The DOI matches its Crossref record; 56 of the 59 references it cites were found in a registry or an index.
Conditional DETR for Fast Training Convergence
Open accessAbstract
The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty.Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR.
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
“In addition, we find the conditional queries [25] used in DAB-DETR does not suit our model and we do not include them in our final model.”
From DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection (2022) · cites this paper as [25]
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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Faster 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
Faster 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
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
(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
YOLO9000: Better, Faster, Stronger
Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892
Research works printed but not linked: 1
- kong, 2019, Foveabox: Beyond anchor-based object detector, CoRR
Cited by
Sorted by citations
DETRs Beat YOLOs on Real-time Object Detection
Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., & Chen, J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01605 · Cited by 5,020
“Recently, the end-to-end Transformer-based detectors (DETRs) [4, 16, 22, 25, 33, 36, 41, 42] have received extensive attention from the academia due to their streamlined architecture and elimination of hand-crafted components.”
BackgroundMasked-attention Mask Transformer for Universal Image Segmentation
Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., & Girdhar, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00135 · Cited by 3,565
Grounding DINO: Marrying DINO with Grounded Pre-training for Open-Set Object Detection
Liu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Jiang, Q., Li, C., Yang, J., Su, H., et al. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72970-6_3 · Cited by 2,074
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
Li, F., Zhang, H., Liu, S., Guo, J., Ni, L. M., & Zhang, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01325 · Cited by 1,102
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L. M., & Shum, H.-Y. (2022) · arXiv · Cited by 1,154
“To address such problems, many methods have been proposed, such as introducing deformable attention [41], decoupling positional and content information [25], providing spatial priors [11,39,37], etc.”
BackgroundMethodPETR: Position Embedding Transformation for Multi-view 3D Object Detection
Liu, Y., Wang, T., Zhang, X., & Sun, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_31 · Cited by 764
DiffusionDet: Diffusion Model for Object Detection
Chen, S., Sun, P., Song, Y., & Luo, P. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01816 · Cited by 657
DETRs with Collaborative Hybrid Assignments Training
Zong, Z., Song, G., & Liu, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00621 · Cited by 725
A Survey of Visual Transformers
Liu, Y., Zhang, Y., Wang, Y., Hou, F., Yuan, J. S., Tian, J., Zhang, Y. S., Shi, Z., Jianping Fan, B., & He, Z. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3227717 · Cited by 652
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
Li, F., Zhang, H., Xu, H., Liu, S., Zhang, L., Ni, L. M.-S., & Shum, H. (2023) · 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/cvpr52729.2023.00297 · Cited by 662
Anchor DETR: Query Design for Transformer-Based Detector
Wang, Y., Zhang, X., Yang, T., & Sun, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20158 · Cited by 526
PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
Liu, Y., Yan, J., Jia, F., Li, S., Gao, A., Wang, T., & Zhang, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00302 · Cited by 477
Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review
Amjoud, A. B., & Amrouch, M. (2023) · IEEE Access · doi:10.1109/access.2023.3266093 · Cited by 389
DEIM: DETR with Improved Matching for Fast Convergence
Huang, S., Lu, Z., Cun, X., Yu, Y., Zhou, X., & Shen, X. (2025) · 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/cvpr52734.2025.01412 · Cited by 397
SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications
Shaker, A., Maaz, M., Rasheed, H., Khan, S., Yang, M., & Khan, F. S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01598 · Cited by 311
