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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]
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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
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“State-of-the-art methods for general object detection [10, 30, 26, 5] are mainly based on deep CNNs [22, 36, 40, 14].”
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“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
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“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
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Going deeper with convolutions
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“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×
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“Recent years have witnessed significant progress in object detection [11].”
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- yang, 2016, Efficient object detection from videos
- tran, 2016, Deep end2end voxe12voxel prediction, CVPR Workshop
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Short-term anchor linking and long-term self-guided attention for video object detection
Cores, D., Brea, V. M., & Mucientes, M. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104179 · Cited by 14
Tracking Assisted Faster Video Object Detection
Yang, W., Liu, B., Li, W., & Yu, N. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00301 · Cited by 13
Spatio-temporal Prompting Network for Robust Video Feature Extraction
Sun, G., Wang, C., Zhang, Z., Deng, J., Zafeiriou, S. P., & Yang, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01250 · Cited by 14
Context Enhanced Transformer for Single Image Object Detection in Video Data
An, S.-J., Park, S., Kim, G., Baek, J., Lee, B., & Kim, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27825 · Cited by 15
Li, X., Ai, D., Song, H., Fan, J., Fu, T., Xiao, D., Wang, Y., & Yang, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3430839 · Cited by 13
Self-citationUAV Video Vehicle Detection: Benchmark and Baseline
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YONA: You Only Need One Adjacent Reference-Frame for Accurate and Fast Video Polyp Detection
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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