com/paper/2503.09027/) (2025)
- [Sparse-Dense Side-Tuner for efficient Video Temporal Grounding](https://mlsanity.com/paper/2507.07744/) (2025)
- [CoSTL: Comprehensive Spatial-Temporal Representation Learning for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2606.01149/) (2026)

## Abstract (arXiv)

Video temporal grounding (VTG) refers to the task of identifying the time interval in a video that corresponds to a given natural-language query. A common zero-shot strategy asks a large vision-language model (VLM) to generate the start and end timestamps directly, so the result depends heavily on the design and training of the model, and grounding accuracy differs widely from one VLM to another. We therefore propose REcognition-based Zero-shot Extraction (REZE), a simple training-free method that splits the video into short clips, asks the model for a clip-level confidence score for the query, and uses a deterministic algorithm to convert the resulting score curve into the output required by the task. Because temporal aggregation is performed outside the model, REZE adapts to different task outputs, from single- and multi-interval moment retrieval to highlight detection. On QVHighlights, REZE improves the best reported training-free moment-retrieval mAP from 38.23 to 40.32, while on highlight detection it reaches 44.18 mAP and 73.41 HIT@1, establishing a new state of the art among training-free methods. Its HIT@1 also outperforms all fully supervised SoTAs on the QVHighlights test split. We evaluate REZE on seven backbones from three model families. On Charades-STA and QVHighlights, it outperforms direct timestamp generation in every available comparison. We further observe that with REZE an earlier-generation model can approach the native performance of a newer model in its family.