Jia, D., Yuan, Y., He, H., Wu, X., Yu, H., Lin, W., Sun, L., Zhang, C., & Hu, H. (2023) · 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/cvpr52729.2023.01887 · Cited by 333
Self-citationAO2-DETR: Arbitrary-Oriented Object Detection Transformer
Dai, L., Liu, H., Tang, H., Wu, Z., & Song, P. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3222906 · Cited by 303
YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-Time Object Detection
Chen, Y., Yuan, X., Wang, J., Wu, R., Li, X., Hou, Q., & Cheng, M. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3538473 · Cited by 311
Self-citationTransformer-Based Visual Segmentation: A Survey
Li, X., Ding, H., Yuan, H., Zhang, W., Pang, J., Cheng, G., Chen, K., Liu, Z., & Loy, C. C. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3434373 · Cited by 305
Interior Attention-Aware Network for Infrared Small Target Detection
Wang, K., Du, S., Liu, C., & Cao, Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3163410 · Cited by 276
Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment
Chen, Q., Chen, X., Wang, J., Zhang, S., Yao, K., Feng, H., Han, J., Ding, E., Zeng, G., & Wang, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00610 · Cited by 304
Self-citationOpen-Vocabulary DETR with Conditional Matching
Zang, Y., Li, W., Zhou, K., Huang, C., & Loy, C. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20077-9_7 · Cited by 254
MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection
Zhang, R., Qiu, H., Wang, T., Guo, Z., Cui, Z., Qiao, Y., Li, H., & Gao, P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00840 · Cited by 250
Self-citationConformer: Local Features Coupling Global Representations for Recognition and Detection
Peng, Z., Guo, Z., Huang, W., Wang, Y., Xie, L., Jiao, J., Tian, Q. C., & Ye, Q. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3243048 · Cited by 194
MTR++: Multi-Agent Motion Prediction With Symmetric Scene Modeling and Guided Intention Querying
Shi, S., Jiang, L., Dai, D., & Schiele, B. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3352811 · Cited by 237
Delving Into the Devils of Bird’s-Eye-View Perception: A Review, Evaluation and Recipe
Li, H., Sima, C., Dai, J., Wang, W., Lu, L., Wang, H., Zeng, J., Li, Z., Yang, J., Deng, H., et al. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3333838 · Cited by 211
Self-citationA survey of small object detection based on deep learning in aerial images
Hua, W., & Chen, Q. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11150-9 · Cited by 205
An End-to-End Transformer Model for Crowd Localization
Liang, D., Xu, W., & Bai, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19769-7_3 · Cited by 182
CenterFormer: Center-Based Transformer for 3D Object Detection
Zhou, Z., Zhao, X., Wang, Y., Wang, P., & Foroosh, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_29 · Cited by 183
Query - Dependent Video Representation for Moment Retrieval and Highlight Detection
Moon, W., Hyun, S., Park, S., Park, D., & Heo, J. (2023) · 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/cvpr52729.2023.02205 · Cited by 174
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
Li, F., Zhang, H., Liu, S., Guo, J., Ni, L. M.-S., & Zhang, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3335410 · Cited by 178
MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction
Liao, B., Chen, S., Zhang, Y., Jiang, B., Zhang, Q., Liu, W., Huang, C., & Wang, X. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02235-z · Cited by 226
ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection
Zeng, Y., Chen, Y., Yang, X., Li, Q., & Yan, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3364713 · Cited by 160
SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos
Liu, H., Teng, Y., Lu, T., Wang, H., & Wang, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01703 · Cited by 154
Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR
Li, F., Zeng, A., Liu, S., Zhang, H., Li, H., Zhang, L., & Ni, L. M. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01780 · Cited by 154
Self-citationLess is More: Focus Attention for Efficient DETR
Zheng, D., Dong, W., Hu, H., Chen, X., & Wang, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00614 · Cited by 162
Self-citationA Simple Framework for Open-Vocabulary Segmentation and Detection
Zhang, H., Li, F., Zou, X., Liu, S., Li, C., Yang, J., & Zhang, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00100 · Cited by 185
