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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 11×
“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
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971
Horn, B. K. P., & Schunck, B. G. (1981) · Artificial Intelligence · doi:10.1016/0004-3702(81)90024-2 · Cited by 10,312
Learning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593 · cited 2×
“On the other hand, exhaustive spatial-temporal convolution is used to directly extract spatial-temporal features [38, 21, 41, 42].”
MethodDeformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513 · cited 1×
“To resolve feature misalignment issue and make it proper for object detection, We utilize a modified version dubbed as “Aligned-Inception-ResNet”, which is described in [6].”
MethodSelf-citationHorn, B. K. P., & Schunck, B. G. (1981) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.965761 · Cited by 7,443 · cited 1×
“Traditional methods are dominated by variational approaches [2, 15], which mainly address small displacements [43].”
MethodLarge-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354 · cited 2×
“On the other hand, exhaustive spatial-temporal convolution is used to directly extract spatial-temporal features [38, 21, 41, 42].”
MethodLong-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228
R-FCN: Object Detection via Region-based Fully Convolutional NetworksPreprint
Dai, J., Li, Y., He, K., & Sun, J. Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06409 · Cited by 5,135 · cited 9×
“To increase the feature resolution, following the practice in [4, 5], the effective stride of the last block is changed from 32 to 16.”
BackgroundMethodInfluentialSelf-citationInception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · cited 1×
“We adopt the state-of-the-art ResNet (-50 and -101) [14] and Inception-Resnet [39] as the feature network.”
MethodFlownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313 · cited 8×
“It takes the feature and the detection sub-networks from the single-frame baseline (a), and the pre-trained off-the-shelf FlowNet.”
BackgroundMethodInfluentialSemantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213 · cited 1×
“To increase the feature resolution, following the practice in [4, 5], the effective stride of the last block is changed from 32 to 16.”
MethodFlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., & Brox, T. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.179 · Cited by 3,177
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_23 · Cited by 3,034 · cited 2×
“A shallow detection-specific network [13, 10, 30, 26, 5] then generates the detection results from the feature maps.”
MethodA Neural Attention Model for Abstractive Sentence Summarization
Rush, A. M., Chopra, S., & Weston, J. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1044 · Cited by 2,932 · cited 1×
“Equation (2) is similar to the formula of attention models [32], where varying weights are assigned to the features in the memory buffer.”
BackgroundLearning Multi-domain Convolutional Neural Networks for Visual Tracking
Nam, H., & Han, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.465 · Cited by 2,806 · cited 1×
“Recently, deep CNNs have been used for object tracking [25, 16] and achieved impressive tracking accuracy.”
MethodHigh Accuracy Optical Flow Estimation Based on a Theory for Warping
Brox, T., Bruhn, A., Papenberg, N., & Weickert, J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24673-2_3 · Cited by 2,634 · cited 1×
“Traditional methods are dominated by variational approaches [2, 15], which mainly address small displacements [43].”
MethodBeyond short snippets: Deep networks for video classification
Ng, J. Y.-H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., & Toderici, G. D. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299101 · Cited by 2,152 · cited 2×
“On one hand, most of these work [34, 24, 47, 7, 46, 1, 9, 35] use recurrent neural network (RNNs) to aggregate features from consecutive frames.”
BackgroundMethodOptical Flow Estimation Using a Spatial Pyramid Network
Ranjan, A., & Black, M. J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.291 · Cited by 1,379 · cited 1×
“, FlowNet [8] and its successors [28, 17]) have been exploited for optical flow recently.”
BackgroundLarge Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
Brox, T., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.143 · Cited by 1,295 · cited 1×
“The recent focus is on large displacements [3], and combinatorial matching (e.”
BackgroundDeepFlow: Large Displacement Optical Flow with Deep Matching
Weinzaepfel, P., Revaud, J., Harchaoui, Z., & Schmid, C. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.175 · Cited by 1,057
Describing Videos by Exploiting Temporal Structure
Yao, L., Torabi, A., Cho, K., Ballas, N., Pal, C., Larochelle, H., & Courville, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.512 · Cited by 1,035 · cited 2×
“On one hand, most of these work [34, 24, 47, 7, 46, 1, 9, 35] use recurrent neural network (RNNs) to aggregate features from consecutive frames.”
