Objects365: A Large-Scale, High-Quality Dataset for Object Detection
Abstract
In this paper, we introduce a new large-scale object detection dataset, Objects365, which has 365 object categories over 600K training images. More than 10 million, high-quality bounding boxes are manually labeled through a three-step, carefully designed annotation pipeline. It is the largest object detection dataset (with full annotation) so far and establishes a more challenging benchmark for the community. Objects365 can serve as a better feature learning dataset for localization-sensitive tasks like object detection and semantic segmentation. The Objects365 pre-trained models significantly outperform ImageNet pre-trained models with 5.6 points gain (42 vs 36.4) based on the standard setting of 90K iterations on COCO benchmark. Even compared with much long training time like 540K iterations, our Objects365 pretrained model with 90K iterations still have 2.7 points gain (42 vs 39.3). Meanwhile, the finetuning time can be greatly reduced (up to 10 times) when reaching the same accuracy. Better generalization ability of Object365 has also been verified on CityPersons, VOC segmentation, and ADE tasks. The dataset as well as the pretrained-models have been released at www.objects365.org.
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Existing large DETR models have demonstrated impressive performance on COCO test-dev leaderboard.
“Existing large DETR models [3, 6, 32, 41, 44, 46] have demonstrated impressive performance on COCO test-dev [20] leaderboard.”
From DETRs Beat YOLOs on Real-time Object Detection (2024) · cites this paper as [32]
DINO achieves the SOTA result with a publicly available SwinL backbone and a public dataset Objects365 (1.7M annotated images) only.
“In contrast, DINO achieves the SOTA result with a publicly available SwinL [23] backbone and a public dataset Objects365 [33] (1.7M annotated images) only.”
From DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection (2022) · cites this paper as [33]
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 · see citing works
Self-citationFaster 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 · see citing works
Self-citationImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
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 56,035 · see citing works
Self-citationYou 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 · see citing works
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 · see citing works
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 · see citing works
(2016) · Cited by 31,590 · see citing works
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939 · see citing works
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581 · see citing works
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137 · see citing works
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
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 · see citing works
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141 · see citing works
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438 · see citing works
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
Khanam, Z., Achari, V. P. S., Boukhennoufa, I., et al. (2024) · Durham Research Online (Durham University) · doi:10.4230/oasics.ng-res.2024.2 · Cited by 14,048 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433 · see citing works
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853 · see citing works
Deformable 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 · see citing works
Cascade R-CNN: Delving Into High Quality Object Detection
Cai, Z., & Vasconcelos, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00644 · Cited by 7,370 · see citing works
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139 · see citing works
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 · see citing works
Self-citationPedestrian Detection: An Evaluation of the State of the Art
Dollár, P., Wojek, C., Schiele, B., & Perona, P. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.155 · Cited by 3,412 · see citing works
Scene Parsing through ADE20K Dataset
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., & Torralba, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.544 · Cited by 3,326 · see citing works
Deformable ConvNets V2: More Deformable, Better Results
Zhu, X., Hu, H., Lin, S., & Dai, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00953 · Cited by 2,943 · see citing works
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01316-z · Cited by 2,460 · see citing works
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00982 · Cited by 2,194 · see citing works
The PASCAL visual object classes challenge 2006 (VOC2006) results
Everingham, M., Zisserman, A., Williams, C. K. I., & Gool, L. V. (2006) · Cited by 2,079 · see citing works
WIDER FACE: A Face Detection Benchmark
Yang, S., Luo, P., Loy, C. C., & Tang, X. (2016) · 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.2016.596 · Cited by 1,907 · see citing works
DSSD : Deconvolutional Single Shot Detector
Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., & Berg, A. C. (2017) · arXiv · doi:10.48550/arxiv.1701.06659 · Cited by 1,624 · see citing works
Rethinking ImageNet Pre-Training
He, K., Girshick, R., & Dollár, P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00502 · Cited by 1,164 · see citing works
CityPersons: A Diverse Dataset for Pedestrian Detection
Zhang, S., Benenson, R., & Schiele, B. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.474 · Cited by 1,061 · see citing works
An Analysis of Scale Invariance in Object Detection - SNIP
Singh, B., & Davis, L. S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00377 · Cited by 913 · see citing works
FDDB: A benchmark for face detection in unconstrained settings
Jain, V., & Miller, E. G. (2010) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 879 · see citing works
CrowdHuman: A Benchmark for Detecting Human in a Crowd
Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., & Sun, J. (2018) · arXiv · doi:10.48550/arxiv.1805.00123 · Cited by 534 · cited 1× · see citing works
“Besides the general object detection datasets, there are also a lot of other detection benchmarks like face detection [19, 35], pedestrian detection [7, 36, 31], and human/vehicle detection for the autonomous driving [11, 2], all of which play an important role in the detection community.”
