Source-linked AI summary
To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression
Yitian Yuan, Tao Mei, Wenwu Zhu
TL;DR
Temporal sentence localization must identify a sentence-described segment within an untrimmed video while accounting for both modalities. ABLR uses contextual encoding, co-attention, and direct coordinate regression from a global video perspective, achieving superior accuracy and efficiency on two datasets. The paper also identifies multiple-sentence localization and joint sentence localization in videos and sentences as further problems.
Problem
Temporal sentence localization requires identifying sentence-aligned start and end points, while scan-and-localize methods neglect global video context and important sentence details and require costly dense sampling.
Method
ABLR encodes video and sentence sequences with bidirectional LSTMs, uses multi-modal co-attention, and directly regresses temporal coordinates from global attention outputs.
Results
ABLR achieves superior localization accuracy and efficiency on ActivityNet Captions and TACoS compared with existing approaches.
Takeaways & Limitations
Global video attention and sentence-detail modeling provide the basis for an end-to-end temporal localization architecture that avoids scan-and-localize post-processing.
Takeaways & Limitations
The paper identifies temporal localization of multiple sentences and sentence localization in both tem- as further problems.
Abstract
from arXiv · showhide
Given an untrimmed video and a sentence description, temporal sentence localization aims to automatically determine the start and end points of the described sentence within the video. The problem is challenging as it needs the understanding of both video and sentence. Existing research predominantly employs a costly "scan and localize" framework, neglecting the global video context and the specific details within sentences which play as critical issues for this problem. In this paper, we propose a novel Attention Based Location Regression (ABLR) approach to solve the temporal sentence localization from a global perspective. Specifically, to preserve the context information, ABLR first encodes both video and sentence via Bidirectional LSTM networks. Then, a multi-modal co-attention mechanism is introduced to generate not only video attention which reflects the global video structure, but also sentence attention which highlights the crucial details for temporal localization. Finally, a novel attention based location regression network is designed to predict the temporal coordinates of sentence query from the previous attention. ABLR is jointly trained in an end-to-end manner. Comprehensive experiments on ActivityNet Captions and TACoS datasets demonstrate both the effectiveness and the efficiency of the proposed ABLR approach.
Introduction
Temporal sentence localization identifies the start and end points of a described segment in an untrimmed video, requiring joint video and language understanding. ABLR addresses limitations of scan-and-localize methods by using global context, sentence details, and direct coordinate regression.
- Temporal sentence localization identifies the start and end points of the video segment corresponding to a sentence query.
- Scan-and-localize methods can disrupt global temporal structure, underuse sentence details, and incur high computational cost through dense sliding-window sampling.
- Bidirectional LSTMs encode contextual video and sentence features before multi-modal co-attention highlights informative video parts and crucial sentence details.
- ABLR predicts sentence coordinates from the overall video sequence rather than independently matching densely sampled candidate clips.
- Experiments on ActivityNet Captions and TACoS report superior localization accuracy and improved efficiency over existing approaches.
Related Work
Related work covers temporal action localization and temporal sentence localization. The paper targets general open-world videos with independent sentence queries, contrasting its global end-to-end regression approach with prior local scan-and-localize models.
- Temporal action localization determines temporal boundaries for actions but is limited by predefined action categories that cannot precisely represent complex video activities.
- Earlier sentence-localization methods often target restricted visual domains or chronologically ordered multiple sentences within one video.
- For independent sentence queries in open-world videos, existing approaches predominantly scan candidate clips and match each clip with the sentence.
- ABLR differs by regressing target coordinates from a global video view and supporting end-to-end training.
Attention Based Location Regression Model
ABLR localizes a sentence directly within the overall video by preserving video and sentence context, using co-attention to focus both modalities, and regressing temporal coordinates end to end.
- Contextual Incorporated Feature Encoding: ABLR encodes video and sentence sequences with contextual representations before predicting the sentence segment’s temporal coordinates.Video clips are encoded with C3D features and Bi-directional LSTM context; sentence words are represented similarly.
