The DOI matches its Crossref record; 51 of the 53 references it cites were found in a registry or an index.
Flow-Guided Feature Aggregation for Video Object Detection
Open accessAbstract
Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong singleframe baselines in ImageNet VID [33], especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The code would be released.
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“Indeed, overlooking these factors results in a decrease in the object detection performance in videos.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [31]
Citations
Cites
Deep Residual Learning for Image Recognition
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“The ResNet-50, ResNet-101, and the Aligned-Inception-ResNet models are all pre-trained on ImageNet classification.”
BackgroundMethodInfluentialVery Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 3×
“State-of-the-art methods for general object detection [10, 30, 26, 5] are mainly based on deep CNNs [22, 36, 40, 14].”
BackgroundMethodImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · cited 3×
“State-of-the-art methods for general object detection [10, 30, 26, 5] are mainly based on deep CNNs [22, 36, 40, 14].”
BackgroundMethodFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472 · cited 5×
“In [11], a multistage pipeline called Regions with Convolutional Neural Networks (R-CNN) is proposed for training deep CNN to classify region proposals for object detection.”
BackgroundMethodInfluentialFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · cited 3×
“State-of-the-art methods for general object detection [10, 30, 26, 5] are mainly based on deep CNNs [22, 36, 40, 14].”
BackgroundMethodImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · cited 12×
“Our approach is evaluated on the large-scale ImageNet VID dataset [33].”
MethodInfluential(2014) · Cited by 33,419
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · cited 3×
“Recent years have witnessed significant progress in object detection [11].”
BackgroundMethod(2016) · Cited by 31,590
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2 of the 53 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- yang, 2016, Efficient object detection from videos
- tran, 2016, Deep end2end voxe12voxel prediction, CVPR Workshop
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ViewAdapt-Det: view-adaptive detection for soccer broadcasting videos
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ROI-SSD: Region of Interest Single Shot Detector
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Latency-Aware Task-Oriented Multi-Drone Communication for Small Object Detection
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MotionDETR: Motion-aware Detection Transformer for sports video object detection
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Reliable Tiny-Airborne-Object Detection via Prior-Guided Motion-Structure Verification
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Multi-graph Cross Diffusion Attention Network for Alignment-free RGBT Video Object Detection
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FG-DINO: Inducing Foreground Approximation Into DINO for Low-Resolution Surveillance Videos
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Metadata
- Authors
- Zhu, X., Wang, Y., Dai, J., Yuan, L., & Wei, Y.
- Affiliations
- University of Science and Technology of China (CN)
- Microsoft (United States) (US)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 408-417
- OpenAlex
- W2964286567
- Cited by
- 729
- References
- 53
- Citations by year
- 2026: 48 · 2025: 57 · 2024: 86 · 2023: 91 · 2022: 102 · 2021: 123 · 2020: 96 · 2019: 75 · 2018: 47 · 2017: 4
- FWCI
- 17.47
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- video object detection, optical flow, temporal feature aggregation, movement paths, end-to-end learning, motion blur, fast-moving objects
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