# Tripping through time: Efficient Localization of Activities in Videos

arXiv: https://arxiv.org/abs/1904.09936 · HTML: https://mlsanity.com/paper/1904.09936/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2019.

## Built on by (most cited first, 30 of 33)

- [Interventional Video Grounding with Dual Contrastive Learning](https://mlsanity.com/paper/2106.11013/) (2021)
- [Video Corpus Moment Retrieval with Contrastive Learning](https://mlsanity.com/paper/2105.06247/) (2021)
- [TR-DETR: Task-Reciprocal Transformer for Joint Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2401.02309/) (2024)
- [End-to-end Multi-modal Video Temporal Grounding](https://mlsanity.com/paper/2107.05624/) (2021)
- [A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metric](https://mlsanity.com/paper/2101.09028/) (2021)
- [Temporal Sentence Grounding in Videos: A Survey and Future Directions](https://mlsanity.com/paper/2201.08071/) (2022)
- [Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding](https://mlsanity.com/paper/2203.02966/) (2022)
- [A Survey on Temporal Sentence Grounding in Videos](https://mlsanity.com/paper/2109.08039/) (2021)
- [Support-Set Based Cross-Supervision for Video Grounding](https://mlsanity.com/paper/2108.10576/) (2021)
- [FlashVTG: Feature Layering and Adaptive Score Handling Network for Video Temporal Grounding](https://mlsanity.com/paper/2412.13441/) (2024)
- [Parallel Attention Network with Sequence Matching for Video Grounding](https://mlsanity.com/paper/2105.08481/) (2021)
- [ViGT: Proposal-free Video Grounding with Learnable Token in Transformer](https://mlsanity.com/paper/2308.06009/) (2023)
- [Skimming, Locating, then Perusing: A Human-Like Framework for Natural Language Video Localization](https://mlsanity.com/paper/2207.13450/) (2022)
- [G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory](https://mlsanity.com/paper/2307.14277/) (2023)
- [Text-Visual Prompting for Efficient 2D Temporal Video Grounding](https://mlsanity.com/paper/2303.04995/) (2023)
- [A Survey on Video Moment Localization](https://mlsanity.com/paper/2306.07515/) (2023)
- [Progressive Localization Networks for Language-based Moment Localization](https://mlsanity.com/paper/2102.01282/) (2021)
- [Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos](https://mlsanity.com/paper/2110.06058/) (2021)
- [Towards Debiasing Temporal Sentence Grounding in Video](https://mlsanity.com/paper/2111.04321/) (2021)
- [A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach](https://mlsanity.com/paper/2203.05243/) (2022)
- [Entity-aware and Motion-aware Transformers for Language-driven Action Localization in Videos](https://mlsanity.com/paper/2205.05854/) (2022)
- [Boundary-Denoising for Video Activity Localization](https://mlsanity.com/paper/2304.02934/) (2023)
- [A Survey on Natural Language Video Localization](https://mlsanity.com/paper/2104.00234/) (2021)
- [DeCafNet: Delegate and Conquer for Efficient Temporal Grounding in Long Videos](https://mlsanity.com/paper/2505.16376/) (2025)
- [Hierarchical Deep Residual Reasoning for Temporal Moment Localization](https://mlsanity.com/paper/2111.00417/) (2021)
- [Long-VMNet: Accelerating Long-Form Video Understanding via Fixed Memory](https://mlsanity.com/paper/2503.13707/) (2025)
- [Learning Commonsense-aware Moment-Text Alignment for Fast Video Temporal Grounding](https://mlsanity.com/paper/2204.01450/) (2022)
- [LocFormer: Enabling Transformers to Perform Temporal Moment Localization on Long Untrimmed Videos With a Feature Sampling Approach](https://mlsanity.com/paper/2112.10066/) (2021)
- [Position-aware Location Regression Network for Temporal Video Grounding](https://mlsanity.com/paper/2204.05499/) (2022)
- [DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2308.15109/) (2023)

## Abstract (arXiv)

Localizing moments in untrimmed videos via language queries is a new and interesting task that requires the ability to accurately ground language into video. Previous works have approached this task by processing the entire video, often more than once, to localize relevant activities. In the real world applications of this approach, such as video surveillance, efficiency is a key system requirement. In this paper, we present TripNet, an end-to-end system that uses a gated attention architecture to model fine-grained textual and visual representations in order to align text and video content. Furthermore, TripNet uses reinforcement learning to efficiently localize relevant activity clips in long videos, by learning how to intelligently skip around the video. It extracts visual features for few frames to perform activity classification. In our evaluation over Charades-STA, ActivityNet Captions and the TACoS dataset, we find that TripNet achieves high accuracy and saves processing time by only looking at 32-41% of the entire video.