BackgroundSelf-citationDiagnosing Error in Object Detectors
Hoiem, D., Chodpathumwan, Y., & Dai, Q. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33712-3_25 · Cited by 520 · see citing works
SNIPER: Efficient Multi-Scale Training
Singh, B., Najibi, M., & Davis, L. S. (2018) · arXiv · doi:10.48550/arxiv.1805.09300 · Cited by 438 · see citing works
MegDet: A Large Mini-Batch Object Detector
Peng, C., Xiao, T., Li, Z., Jiang, Y., Zhang, X., Jia, K., Yu, G., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00647 · Cited by 311 · see citing works
Self-citationDetNet: A Backbone network for Object DetectionPreprint
Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y. S., & Sun, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.06215 · Cited by 228 · see citing works
Self-citationScalable multi-label annotation
Deng, J., Russakovsky, O., Krause, J., Bernstein, M. S., Berg, A., & Fei-Fei, L. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557011 · Cited by 156 · see citing works
Soft Sampling for Robust Object DetectionPreprint
Wu, Z., Bodla, N., Singh, B., Najibi, M., Chellappa, R., & Davis, L. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.06986 · Cited by 55 · see citing works
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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 · see citing works
“After pre-training with Objects365 [32], RT-DETR-R50 / R101 achieves 55 .”
Object Detection in 20 Years: A Survey
Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3238524 · Cited by 3,689 · see citing works
“, ILSVRC2014) [56], MS-COCO Detection Challenge [57], Open Images Dataset [58, 59], Objects365 [60], etc.”
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
Terven, J. R., Esparza, D. M. C., & Romero-González, J.-A. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5040083 · Cited by 3,681 · see citing works
“The model is pre-trained on Objects365 [126], which contains two million images and 365 categories, then the COCO dataset was used to generate pseudo-labels.”
MethodSwin Transformer V2: Scaling Up Capacity and Resolution
Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., et al. (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/cvpr52688.2022.01170 · Cited by 2,741 · see citing works
“For our largest model experiments, we employ an additional detection pre-training phase using Object 365 v2 dataset [59], in-between the image classification pre-training phase and the COCO fine-tuning phase.”
MethodSwin Transformer V2: Scaling Up Capacity and Resolution
Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., et al. (2021) · arXiv · doi:10.48550/arxiv.2111.09883 · Cited by 2,679 · see citing works
“For our largest model experiments, we employ an additional detection pre-training phase using Object 365 v2 dataset [59], in-between the image classification pre-training phase and the COCO fine-tuning phase.”
MethodGrounding 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 · see citing works
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., & Zhao, H. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00987 · Cited by 1,296 · see citing works
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Ghiasi, G., Cui, Y., Srinivas, A., Qian, R., Lin, T.-Y., Cubuk, E. D., Le, Q. V., & Zoph, B. (2021) · 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/cvpr46437.2021.00294 · Cited by 1,248 · see citing works
“When combined with self-training using unlabeled COCO and unlabeled Objects365 [49] for pseudo-labeling, we see a further improvement of 2.5 box AP and 2.2 mask AP, resulting in a model with a strong performance of 57.3 box AP and 49.1 mask AP on COCO test-dev without test-time augmen-tations and model ensembling.”
MethodInfluentialGrounded Language-Image Pre-training
Li, L. H., Zhang, P., Zhang, H., Yang, J., Li, C., Zhong, Y., Wang, L., Yuan, L., Zhang, L., Hwang, J., et al. (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/cvpr52688.2022.01069 · Cited by 1,197 · see citing works
“, Objects365 [47]), in practice, we can split the categories into multiple prompts during training and inference.”
BackgroundMethodVision-Language Models for Vision Tasks: A Survey
Zhang, J., Huang, J., Jin, S., & Lu, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3369699 · Cited by 1,051 · see citing works
“With the object locating ability learned from auxiliary datasets [85], [86], pre-trained VLMs achieve zero-shot prediction for object detection tasks by comparing the embeddings of the given object proposals and texts.”
BackgroundMethodInternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
Wang, W., Dai, J., Chen, Z., Huang, Z., Li, Z., Zhu, X., Hu, X., Lu, T., Lu, L., Li, H., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01385 · Cited by 1,080 · see citing works
“For this dataset, we initialize our InternImage-H with the Objects365 [79]”
MethodInfluentialSelf-citationVinVL: Revisiting Visual Representations in Vision-Language Models
Zhang, P., Li, X., Hu, X., Yang, J., Zhang, L., Wang, L., Choi, Y., & Gao, J. (2021) · 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/cvpr46437.2021.00553 · Cited by 965 · see citing works
“3 mAP 50 in [30], which is obtained by pre-training on Objects365.”
BackgroundMethodYOLO-World: Real-Time Open-Vocabulary Object Detection
Cheng, T., Song, L., Ge, Y., Liu, W., Wang, X., & Shan, Y. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01599 · Cited by 815 · see citing works
“For pre-training YOLO-World, we mainly adopt detection or grounding datasets including Ob-jects365 (V1) [46], GQA [18], Flickr30k [38], as specified in Tab.”
MethodDINO: 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 · see citing works
“DINO with ResNet-50 is trained on train2017 without extra data, while DINO with SwinL is first pre-trained on Object365 [33] and then fine-tuned on train2017.”
BackgroundMethodInfluentialDETRs 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 650 · see citing works
“Then we perform intermediate finetuning on the Objects365 dataset us-ing C o-DINO-Deformable-DETR for 26 epochs and reduce the learning rate by a factor of 0.1 at epoch 24.”