- Attention Based Location Regression Model: ABLR jointly optimizes its end-to-end pipeline from video and sentence inputs to temporal-coordinate outputs.The architecture combines contextual encoding, co-attention interaction, and coordinate prediction in one jointly optimized model.
- Multi-Modal Co-Attention Interaction: Multi-modal co-attention alternates video and sentence attention to identify relevant video content and crucial sentence words or phrases.The mechanism attends to video from sentence features, then sentence from attended video, and finally video again from attended sentence features.
- Multi-Modal Co-Attention Interaction: Video attention weights represent the global temporal structure while emphasizing clips associated with the sentence description.Each attention element reflects the relative association between one video clip and the sentence.
- Attention Based Coordinates Prediction: The location prediction network directly regresses normalized start and end coordinates from video attention weights or attended features.ABLR provides attention-weight-based and attended-feature-based regression strategies instead of relying on post-processing to select or merge clips.
- Learning of ABLR: The model normalizes ground-truth start and end times by video duration and optimizes coordinate prediction with a smooth L1 attention regression loss.A training sample contains a video, its duration, a sentence description, and the segment’s start and end points.
Experiments
Experiments on ActivityNet Captions and TACoS compare ABLR with existing and ablated approaches for localization accuracy, interpretability, and efficiency. ABLR generally performs better and localizes substantially faster, while dataset characteristics affect high-IoU performance.
- Experimental setup: Experiments evaluate ABLR and baseline methods on ActivityNet Captions and TACoS using R@1, IoU@σ and mIoU.The datasets provide timestamp-aligned sentence-video pairs, with ActivityNet Captions containing 20k videos and 100k descriptions and TACoS containing approximately 17,000 pairs.
- ActivityNet Captions: On ActivityNet Captions, ABLR consistently outperforms competing methods, with ABLRfull−aw improving mIoU over ACRN by 53.1%.The comparison includes MCN, CTRL, and ACRN, while the authors attribute weaker baseline performance to limited treatment of global temporal structure.
- ActivityNet Captions: Qualitative examples show that ABLR uses global video attention and sentence attention to distinguish repeated scenes and highlight objects, actions, and temporal cues.For example, the phrase “in the end” helps select the later occurrence of a speaking scene.
- Ablation study: Ablations show that video context, sentence attention, attention calibration, and attention-based regression each contribute to localization performance.Relative improvements include 6.4% from contextual video encoding and up to 18.9% from sentence attention on R@1,IoU@0.5.
- TACoS: On TACoS, ABLRfull−af improves over ACRN by 43.4% at IoU@0.1 and 2.6% at IoU@0.3, but trails ACRN at IoU@0.5.The authors relate this pattern to TACoS videos sharing similar cooking scenes, which makes precise boundaries harder to distinguish.
- Efficiency analysis: ABLR reduces localization time by a factor of 4–15 on ActivityNet Captions compared with MCN, CTRL, and ACRN, with a larger advantage on longer TACoS videos.The efficiency gain is attributed to encoding each video twice instead of densely processing overlapping candidate clips.
Conclusions and Future Work
ABLR is an end-to-end architecture for temporal sentence localization that learns global video and sentence attention and directly regresses temporal coordinates. The paper reports superior accuracy and efficiency on two datasets, while identifying broader multi-sentence and spatiotemporal localization as future work.
- Conclusions: ABLR combines multi-modal co-attention with direct temporal-coordinate regression for globally optimized localization in untrimmed videos.The model learns video attention reflecting global temporal structure and sentence attention highlighting crucial localization details, avoiding trivial post-processing.
- Conclusions: ABLR achieves superior localization accuracy on ActivityNet Captions and TACoS while significantly improving localization efficiency.
- Future Work: Future work includes localizing multiple sentences and extending sentence localization to both temporal and spatial dimensions.