BackgroundMethodVisual Tracking with Fully Convolutional Networks
Wang, L., Ouyang, W., Wang, X., & Lu, H. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.357 · Cited by 1,012 · cited 1×
“Recently, deep CNNs have been used for object tracking [25, 16] and achieved impressive tracking accuracy.”
MethodDelving Deeper into Convolutional Networks for Learning Video RepresentationsPreprint
Ballas, N., Yao, L., Pal, C., & Courville, A. C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06432 · Cited by 767 · cited 2×
“On one hand, most of these work [34, 24, 47, 7, 46, 1, 9, 35] use recurrent neural network (RNNs) to aggregate features from consecutive frames.”
BackgroundMethodEpicFlow: Edge-preserving interpolation of correspondences for optical flow
Revaud, J., Weinzaepfel, P., Harchaoui, Z., & Schmid, C. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298720 · Cited by 757 · cited 2×
“, DeepFlow [44], EpicFlow [31]) has been integrated into the variational approach.”
MethodDeep Feature Flow for Video Recognition
Zhu, X., Xiong, Y., Dai, J., Yuan, L., & Wei, Y. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.441 · Cited by 695 · cited 4×
“As motivated by [49], given a reference frame Ii and a neighbor frame Ij , a flow field Mi→j = F(Ii, Ij) is estimated by a flow network F (e.”
BackgroundMethodInfluentialSelf-citationAction Recognition using Visual AttentionPreprint
Sharma, S., Kiros, R., & Salakhutdinov, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04119 · Cited by 623 · cited 2×
“On one hand, most of these work [34, 24, 47, 7, 46, 1, 9, 35] use recurrent neural network (RNNs) to aggregate features from consecutive frames.”
BackgroundMethodYouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video
Real, E., Shlens, J., Mazzocchi, S., Pan, X., & Vanhoucke, V. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.789 · Cited by 589
Human Action Recognition Using Factorized Spatio-Temporal Convolutional Networks
Sun, L., Jia, K., Yeung, D., & Shi, B. E. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.522 · Cited by 585 · cited 2×
“On the other hand, exhaustive spatial-temporal convolution is used to directly extract spatial-temporal features [38, 21, 41, 42].”
MethodT-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos
Kang, K., Li, H., Yan, J., Zeng, X., Yang, B., Xiao, T., Zhang, C., Wang, Z., Wang, R., Wang, X., et al. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2736553 · Cited by 554 · cited 8×
“Following the protocols in [18, 23], model training and evaluation are performed on the 3,862 video snippets from the training set and the 555 snippets from the validation set, respectively.”
BackgroundMethodInfluentialVideoLSTM convolves, attends and flows for action recognition
Li, Z., Gavrilyuk, K., Gavves, E., Jain, M., & Snoek, C. G. M. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.10.011 · Cited by 492 · cited 2×
“On one hand, most of these work [34, 24, 47, 7, 46, 1, 9, 35] use recurrent neural network (RNNs) to aggregate features from consecutive frames.”
BackgroundMethodObject Detection from Video Tubelets with Convolutional Neural Networks
Kang, K., Ouyang, W., Li, H., & Wang, X. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.95 · Cited by 386 · cited 2×
“Such temporal information is exploited in existing video object detection methods [18, 19, 12, 23] in a simple way.”
BackgroundMethodSeq-NMS for Video Object DetectionPreprint
Han, W., Khorrami, P., Paine, T. L., Ramachandran, P., Babaeizadeh, M., Shi, H., Li, J., Yan, S., & Huang, T. S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.08465 · Cited by 305 · cited 9×
“In particular, we tested three prevalent techniques, namely, motion guided propagation (MGP) [18], Tubelet rescoring [18], and Seq-NMS [12].”
BackgroundMethodInfluentialCosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks
Luo, C., Zhan, J., Xue, X., Wang, L., Ren, R., & Yang, Q. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01418-6_38 · Cited by 239 · cited 1×
“Here, we use the cosine similarity metric [27] to measure the similarity between the warped features and the features extracted from the reference frame.”
