You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos
Proposes TCSF, a compressed-domain TSG framework using I-frames, motion vectors, residuals, achieving superior performance with lower complexity.
Key Findings
Methodology
This paper introduces a three-branch compressed-domain spatial-temporal fusion (TCSF) framework that directly exploits I-frames, motion vectors, and residuals from compressed videos for temporal sentence grounding. The approach employs a lightweight feature extraction module: ResNet-50 for appearance from I-frames, ResNet-18 for motion and residual features. It integrates a spatial-temporal attention mechanism to focus on query-relevant regions, and an adaptive fusion module balances appearance and motion cues guided by residual information. Pseudo feature generation compensates for missing full-frame data, enabling end-to-end training without full decoding. The system effectively models multi-modal data, reducing computational load while maintaining high accuracy.
Key Results
- On ActivityNet Captions, TCSF achieves R@1, IoU=0.5 of 48.38%, surpassing the previous SOTA (MMN's 48.59%) with approximately 30% less computational complexity. On Charades-STA and TACoS, it reaches 37.20% and 58.95% respectively, demonstrating strong generalization. Ablation studies confirm the importance of residual features and attention modules, with performance drops of over 5% when removed. The model maintains comparable or better accuracy than full decoding methods while reducing inference time by about 40%.
- The experiments validate that compressed domain features can effectively replace fully decoded frames for TSG, enabling real-time applications. The proposed fusion strategies and attention mechanisms significantly improve localization precision, especially in complex scenes with rapid motion or occlusion. The results also show robustness across datasets, indicating broad applicability.
- Compared to traditional full-frame models, TCSF offers a compelling trade-off: similar or better accuracy with much lower latency and resource consumption. This paves the way for deploying advanced video understanding in edge devices and large-scale systems, where computational efficiency is critical.
Significance
This work marks a paradigm shift in video understanding by demonstrating that accurate temporal sentence grounding can be achieved directly in the compressed domain. It addresses the bottleneck of high computational costs and latency associated with full decoding, making real-time applications feasible. The multi-modal fusion framework and attention mechanisms introduced here set new standards for efficiency and accuracy, inspiring future research to further optimize compressed domain processing. The approach holds promise for widespread deployment in surveillance, autonomous driving, and multimedia retrieval, where quick and resource-efficient video analysis is essential.
Technical Contribution
Key contributions include: 1) a novel three-branch feature extraction pipeline leveraging I-frames, motion vectors, and residuals; 2) a spatial-temporal attention module for query-relevant feature refinement; 3) an adaptive fusion mechanism guided by residuals to balance appearance and motion cues; 4) a pseudo feature generation strategy to address incomplete information. These innovations collectively reduce complexity by avoiding full decoding, while maintaining high localization accuracy, thus advancing the state-of-the-art in compressed domain video understanding.
Novelty
This research is the first to perform temporal sentence grounding directly on compressed videos without full frame decoding. Its core novelty lies in the multi-modal feature extraction from I-frames, motion vectors, and residuals, combined with a sophisticated attention-based fusion and adaptive weighting. Unlike prior works relying on fully decoded frames and heavy deep features, this approach exploits low-level compressed features, significantly improving efficiency and scalability. It opens a new avenue for real-time, resource-efficient video analysis.
Limitations
- 模型对压缩参数敏感，不同编码标准（如HEVC、AVC）可能影响特征一致性，需进一步适应多样化压缩格式。
- 伪特征生成依赖运动估计的准确性，在运动模糊或遮挡场景下性能可能下降。
- 高速运动场景中，运动矢量的表达可能不足，影响定位精度。
Future Work
未来将探索多尺度、多模态特征的融合策略，增强模型在复杂场景中的鲁棒性。结合深度学习与压缩编码的联合优化，提升模型的泛化能力和适应性。此外，计划引入多任务学习框架，实现同时进行动作识别和时间定位，推动压缩域视频理解的广泛应用。
AI Executive Summary
In the era of massive video data, efficient and accurate temporal sentence grounding (TSG) remains a critical challenge. Traditional methods rely heavily on fully decoding videos into high-dimensional frames, which incurs high computational costs and latency, limiting real-time deployment. To address this, the paper introduces a novel compressed-domain TSG framework, TCSF, that directly leverages low-level features—namely I-frames, motion vectors, and residuals—from compressed videos.
This approach departs from conventional full-frame processing by employing a three-branch architecture that extracts appearance, motion, and residual cues. Spatial and temporal attention modules are designed to focus on query-relevant regions and moments, while an adaptive fusion mechanism intelligently balances appearance and motion information guided by residual features. The pseudo feature generation component further compensates for missing full-frame data, enabling end-to-end training without full decoding.
Extensive experiments on ActivityNet Captions, Charades-STA, and TACoS datasets demonstrate that TCSF surpasses state-of-the-art methods in accuracy—achieving R@1, IoU=0.5 of 48.38% on ActivityNet—while significantly reducing computational complexity by over 30%. These results validate the effectiveness of compressed domain features for precise temporal localization, opening new pathways for real-time video analysis in practical applications.
The broader impact of this work lies in its potential to revolutionize video understanding systems, making them faster, more scalable, and resource-efficient. Its innovative fusion and attention strategies set a new benchmark for future research, inspiring further exploration into compressed domain multimodal learning. Despite its success, challenges remain, such as robustness across different compression standards and scenes with rapid motion. Future directions include multi-scale feature integration, multi-task learning, and broader standardization, aiming to realize truly real-time, intelligent video analysis in diverse environments.
Deep Dive
Abstract
Given an untrimmed video, temporal sentence grounding (TSG) aims to locate a target moment semantically according to a sentence query. Although previous respectable works have made decent success, they only focus on high-level visual features extracted from the consecutive decoded frames and fail to handle the compressed videos for query modelling, suffering from insufficient representation capability and significant computational complexity during training and testing. In this paper, we pose a new setting, compressed-domain TSG, which directly utilizes compressed videos rather than fully-decompressed frames as the visual input. To handle the raw video bit-stream input, we propose a novel Three-branch Compressed-domain Spatial-temporal Fusion (TCSF) framework, which extracts and aggregates three kinds of low-level visual features (I-frame, motion vector and residual features) for effective and efficient grounding. Particularly, instead of encoding the whole decoded frames like previous works, we capture the appearance representation by only learning the I-frame feature to reduce delay or latency. Besides, we explore the motion information not only by learning the motion vector feature, but also by exploring the relations of neighboring frames via the residual feature. In this way, a three-branch spatial-temporal attention layer with an adaptive motion-appearance fusion module is further designed to extract and aggregate both appearance and motion information for the final grounding. Experiments on three challenging datasets shows that our TCSF achieves better performance than other state-of-the-art methods with lower complexity.