MethodInfluentialImbalance Problems in Object Detection: A Review
Öksüz, K., Çam, B. C., Kalkan, S., & Akbaş, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2981890 · Cited by 607 · see citing works
“Throughout the text, unless otherwise specified, “COCO”, “PASCAL”, and “Open Images” respectively correspond to the Pascal VOC 2012 trainval [51], MS-COCO 2017 train [90], Open Images v5 training (subset with bounding boxes) [91] and Objects365 train [92] dataset partitions.”
BackgroundGLIGEN: Open-Set Grounded Text-to-Image Generation
Li, Y., Liu, H., Wu, Q., Mu, F., Yang, J., Gao, J., Li, C., & Lee, Y. 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/cvpr52729.2023.02156 · Cited by 610 · see citing works
“Specifically, we follow GLIP [34] and train on Object365 [58] and GoldG [34], which combines two grounding datasets: Flickr [49] and VG [31].”
End-to-End Semi-Supervised Object Detection with Soft Teacher
Xu, M., Zhang, Z., Hu, H., Wang, J., Wang, L., Wei, F., Bai, X., & Liu, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00305 · Cited by 582 · see citing works
“* indicates that models are pre-trained with Object365 [24] dataset.”
BackgroundMethodInfluentialMask 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 597 · see citing works
“After detection pre-training on the Objects365 [31] dataset with a SwinL [24] backbone, Mask DINO significantly improves all segmentation tasks and achieves the best results on instance ( 54.5 AP on COCO), panoptic ( 59.4 PQ on COCO), and semantic ( 60.8 mIoU on ADE20K) segmentation among models under one billion param…”
BackgroundMethodInfluentialDetecting Twenty-Thousand Classes Using Image-Level Supervision
Zhou, X., Girdhar, R., Joulin, A., Krähenbühl, P., & Misra, I. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20077-9_21 · Cited by 564 · see citing works
“We evaluate on two large-scale object detection datasets: Objects365v2 [44] and OpenImages [25], both with around 1.8M training images.”
BackgroundMethodInfluentialSun, X., Wang, P., Yan, Z., Xu, F., Wang, R., Diao, W., Chen, J., Li, J., Feng, Y., Xu, T., et al. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.12.004 · Cited by 555 · see citing works
“More recently, Megvii released Objects365 [46], a largescale dataset containing 638k images, which is 5 times larger than MSCOCO dataset.”
MethodSelf-citationImage as a Foreign Language: BEIT Pretraining for Vision and Vision-Language Tasks
Wang, W., Bao, H., Dong, L., Bjorck, J., Peng, Z., Liu, Q., Aggarwal, K., Mohammed, O. K., Singhal, S., Som, S., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01838 · Cited by 561 · see citing works
“Following common practices [39, 72], we first conduct intermediate finetuning on the Objects365 [51] dataset.”
MethodEVA: Exploring the Limits of Masked Visual Representation Learning at Scale
Fang, Y., Wang, W., Xie, B., Sun, Q., Wu, L., Wang, X., Huang, T., Wang, X., & Cao, Y. (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/cvpr52729.2023.01855 · Cited by 540 · see citing works
“For intermediate fine-tuning on Objects365 [87], the model is trained with a batch size of 128 for 380k iterations.”
MethodInfluentialRethinking Pre-training and Self-trainingPreprint
Zoph, B., Ghiasi, G., Lin, T.-Y., Cui, Y., Liu, H., Cubuk, E. D., & Le, Q. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06882 · Cited by 547 · see citing works
“Note that OID is found to not be helpful on COCO by pre-training in [41].”
BackgroundYOLO-based Object Detection Models: A Review and its Applications
Vijayakumar, A., & Subramaniyaswamy, V. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18872-y · Cited by 516 · see citing works
“[24] The effectiveness and generalizability of the resulting computer vision models are strongly influenced by the quality and diversity of the data that was gathered.”
Open-vocabulary Object Detection via Vision and Language Knowledge DistillationPreprint
Gu, X., Lin, T.-Y., Kuo, W., & Cui, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.13921 · Cited by 581 · see citing works
“We evaluate the transferability of ViLD on PASCAL VOC (Everingham et al., 2010), COCO (Lin et al., 2014), and Objects365 (Shao et al., 2019).”
MethodInfluentialAli, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
“However, the architectural advancements in YOLOv10 and YOLOv11 resulted in even better performance, with YOLOv11 achieving an mAP50 of 0.958.”
InfluentialLearning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model
Du, Y., Wei, F., Zhang, Z., Shi, M., Gao, Y., & Li, G. (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/cvpr52688.2022.01369 · Cited by 367 · see citing works
“Meanwhile, we conduct transfer experiments to demonstrate generalization ability of our approach, and evaluate our LVIS-trained model on Pascal VOC [4] test set, COCO [15] validation set and Objects365 [23] validation set.”