Ying, X., Liu, L., Wang, Y., Li, R., Chen, N., Lin, Z., Sheng, W., & Zhou, S. (2023) · 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/cvpr52729.2023.01490 · Cited by 140
A Dual Weighting Label Assignment Scheme for Object Detection
Li, S., He, C., Li, R., & Zhang, L. (2022) · 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/cvpr52688.2022.00917 · Cited by 138
Transformer for object detection: Review and benchmark
Li, Y., Miao, N., Ma, L., Shuang, F., & Huang, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107021 · Cited by 135
AdaMixer: A Fast-Converging Query-Based Object Detector
Gao, Z., Wang, L., Han, B., & Guo, S. (2022) · 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/cvpr52688.2022.00529 · Cited by 142
High-Resolution Feature Pyramid Network for Small Object Detection on Drone View
Chen, Z., Ji, H., Zhang, Y., Zhu, Z., & Li, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3286896 · Cited by 143
Accelerating DETR Convergence via Semantic-Aligned Matching
Zhang, G., Luo, Z., Yu, Y., Cui, K., & Lu, S. (2022) · 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/cvpr52688.2022.00102 · Cited by 135
Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement
Hou, X., Liu, M., Zhang, S., Wei, P., & Chen, B. (2024) · 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/cvpr52733.2024.01664 · Cited by 131
A DeNoising FPN With Transformer R-CNN for Tiny Object Detection
Liu, H.-I., Tseng, Y., Chang, K.-C., Wang, P.-J., Shuai, H.-H., & Cheng, W.-H. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3396489 · Cited by 138
Object Detection in Traffic Videos: A Survey
Ghahremannezhad, H., Shi, H., & Liu, C. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3258683 · Cited by 126
DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer
Ye, M., Zhang, J., Zhao, S., Liu, J., Du, B., & Tao, D. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25430 · Cited by 119
Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection
Tang, J., Zhang, W., Liu, H., Yang, M., Jiang, B., Hu, G., & Bai, X. (2022) · 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/cvpr52688.2022.00452 · Cited by 116
A CNN-Transformer Hybrid Model Based on CSWin Transformer for UAV Image Object Detection
Lu, W., Lan, C., Niu, C., Liu, W., Lyu, L., Shi, Q., & Wang, S. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3234161 · Cited by 113
DQ-DETR: DETR with Dynamic Query for Tiny Object Detection
Huang, Y.-X., Liu, H.-I., Shuai, H.-H., & Cheng, W.-H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73116-7_17 · Cited by 112
EF-DETR: A Lightweight Transformer-Based Object Detector With an Encoder-Free Neck
Cheng, S., Song, J., Zhou, M., Wei, X., Pu, H., Luo, J. T., & Jia, W. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3431044 · Cited by 106
Rethinking the multi-scale feature hierarchy in object detection transformer (DETR)
Liu, F., Zheng, Q., Tian, X., Shu, F., Jiang, W., Wang, M., Elhanashi, A., & Saponara, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113081 · Cited by 107
MP-Former: Mask-Piloted Transformer for Image Segmentation
Zhang, H., Li, F., Xu, H., Huang, S., Liu, S., Ni, L. M.-S., & Zhang, L. (2023) · 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/cvpr52729.2023.01733 · Cited by 102
TransVG++: End-to-End Visual Grounding With Language Conditioned Vision Transformer
Deng, J., Yang, Z., Liu, D., Chen, T., Zhou, W., Zhang, Y., Li, H., & Ouyang, W. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3296823 · Cited by 106
Self-citationChen, W., Luo, J., Zhang, F., & Tian, Z. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17949-4 · Cited by 103
Object Detection with Transformers: A Review
Shehzadi, T., Hashmi, K. A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2025) · Sensors · doi:10.3390/s25196025 · Cited by 93
Spatial Residual for Underwater Object Detection
Zhou, J., He, Z., Zhang, D., Liu, S., Fu, X., & Li, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3548652 · Cited by 92
MS-DETR: Efficient DETR Training with Mixed Supervision
Zhao, C., Sun, Y., Wang, W., Chen, Q., Ding, E., Yang, Y., & Wang, J. (2024) · 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/cvpr52733.2024.01611 · Cited by 99
Self-citationComprehensive review of recent developments in visual object detection based on deep learning
Edozie, E., Shuaibu, A. N., John, U. K., & Sadiq, B. O. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11284-w · Cited by 100
Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
Zhang, J., Lin, X., Zhang, W., Wang, K., Tan, X., Han, J., Ding, E., Wang, J., & Li, G. (2023) · 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/cvpr52729.2023.02280 · Cited by 91
Self-citationBoosting Object Detection with Zero-Shot Day-Night Domain Adaptation
Du, Z., Shit, M., & Deng, J. (2024) · 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/cvpr52733.2024.01204 · Cited by 88
Temporally Efficient Vision Transformer for Video Instance Segmentation
Yang, S., Wang, X., Li, Y., Fang, Y., Fang, J., Liu, W., Zhao, X., & Shan, Y. (2022) · 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/cvpr52688.2022.00290 · Cited by 83
Towards Data-Efficient Detection Transformers
Wang, W., Zhang, J., Cao, Y. H., Shen, Y., & Tao, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20077-9_6 · Cited by 80
DESTR: Object Detection with Split Transformer
He, L., & Todorović, S. (2022) · 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/cvpr52688.2022.00916 · Cited by 76
Action Quality Assessment with Temporal Parsing Transformer
Bai, Y., Zhou, D., Zhang, S., Wang, J., Ding, E., Guan, Y., Long, Y., & Wang, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19772-7_25 · Cited by 73
Self-citationKnowing Where to Focus: Event-aware Transformer for Video Grounding
Jang, J., Park, J., Kim, J., Kwon, H., & Sohn, K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01273 · Cited by 73
AG-YOLO: A Rapid Citrus Fruit Detection Algorithm with Global Context Fusion
Lin, Y.-S., Huang, Z., Liang, Y., Liu, Y., & Jiang, W. (2024) · Agriculture · doi:10.3390/agriculture14010114 · Cited by 70
Enhanced Training of Query-Based Object Detection via Selective Query Recollection
Chen, F., Zhang, H., Hu, K., Huang, Y., Zhu, C., & Savvides, M. (2023) · 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/cvpr52729.2023.02275 · Cited by 69
Exploring Predicate Visual Context in Detecting of Human–Object Interactions
Zhang, F. Z., Yuan, Y., Campbell, D., Zhong, Z., & Gould, S. J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00955 · Cited by 69
Self-citationFS-DETR: Few-Shot DEtection TRansformer with prompting and without re-training
Bulat, A., Guerrero, R., Martínez, B., & Tzimiropoulos, G. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01083 · Cited by 67
Lin, W., Sun, Z., Ma, C., Li, M., Wang, J., Sun, L., & Huo, Q. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548038 · Cited by 66
Self-citationCascade-DETR: Delving into High-Quality Universal Object Detection
Ye, M., Ke, L., Li, S., Tai, Y., Tang, C., Danelljan, M., & Yu, F. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00617 · Cited by 68
CF-DETR: Coarse-to-Fine Transformers for End-to-End Object Detection
Cao, X., Yuan, P., Feng, B., & Niu, K. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19893 · Cited by 62
SPTS v2: Single-Point Scene Text Spotting
Liu, Y., Zhang, J., Peng, D., Huang, M., Wang, X., Tang, J., Huang, C., Lin, D., Shen, C., Bai, X., et al. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3312285 · Cited by 62
Zheng, H., Wang, G., Xiao, D., Liu, H., & Hu, X. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123394 · Cited by 67
Xing, Y., Yang, S., Wang, S., Zhang, S., Liang, G., Zhang, X., & Zhang, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3450584 · Cited by 63
Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors
Zhang, G., Luo, Z., Tian, Z., Zhang, J., Zhang, X., & Lu, S. (2023) · 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/cvpr52729.2023.00601 · Cited by 61
Optimizing Insulator Defect Detection with Improved DETR Models
Li, D., Yang, P., & Zou, Y. (2024) · Mathematics · doi:10.3390/math12101507 · Cited by 62
Detection Transformer with Stable Matching
Liu, S., Ren, T., Chen, J., Zeng, Z., Zhang, H., Li, F., Li, H., Huang, J. G., Su, H., Zhu, J., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00597 · Cited by 75
Self-citationSDD-DETR: Surface Defect Detection for No-Service Aero-Engine Blades With Detection Transformer
Sun, X., Song, K., Wen, X., Wang, Y., & Yan, Y. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3457829 · Cited by 59
Guo, J., Gao, C., Liu, F., Meng, D., & Gao, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73383-3_27 · Cited by 59
Self-citationBridged Transformer for Vision and Point Cloud 3D Object Detection