MethodKar, A., Rai, N., Sikka, K., & Sharma, G. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.604 · Cited by 172 · cited 1×
“Feature aggregation is widely used in action recognition [34, 20, 24, 47, 38, 1, 21, 41] and video description [7, 46].”
MethodMulti-class Multi-object Tracking Using Changing Point Detection
Lee, B., Erdenee, E., Jin, S., Nam, M. Y., Jung, Y. G., & Rhee, P. K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_6 · Cited by 151 · cited 4×
“Following the protocols in [18, 23], model training and evaluation are performed on the 3,862 video snippets from the training set and the 555 snippets from the validation set, respectively.”
BackgroundMethodInfluentialConvolutional gated recurrent networks for video segmentation
Siam, M., Valipour, S., Jägersand, M., & Ray, N. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296851 · Cited by 110 · cited 1×
“On one hand, most of these work [34, 24, 47, 7, 46, 1, 9, 35] use recurrent neural network (RNNs) to aggregate features from consecutive frames.”
BackgroundDeep End2End Voxel2Voxel Prediction
Tran, D., Bourdev, L. D., Fergus, R., Torresani, L., & Paluri, M. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.57 · Cited by 108 · cited 1×
“On the other hand, exhaustive spatial-temporal convolution is used to directly extract spatial-temporal features [38, 21, 41, 42].”
MethodA Survey on Variational Optic Flow Methods for Small Displacements
Weickert, J., Bruhn, A., Brox, T., et al. (2006) · Mathematics in industry · doi:10.1007/978-3-540-34767-5_5 · Cited by 91
STFCN: Spatio-Temporal FCN for Semantic Video SegmentationPreprint
Fayyaz, M., Saffar, M. H., Sabokrou, M., Fathy, M., Klette, R., & Huang, F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.05971 · Cited by 58 · cited 1×
“On one hand, most of these work [34, 24, 47, 7, 46, 1, 9, 35] use recurrent neural network (RNNs) to aggregate features from consecutive frames.”
BackgroundOn The Stability of Video Detection and TrackingPreprint
Zhang, H., & Wang, N. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.06467 · Cited by 19 · cited 1×
“Comparison with state-of-the-art systems Unlike image object detection, the area of video object detection lacks principled metrics [48] and guidelines for evaluation and comparison.”
Background
Flagged first, then by citations
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
Cited by
Sorted by citations
Simple Baselines for Human Pose Estimation and Tracking
Xiao, B., Wu, H., & Wei, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_29 · Cited by 2,278
“d occlusion introduced by video frames. As shown in Fig. 2(c), the detector misses the left black person due to fast motion. Temporal information is often leveraged to generate more robust detections [36,35]. We propose to generate boxes for the processing frame from nearby frames using temporal information expressed…”
BackgroundSelf-citationTSM: Temporal Shift Module for Efficient Video Understanding
Lin, J., Gan, C., & Han, S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00718 · Cited by 2,209
“We also compare to a strong baseline FGFA [60] that uses optical flow to aggregate the temporal information from 21 frames (past 10 frames and future 10 frames) for offline video detection.”
MethodZhou, X., Koltun, V., & Krähenbühl, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58548-8_28 · Cited by 1,428
“posals using bounding box regression. In video object detection, Kang et al. [16,17] feed stacked consecutive frames into the network and do object detection for a whole video segment. And Zhu et al. [59] use flow to warp intermediate features from previous frames to accelerate inference. Our method belongs to this cat…”
MethodVideo Enhancement with Task-Oriented Flow
Xue, T., Chen, B., Wu, J., Wei, D., & Freeman, W. T. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-01144-2 · Cited by 1,401
“…(Liu et al 2017), light-field interpolation (Wang et al 2017), novel view synthesis (Zhou et al 2016), eye gaze manipulation (Ganin et al 2016), object detection (Zhu et al 2017), denoising (Wen et al 2017), and super-resolution (Caballero et al 2017; Tao et al 2017; Makansi et al 2017).”
A Survey of Deep Learning-Based Object Detection
Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., & Qu, R. (2019) · IEEE Access · doi:10.1109/access.2019.2939201 · Cited by 1,454
“Video detectors [266][267][268][269][270][271][272][273][274][275] exploit temporal contexts to meet this challenge.”
Background
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 ›
Are you an author, or do you see a problem with this page? Request a review