MethodSimple Open-Vocabulary Object Detection
Minderer, M., Gritsenko, A. A., Stone, A., Neumann, M., Weissenborn, D., Dosovitskiy, A., Mahendran, A., Arnab, A., Dehghani, M., Shen, Z., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_42 · Cited by 365 · see citing works
How far are we to GPT-4V? Closing the gap to commercial multimodal models with open-source suites
Chen, Z., Wang, W., Tian, H., Ye, S., Gao, Z., Cui, E., Tong, W., Hu, K., Luo, J., Ma, Z., et al. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4231-5 · Cited by 432 · see citing works
“Detection and grounding tasks utilize datasets like Ob-jects365 [93], GRIT [86], and All-Seeing [114], making up 5.2%.”
InfluentialFlorence-2: Advancing a Unified Representation for a Variety of Vision Tasks
Xiao, B., Wu, H., Xu, W., Dai, X., Hu, H., Lu, Y., Zeng, M., Liu, C., & Yuan, L. (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/cvpr52733.2024.00461 · Cited by 338 · see citing works
“In addition, our dataset has around 1.3B region-text annotations, which is more than 30x larger than the academic object detection datasets such as OpenImages [40] and Object 365 [70].”
BackgroundMethodExploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
Wang, S., Liu, Y., Wang, T., Li, Y., & Zhang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00335 · Cited by 292 · see citing works
“Following BEVFormerv2 [50], the ViT-Large [8] is pre-trained on Objects365 [41] and COCO [28] dataset.”
MethodSelf-citationBeyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection
Guo, Z., Liu, C., Zhang, X., Jiao, J., Ji, X., & Ye, Q. (2021) · 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/cvpr46437.2021.00868 · Cited by 267 · see citing works
“This is attributed to the availability of deep networks incorporating rich feature representation [16, 15] and large-scale databases [3, 17, 28] for pretraining representative models.”
BackgroundSelf-citationGroup 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 266 · see citing works
“We follow its training pipeline and settings: (i) pre-train [5] and fine-tune the ViT-Huge on ImageNet-1K [8], (ii) pre-train the whole detector on Object365 [22] for 24 epochs with 64 A100 GPUs, and (iii) finetune the detector on COCO [15] for 20 epochs with 32 A100 GPUs.”
MethodTransformer-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 272 · see citing works
“However, due to the extra datasets [352] they used, we do not list them here.”
BackgroundS2Looking: A Satellite Side-Looking Dataset for Building Change Detection
Shen, L., Lu, Y., Chen, H., Wei, H., Xie, D., Yue, J., Chen, R., Lv, S., & Jiang, B. (2021) · Remote Sensing · doi:10.3390/rs13245094 · Cited by 242 · see citing works
“These include ImageNet [39], COCO [40], PIE [41], MVTec AD [42], Objects365[43], WildDeepfake [44], FineGym [45] and ISIA Food-500 [46].”
MethodKhanam, R., Hussain, M., Hill, R. C., & Allen, P. (2024) · IEEE Access · doi:10.1109/access.2024.3425166 · Cited by 273 · see citing works
“More recent contributions such as Objects365 [236] have further emphasized the importance of temporal dynamics and object interactions in OD tasks.”
BackgroundTransfer learning for medical images analyses: A survey
Yu, X., Wang, J., Hong, Q., Teku, R., Wang, S., & Zhang, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.08.159 · Cited by 237 · see citing works
AutoAssign: Differentiable Label Assignment for Dense Object DetectionPreprint
Zhu, B., Wang, J., Jiang, Z., Zong, F., Liu, S., Li, Z., & Sun, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03496 · Cited by 208 · see citing works
“Extensive experiments with competitive results are carried out on the challenging MS COCO [12] dataset and other datasets from different domains, such as PASCAL VOC [3, 4], Object365 [19] and Wider-Face [22], demonstrating the effectiveness and broad applicability of AutoAssign.”
BackgroundMethodSelf-citationCut and Learn for Unsupervised Object Detection and Instance Segmentation
Wang, X., Girdhar, R., Yu, S. X., & Misra, I. (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/cvpr52729.2023.00305 · Cited by 202 · see citing works
How Much Can CLIP Benefit Vision-and-Language Tasks?Preprint
Shen, S., Li, L. H., Tan, H. X., Bansal, M., Rohrbach, A., Chang, K.-W., Yao, Z., & Keutzer, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.06383 · Cited by 272 · see citing works
“VinVL (Li et al., 2020) is an extreme scaleup of the region-based paradigm, which is pretrained on multiple object detection datasets, including MS COCO (Lin et al., 2014), OpenImages (Kuznetsova et al., 2020), Object365 (Shao et al., 2019), and Visual Genome (Krishna et al., 2017).”
MethodWu, T., Zhang, J., Fu, X. Y., Wang, Y., Ren, J., Pan, L., Wu, W., Yang, L., Wang, J., Qian, C., et al. (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/cvpr52729.2023.00084 · Cited by 202 · see citing works
GLaMM: Pixel Grounding Large Multimodal Model
Rasheed, H., Maaz, M., Shaji, S., Shaker, A., Khan, S., Cholakkal, H., Anwer, R. M., Xing, E. P., Yang, M., & Khan, F. S. (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/cvpr52733.2024.01236 · Cited by 205 · see citing works
“Images Regions Concepts Tokens Captions † COCO [25] 0.1M 0.9M 80 - - LVIS [9] 0.1M 1.5M 1,203 - - Objects365 [42] 0.6M 10.1M 365 - - Open Images [20] 1.5M 14.8M 600 - - BigDetection [4] 3.5M 36.0M 600 - - V3Det [43] 0.2M 1.5M Table 2.”
