# Fine-grained Iterative Attention Network for TemporalLanguage Localization in Videos

arXiv: https://arxiv.org/abs/2008.02448 · HTML: https://mlsanity.com/paper/2008.02448/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 30 of 46)

- [Context-aware Biaffine Localizing Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2103.11555/) (2021)
- [Knowing Where to Focus: Event-aware Transformer for Video Grounding](https://mlsanity.com/paper/2308.06947/) (2023)
- [Natural Language Video Localization: A Revisit in Span-based Question Answering Framework](https://mlsanity.com/paper/2102.13558/) (2021)
- [Memory-Guided Semantic Learning Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2201.00454/) (2022)
- [Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video](https://mlsanity.com/paper/2203.06667/) (2022)
- [Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos](https://mlsanity.com/paper/2109.06398/) (2021)
- [MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer](https://mlsanity.com/paper/2305.00355/) (2023)
- [Hierarchical Local-Global Transformer for Temporal Sentence Grounding](https://mlsanity.com/paper/2208.14882/) (2022)
- [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)
- [Reducing the Vision and Language Bias for Temporal Sentence Grounding](https://mlsanity.com/paper/2207.13457/) (2022)
- [CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval](https://mlsanity.com/paper/2109.10016/) (2021)
- [Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding](https://mlsanity.com/paper/2109.06400/) (2021)
- [Unsupervised Temporal Video Grounding with Deep Semantic Clustering](https://mlsanity.com/paper/2201.05307/) (2022)
- [A Survey on Temporal Sentence Grounding in Videos](https://mlsanity.com/paper/2109.08039/) (2021)
- [Multi-Attention Network for Compressed Video Referring Object Segmentation](https://mlsanity.com/paper/2207.12622/) (2022)
- [Exploring Motion and Appearance Information for Temporal Sentence Grounding](https://mlsanity.com/paper/2201.00457/) (2022)
- [Scanning Only Once: An End-to-end Framework for Fast Temporal Grounding in Long Videos](https://mlsanity.com/paper/2303.08345/) (2023)
- [Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding](https://mlsanity.com/paper/2311.03106/) (2023)
- [Video Moment Retrieval from Text Queries via Single Frame Annotation](https://mlsanity.com/paper/2204.09409/) (2022)
- [Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding](https://mlsanity.com/paper/2301.00514/) (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-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network](https://mlsanity.com/paper/2412.15678/) (2024)
- [You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos](https://mlsanity.com/paper/2205.12886/) (2022)
- [Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning](https://mlsanity.com/paper/2104.02429/) (2021)
- [Entity-aware and Motion-aware Transformers for Language-driven Action Localization in Videos](https://mlsanity.com/paper/2205.05854/) (2022)
- [Moment of Untruth: Dealing with Negative Queries in Video Moment Retrieval](https://mlsanity.com/paper/2502.08544/) (2025)
- [Context-Enhanced Video Moment Retrieval with Large Language Models](https://mlsanity.com/paper/2405.12540/) (2024)
- [Temporal Sentence Grounding in Streaming Videos](https://mlsanity.com/paper/2308.07102/) (2023)

## Abstract (arXiv)

Temporal language localization in videos aims to ground one video segment in an untrimmed video based on a given sentence query. To tackle this task, designing an effective model to extract ground-ing information from both visual and textual modalities is crucial. However, most previous attempts in this field only focus on unidirectional interactions from video to query, which emphasizes which words to listen and attends to sentence information via vanilla soft attention, but clues from query-by-video interactions implying where to look are not taken into consideration. In this paper, we propose a Fine-grained Iterative Attention Network (FIAN) that consists of an iterative attention module for bilateral query-video in-formation extraction. Specifically, in the iterative attention module, each word in the query is first enhanced by attending to each frame in the video through fine-grained attention, then video iteratively attends to the integrated query. Finally, both video and query information is utilized to provide robust cross-modal representation for further moment localization. In addition, to better predict the target segment, we propose a content-oriented localization strategy instead of applying recent anchor-based localization. We evaluate the proposed method on three challenging public benchmarks: Ac-tivityNet Captions, TACoS, and Charades-STA. FIAN significantly outperforms the state-of-the-art approaches.