Wang, Y., Ye, T., Cao, L., Huang, W., Sun, F., He, F., & Tao, D. (2022) · 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/cvpr52688.2022.01180 · Cited by 59
CAPE: Camera View Position Embedding for Multi-View 3D Object Detection
Xiong, K., Gong, S., Ye, X., Tan, X. Y., Wan, J., Ding, E., Wang, J., & Bai, X. (2023) · 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/cvpr52729.2023.02066 · Cited by 58
Self-citationMuzammul, M., & Li, X. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02375-9 · Cited by 63
Li, X., Cai, M., Tan, X., Yin, C., Chen, W., Liu, Z., Wen, J., & Han, Y. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109160 · Cited by 56
EMO2-DETR: Efficient-Matching Oriented Object Detection With Transformers
Hu, Z., Gao, K., Zhang, X., Wang, J., Wang, H., Yang, Z., Li, C., & Li, W. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3300154 · Cited by 55
Self-citationCheng, Y., & Liu, D. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3420265 · Cited by 61
DS-DETR: A Model for Tomato Leaf Disease Segmentation and Damage Evaluation
Wu, J., Wen, C., Chen, H., Ma, Z., Zhang, T., Su, H., & Yang, C. (2022) · Agronomy · doi:10.3390/agronomy12092023 · Cited by 53
End-to-End 3D Dense Captioning with Vote2Cap-DETR
Chen, S., Zhu, H., Chen, X., Lei, Y., Yu, G., & Chen, T. (2023) · 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/cvpr52729.2023.01070 · Cited by 62
Self-citationGroup Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation
Liu, H., Chen, Q., Tan, Z., Liu, J., Wang, J., Su, X., Li, X., Yao, K., Han, J., Ding, E., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01380 · Cited by 59
Self-citationDenoDet: Attention as Deformable Multisubspace Feature Denoising for Target Detection in SAR Images
Dai, Y., Zou, M., Li, Y., Li, X., Ni, K., & Yang, J. J. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3507786 · Cited by 53
UAV-DETR: An Enhanced RT-DETR Architecture for Efficient Small Object Detection in UAV Imagery
Zhou, Y., & Wei, Y. (2025) · Sensors · doi:10.3390/s25154582 · Cited by 53
PolarDETR: Polar Parametrization for vision-based surround-view 3D detection
Chen, S., Wang, X., Cheng, T., Zhang, Q., Huang, C., & Liu, W. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105438 · Cited by 73
Distillation Using Oracle Queries for Transformer-based Human-Object Interaction Detection
Qu, X., Ding, C., Li, X., Zhong, X., & Tao, D. (2022) · 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/cvpr52688.2022.01895 · Cited by 51
Ma, S., Zhang, Y., Peng, L., Sun, C., Ding, B., & Zhu, Y. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3560928 · Cited by 54
Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring
You, S., Li, B., Chen, Y., Ren, Z., Liu, Y., Wu, Q., Tao, J., Zhang, Z., Zhang, C., Xue, F., et al. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1607582 · Cited by 51
Self-citationDSR-Net: Distinct selective rollback queries for road cracks detection with detection transformer
Deng, Y., Ma, J., Wu, Z., Wang, W., & Liu, H. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105266 · Cited by 52
Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey
Shi, K., He, S., Shi, Z., An-jun, C., Xiong, Z., Chen, J., & Luo, J. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3599596 · Cited by 62
Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction
Liu, Z. W., Zhang, X., Liu, G., Zhao, J. Y., & Xu, N. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72998-0_26 · Cited by 48
DDOD: Dive Deeper into the Disentanglement of Object Detector
Chen, Z., Yang, C., Chang, J., Zhao, F., Zha, Z.-J., & Wu, F. H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3264008 · Cited by 46
Metadata
- Authors
- Meng, D., Chen, X., Fan, Z., Zeng, G., Li, H., Yuan, Y., Sun, L., & Wang, J.
- Affiliations
- University of Science and Technology of China (CN)
- Peking University (CN)
- Microsoft (United States) (US)
- Microsoft Research Asia (China) (CN)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 3631-3640
- OpenAlex
- W3194329900
- Cited by
- 1,055
- References
- 59
- Citations by year
- 2026: 222 · 2025: 331 · 2024: 263 · 2023: 174 · 2022: 61 · 2021: 4
- FWCI
- 41.05
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Research and Development
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- DETR, object detection, cross-attention, training convergence, content embedding, spatial localization, object classification, transformer encoder-decoder
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