Tools, techniques, datasets and application areas for object detection in an image: a review
Kaur, J., & Singh, W. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13153-y · Cited by 194 · see citing works
“Most of the dataset has contained the images downloaded from the “Flickr photo-sharing website” [35, 155].”
MethodProbabilistic two-stage detectionPreprint
Zhou, X., Koltun, V., & Krähenbühl, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07461 · Cited by 219 · see citing works
“In fact, they are no longer faster than their two-stage counterparts if the vocabulary (i.e., the set of object classes) is large (as in the LVIS or Objects365 datasets).”
BackgroundMethodInfluentialA Survey on Long-Tailed Visual Recognition
Yang, L., Jiang, H., Song, Q., & Guo, J. H. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01622-8 · Cited by 170 · see citing works
“10 (Object365 [133] and ADE20K [201]), and evaluate their performance to investigate whether the long-tailed problem is practically shown in visual recognition.”
BackgroundMethodInfluentialVision-Language Pre-Training: Basics, Recent Advances, and Future Trends
Gan, Z., Li, L., Li, C., Wang, L., Liu, Z., & Gao, J. (2022) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000105 · Cited by 155 · see citing works
GRIT: Faster and Better Image Captioning Transformer Using Dual Visual Features
Nguyen, V.-Q., Suganuma, M., & Okatani, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20059-5_10 · Cited by 152 · see citing works
UniVTG: Towards Unified Video-Language Temporal Grounding
Lin, K. Q., Zhang, P., Chen, J., Pramanick, S., Gao, D., Wang, A. J., Yan, R., & Shou, M. Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00262 · Cited by 156 · see citing works
“The emergence of large-scale vision-language datasets, such as [43, 41, 28, 4], has paved the way for the development of VLP [36, 22, 19, 34, 21] to enhance video-text representation for various vision-language tasks [60, 54, 53].”
BackgroundUniversal Instance Perception as Object Discovery and Retrieval
Yan, B., Jiang, Y., Wu, J., Wang, D., Luo, P., Yuan, Z., & Lu, 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/cvpr52729.2023.01471 · Cited by 152 · see citing works
“Since Objects365 does not have mask annotations, we introduce two auxiliary losses proposed by BoxInst [97] for training the mask branch.”
BackgroundMethodInfluentialAligning Bag of Regions for Open-Vocabulary Object Detection
Wu, S., Zhang, W., Jin, S., Liu, W., & Loy, C. C. (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/cvpr52729.2023.01464 · Cited by 146 · see citing works
“We transfer the openvocabulary detector trained on OV-LVIS to three other datasets, including Pascal VOC 2007 [11] test set, COCO [31] validation set and Objects365 [43] v2 validation set.”
MethodMultimodal Foundation Models: From Specialists to General-Purpose Assistants
Li, C., Gan, Z., Yang, Z., Yang, J., Li, L., Wang, L., & Gao, J. (2024) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000110 · Cited by 225 · see citing works
A 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 149 · see citing works
“For object detection, we use Objects365 [42] (660k images for v1 and 1700k images for v2).”
MethodInfluentialInjecting Semantic Concepts into End-to-End Image Captioning
Fang, Z., Wang, J., Hu, X., Liang, L., Gan, Z., Wang, L., Yang, Y., & Liu, Z. (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/cvpr52688.2022.01748 · Cited by 141 · see citing works
“To test the generalization of ViTCAP, we also report the performances on nocaps dataset [1], a benchmark consisting of 166 k human-generated captions describing 15 k images in the wild collected from the OpenImages dataset [60].”
MethodReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension
Subramanian, S., Merrill, W., Darrell, T., Gardner, M., Singh, S., & Rohrbach, A. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.357 · Cited by 139 · see citing works
RT-DETRv3: Real-Time End-to-End Object Detection with Hierarchical Dense Positive Supervision
Wang, S., Xia, C., Lv, F., & Shi, Y. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00166 · Cited by 128 · see citing works
“For example, our RT-DETRv3-R101 can achieve 54.6 AP, which is better than YOLOv10-X.”
InfluentialAffordance Transfer Learning for Human-Object Interaction Detection
Hou, Z., Yu, B., Qiao, Y., Peng, X., & Tao, D. (2021) · 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/cvpr46437.2021.00056 · Cited by 133 · see citing works
“In detail, we select objects that are labeled as COCO classes from Object365 validation dataset to evaluate the affordance recognition of objects on new domain.”
BackgroundMethodInfluentialLearning Open-World Object Proposals Without Learning to Classify
Kim, D., Lin, T.-Y., Angelova, A., Kweon, I. S., & Kuo, W. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3146922 · Cited by 121 · see citing works
“Therefore, in this work we study the generalization setting of training on COCO, then testing on other datasets: RoboNet [15], Objects365 [48], EpicKitchens [14], and LVIS [24].”
MethodInfluentialDiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision
Lan, S., Yu, Z., Choy, C., Radhakrishnan, S., Liu, G., Zhu, Y., Davis, L. S., & Anandkumar, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00339 · Cited by 114 · see citing works
“Our weakly supervised design allows D ISCO B OX to be conveniently trained like any object detection algo-rithm on the increasingly large datasets [58–60], but output additional predictions beyond just bounding boxes.”
BackgroundContextual Object Detection with Multimodal Large Language Models
Zang, Y., Li, W., Han, J., Zhou, K., & Loy, C. C. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02214-4 · Cited by 130 · see citing works
“However, these evaluations still rely on the predefined ID-name mappings, while objects with names not included in predefined mappings are impossible.”
A Generalist Framework for Panoptic Segmentation of Images and Videos
Chen, T., Li, L., Saxena, S., Hinton, G. E., & Fleed, D. J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00090 · Cited by 113 · see citing works
“The image encoder is taken from the Pix2Seq [10] object detection checkpoint, pre-trained on objects365 [49].”
MethodWang, R., Liu, L., Xie, C., Yang, P., Li, R., & Zhou, M. (2021) · Sensors · doi:10.3390/s21051601 · Cited by 105 · see citing works
“The current large volume of standardized object detection datasets [1–3] help to explore many key research challenges that are related to object detection and evaluate the performance of different algorithms and technologies.”
BackgroundFlexiViT: One Model for All Patch Sizes
Beyer, L., Izmailov, P., Kolesnikov, A., Caron, M., Kornblith, S., Zhai, X., Minderer, M., Tschannen, M., Alabdulmohsin, I., & Pavetic, F. (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/cvpr52729.2023.01393 · Cited by 106 · see citing works
“8 [48] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun.”
BackgroundMethodProposal Learning for Semi-Supervised Object Detection
Tang, P., Ramaiah, C., Wang, Y., Xu, R., & Xiong, C. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00234 · Cited by 102 · see citing works
“However, training accurate object detectors relies on the availability of large scale labeled datasets [9, 29, 40, 42], which are very expensive and time-consuming to collect.”
BackgroundFashionVLP: Vision Language Transformer for Fashion Retrieval with Feedback
Goenka, S., Zheng, Z., Jaiswal, A., Chada, R., Wu, Y., Hedau, V., & Natarajan, P. (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/cvpr52688.2022.01371 · Cited by 102 · see citing works
“We use a publicly available [54] Faster-RCNN-C4 [39] with ResNeXt-152 [52] backbone (X152-C4) trained on MSCOCO [29], Visual Genome [23], Objects365 [43], and OpenImage V5 [24], following [54].”
MethodDetecting Everything in the Open World: Towards Universal Object Detection
Wang, Z., Li, Y., Chen, X., Lim, S.-N., Torralba, A. B., Zhao, H., & Wang, 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/cvpr52729.2023.01100 · Cited by 103 · see citing works
“However, if we further add OpenImages images to COCO and Objects365, the LVIS v0.”
InfluentialExploiting Unlabeled Data with Vision and Language Models for Object Detection
Zhao, S., Zhang, Z.-X., Schulter, S., Zhao, L., Kumar, B. G. V., Stathopoulos, A., Chandraker, M. K., & Metaxas, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20077-9_10 · Cited by 100 · see citing works
PANDA: A Gigapixel-Level Human-Centric Video Dataset
Wang, X., Zhang, X., Zhu, Y., Guo, Y., Yuan, X., Xiang, L., Wang, Z., Ding, G., Brady, D. J., Dai, Q., et al. (2020) · 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/cvpr42600.2020.00333 · Cited by 99 · see citing works
Self-citationConfidence Score: The Forgotten Dimension of Object Detection Performance Evaluation
Wenkel, S., Alhazmi, K., Liiv, T., Alrshoud, S. R., & Simón, M. (2021) · Sensors · doi:10.3390/s21134350 · Cited by 102 · see citing works
“However, benchmark datasets used in publications are not growing despite a growing number of datasets covering the task of 2D (e.g., [12–16]) and 3D object detection (e.g., [17–23]).”
BackgroundActive Teacher for Semi-Supervised Object Detection
Mi, P., Lin, J., Zhou, Y., Shen, Y., Luo, G., Sun, X., Cao, L., Fu, R., Xu, Q., & Ji, R. (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/cvpr52688.2022.01408 · Cited by 97 · see citing works
“Recent years have witnessed the rapid development of object detection supported by a flurry of benchmark datasets [9, 11, 22, 32] and methods [13–15, 23, 26, 28, 29].”
Simple Multi-dataset Detection
Zhou, X., Koltun, V., & Krähenbühl, P. (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/cvpr52688.2022.00742 · Cited by 98 · see citing works
“Yet object detection is fragmented into datasets [18, 22, 24, 33] and our models are locked into the corresponding domains.”
BackgroundMethodInfluentialChen, W., Luo, J., Zhang, F., & Tian, Z. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17949-4 · Cited by 94 · see citing works
Segment and Recognize Anything at Any Granularity
Li, F., Zhang, H., Sun, P., Zou, X., Liu, S., Li, C., Yang, J., Zhang, L., & Gao, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73195-2_27 · Cited by 89 · see citing works
“Generic object detection and segmentation have led to the development of abundant datasets, such as MSCOCO [39], LVIS [21], Objects365 [53], PAS-CAL [15],CityScapes [12],ADE20k [68].”
InfluentialOpen-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling
Huynh, D., Kuen, J., Lin, Z., Gu, J., & Elhamifar, E. (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/cvpr52688.2022.00689 · Cited by 87 · see citing works
“To demonstrate the scalability of our method, we apply cross-modal pseudo-labeling with multiple segmentation datasets (Open Images [2], LVIS [3]), object detection dataset (Objects365 [103]), and caption dataset (Conceptual Captions [99]), to create a high-performance student model.”
MethodClass-Agnostic Object Detection with Multi-modal Transformer
Maaz, M., Rasheed, H., Khan, S. H., Khan, F. S., Anwer, R. M., & Yang, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_30 · Cited by 87 · see citing works
“Settings: Table 1 shows the object proposal generation performance of MViTs with the typical bottom-up approaches and the end-to-end supervised deep learning methods on five challenging natural image OD datasets (Pascal VOC [14], MS COCO [40], KITTI [17], Objects365 [56] and LVIS [19]).”
MethodOpen Vocabulary Object Detection with Pseudo Bounding-Box Labels
Gao, M., Chen, X., Niebles, J. C., Li, J., Xu, R., Liu, W., & Xiong, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_16 · Cited by 87 · see citing works
“Therefore, we evaluate our method and the strongest baseline (both are fine-tuned using COCO base classes) on PASCAL VOC [5] test set, Objects365 v2 [25] validation set and LVIS [10] validation set 1.”
MethodSelf-citationRegion-Aware Pretraining for Open-Vocabulary Object Detection with Vision Transformers
Kim, D., Angelova, A., & Kuo, W. (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/cvpr52729.2023.01072 · Cited by 85 · see citing works
“• Objects365 [5]: Custom (research-only, noncommercial)”
Diffusioninst: Diffusion Model for Instance Segmentation
Gu, Z., Chen, H., & Xu, Z. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447191 · Cited by 94 · see citing works
“This section compares our model with popular existing meth-ods and the performances are all obtained without pretraining on extra data like Objects-365 [24].”
MethodA Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future
Zhu, C., & Chen, L. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3413013 · Cited by 87 · see citing works
DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment
Yao, L., Han, J., Liang, X., Xu, D., Zhang, W., Li, Z., & Xu, 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/cvpr52729.2023.02250 · Cited by 82 · see citing works
“Specifically, for detection data, we use a sampled subset from Objects365v2 [43] dataset (denoted as O365) with 0.”
MethodSelf-citationOMG-Seg: Is One Model Good Enough for all Segmentation?
Li, X., Yuan, H., Li, W., Ding, H., Wu, S., Zhang, W., Li, Y., Chen, K., & Loy, C. C. (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/cvpr52733.2024.02640 · Cited by 82 · see citing works
“* denotes models are pre-trained on the Object365 dataset [73].”
Relation DETR: Exploring Explicit Position Relation Prior for Object Detection
Hou, X., Liu, M., Zhang, S., Wei, P., Chen, B., & Lan, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72973-7_6 · Cited by 83 · see citing works
XAIR: A Framework of Explainable AI in Augmented Reality
Xu, X., Yu, A., Jonker, T. R., Todi, K., Lu, F., Qian, X., Belo, J. M. E., Wang, T., Li, M., Mun, A., et al. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581500 · Cited by 84 · see citing works
“For ingredient recognition, we trained a vision-based object detection model that was a variant of the Vision Transformer from CLIP [173] on the LVIS [95] and Objects365 [197] datasets.”
MethodSelf-citationPACO: Parts and Attributes of Common Objects
Ramanathan, V., Kalia, A., Petrovic, V., Wen, Y., Zheng, B., Guo, B., Wang, R., Marquez, A., Kovvuri, R., Kadian, A., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00690 · Cited by 79 · see citing works
“Object detection and segmentation datasets The task of detecting and segmenting object instances is well studied with popular benchmark datasets such as COCO [32], LVIS [15], Object365 [40], Open Images [26] and Pascal [9] for common objects.”
BackgroundPartImageNet: A Large, High-Quality Dataset of Parts
He, J., Yang, S., Yang, S., Kortylewski, A., Yuan, X., Chen, J.-N., Liu, S., Yang, C., Yu, Q., & Yuille, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20074-8_8 · Cited by 78 · see citing works
“Objects365: A large-scale, high-quality dataset for object detection.”
MethodX2-VLM: All-in-One Pre-Trained Model for Vision-Language Tasks
Zeng, Y., Zhang, X., Li, H., Wang, J., Zhang, J., & Zhou, W. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3339661 · Cited by 91 · see citing works
“We leverage three types of data for vision language pre-training, including object labels on images [14], [15], [16] such as “man” or “backpack”, region annotations on images [16], [17] such as “boy wearing back-pack”, and text descriptions for images such as “The first day of school gives a mixed…”
MethodSelf-citationFar3D: Expanding the Horizon for Surround-View 3D Object Detection
Jiang, X., Li, S., Liu, Y., Wang, S., Jia, F., Wang, T., Han, L., & Zhang, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.28033 · Cited by 78 · see citing works
Self-citationMulti-Task Self-Training for Learning General Representations
Ghiasi, G., Zoph, B., Cubuk, E. D., Le, Q. V., & Lin, T.-Y. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00873 · Cited by 84 · see citing works
“showed similar findings using the Objects365 dataset [49].”
BackgroundResultInfluentialLearning to Generate Scene Graph from Natural Language Supervision
Zhong, Y., Shi, J., Yang, J., Xu, C., & Li, Y. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00184 · Cited by 73 · see citing works
“We additionally consider another object detector trained on Object365 dataset [39].”
MethodV*: Guided Visual Search as a Core Mechanism in Multimodal LLMs
Wu, P., & Xie, S. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01243 · Cited by 74 · see citing works
“Objects365 v2 [42] and GoldG [17] datasets are only used as detection data.”
MethodWu, Z., Liu, C., Wen, J., Xu, Y. D., Yang, J. J., & Li, X. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3231744 · Cited by 75 · see citing works
“However, its training requires the large-scale dataset with bounding-box annotations [13], [14], [15], which are labor-intensive and time-consuming to be collected.”
BackgroundCompressing Visual-linguistic Model via Knowledge Distillation
Fang, Z., Wang, J., Hu, X., Wang, L., Yang, Y., & Liu, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00146 · Cited by 70 · see citing works
Tackling class imbalance in computer vision: a contemporary review
Saini, M., & Susan, S. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10557-6 · Cited by 68 · see citing works
Open-Vocabulary SAM: Segment and Recognize Twenty-Thousand Classes Interactively
Yuan, H., Li, X., Zhou, C., Li, Y., Chen, K., & Loy, C. C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72775-7_24 · Cited by 70 · see citing works
“In particular, we adopt more detection data (V3Det [57], Object365 [53]) and classification data (Im-ageNet22k [11]).”
MethodLarge-Scale Object Detection in the Wild From Imbalanced Multi-Labels
Peng, J., Bu, X., Sun, M., Zhang, Z., Tan, T., & Yan, J. (2020) · 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/cvpr42600.2020.00973 · Cited by 67 · see citing works
“ges with 12.4 million boxes of 500 categories in its challenge split. The scale of training images is 15 times of the MS COCO [21] and 3 times of the second largest object detection dataset Object365 [30]. Considering the huge size of Open Images dataset, we split a mini Open Images dataset for our ablation study. The…”
BackgroundInfluentialVIVO: Visual Vocabulary Pre-Training for Novel Object Captioning
Hu, X., Yin, X. P., Lin, K., Zhang, L., Gao, J., Wang, L., & Liu, Z. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16249 · Cited by 66 · see citing works
“Thus, it opens the possibility of leveraging, for VLP, many existing vision datasets originally developed for image tagging or object detection tasks like ImageNet (Deng et al. 2009), Open Images (Kuznetsova et al. 2020), Objects365 (Shao et al. 2019), etc.”
BackgroundYOLOE: Real-time seeing anything
Wang, A., Liu, L., Chen, H., Lin, Z., Han, J., & Ding, G. (2025) · 2025 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02280 · Cited by 65 · see citing works
“Objects365 [52] Detection ✓ ✓ 609k 8,530k GQA [17] Grounding ✓ ✓ 621k 3,662k Flickr [43] Grounding ✓ ✓ 149k 638k Training.”
InfluentialT2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-Image Generation
Huang, K., Duan, C., Sun, K., Xie, E., Li, Z., & Liu, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3531907 · Cited by 74 · see citing works
“We employ the UniDet [78] model trained on 4 large-scale detection datasets (COCO [58], Objects365 [85], OpenImages [86], and Mapillary [87]).”
Self-citationDetGPT: Detect What You Need via Reasoning
Pi, R., Gao, J., Diao, S., Pan, R., Dong, H., Zhang, J., Yao, L., Han, J., Xu, H., Kong, L., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.876 · Cited by 74 · see citing works
Self-citationBEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection
Li, Z., Lan, S., Álvarez, J. M., & Wu, Z. (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/cvpr52733.2024.01901 · Cited by 63 · see citing works
“ViT-L [10] is pretrained on COCO [33] and Objects365 [52], while ViT-Adapter-L [8] is pretrained on DINOv2 [47].”
BackgroundSelf-citation
- Authors
- Shao, S., Li, Z., Zhang, T., Peng, C., Yu, G., Zhang, X., Li, J., & Sun, J.
- Affiliations
- Megvii (China) (CN)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 8429-8438
- OpenAlex
- W2983943451
- Cited by
- 673
- References
- 42
- Citations by year
- 2026: 70 · 2025: 155 · 2024: 180 · 2023: 132 · 2022: 67 · 2021: 50 · 2020: 17 · 2019: 2
- FWCI
- 11.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- bounding box annotation, annotation pipeline, pre-trained models, representation learning, semantic segmentation, benchmark, COCO benchmark, CityPersons, ADE20K
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