The DOI matches its Crossref record; 27 of the 28 references it cites were found in a registry or an index.
LVIS: A Dataset for Large Vocabulary Instance Segmentation
Abstract
Progress on object detection is enabled by datasets that focus the research community’s attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks. In this work, we introduce LVIS (pronounced ‘el-vis’): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect 2.2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images. Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge. LVIS is available at http://www.lvisdataset.org.
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
“We build our benchmark on the LVIS dataset (Gupta et al., 2019), known for its rich long-tail vocabulary and abundance of small, densely packed objects.”
From ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning (2025) · cites this paper as Gupta et al., 2019
In the citing paper
“LVIS. We construct our benchmark for open-world small and dense object grounding based on the LVIS (Gupta et al., 2019) dataset, which offers the richest long-tail object vocabulary and the highest prevalence of small and densely packed instances among existing segmentation datasets.”
From ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning (2025) · cites this paper as Gupta et al., 2019
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
ImageNet 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
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
ImageNet 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
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
Self-citationBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
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
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Kilgarriff, A. (2000) · Language · doi:10.2307/417141 · Cited by 11,529
Flagged first, then by citations
1 of the 28 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- liberman, 2015, Reproducible research and the common task method, Simmons Foundation Lecture
- miller, 1998, WordNet An Electronic Lexical Database
Cited by
Sorted by citations
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347
Self-citationKirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00371 · Cited by 11,953
“ADE20K[117] BBBC038v1[12] Cityscapes[25] DOORS[80] DRAM[24] EgoHOS[113] GTEA[34,63] Hypersim[86] IBD[17] iShape[111] LVIS[44] NDD20[100] NDISPark[22,23] OVIS[81] PPDLS[74] Plittersdorf[46] STREETS[91] TimberSeg[38] TrashCan[52] VISOR[28,27] WoodScape[112] PIDRay[104] ZeroWaste-f[6] Figure 8: Samples from the 23 divers…”
BackgroundMethodResultInfluentialSelf-citationScalability in Perception for Autonomous Driving: Waymo Open Dataset
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00252 · Cited by 3,437
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
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 1,324
PointRend: Image Segmentation As Rendering
Kirillov, A., Wu, Y., He, K., & Girshick, R. (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/cvpr42600.2020.00982 · Cited by 1,284
Self-citationGrounded 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 1,336
Decoupling Representation and Classifier for Long-Tailed RecognitionPreprint
Kang, B., Xie, S., Rohrbach, M., Yan, Z., Gordo, A., Feng, J., & Kalantidis, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.09217 · Cited by 1,217
Vision-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
Recent advances in deep learning for object detection
Wu, X., Sahoo, D., & Hoi, S. C. H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.085 · Cited by 1,047
BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition
Zhou, B., Cui, Q., Wei, X.-S., & Chen, Z.-M. (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/cvpr42600.2020.00974 · Cited by 972
Exploring Plain Vision Transformer Backbones for Object Detection
Li, Y., Mao, H., Girshick, R., & He, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20077-9_17 · Cited by 1,027
Self-citationDeep Long-Tailed Learning: A Survey
Zhang, Y., Kang, B., Hooi, B., Yan, S., & Feng, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3268118 · Cited by 924
YOLO-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
MDETR - Modulated Detection for End-to-End Multi-Modal Understanding
Kamath, A., Singh, M., LeCun, Y., Synnaeve, G., Misra, I., & Carion, N. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00180 · Cited by 802
Objaverse: A Universe of Annotated 3D Objects
Deitke, M., Schwenk, D., Salvador, J., Weihs, L., Michel, O., VanderBilt, E., Schmidt, L., Ehsanit, K., Kembhavi, A., & Farhadi, A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01263 · Cited by 725
“We choose the categories from the LVIS dataset [26] for categorizing a long-tail subset of objects in OBJAVERSE.”
BackgroundMethodInfluentialA comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT
Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., Zhang, K., Ji, C., Yan, Q., He, L., et al. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02443-6 · Cited by 1,001
BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation
Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., & Yan, Y. (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/cvpr42600.2020.00860 · Cited by 680
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
RegionCLIP: Region-based Language-Image Pretraining
Zhong, Y., Yang, J., Zhang, P., Li, C., Codella, N., Li, L. H., Zhou, L., Dai, X., Yuan, L., Li, Y., 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/cvpr52688.2022.01629 · Cited by 681
GLIGEN: 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 715
Image Segmentation Using Text and Image Prompts
Lüddecke, T., & Ecker, A. 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/cvpr52688.2022.00695 · Cited by 657
Open-vocabulary Object Detection via Vision and Language Knowledge DistillationPreprint
Gu, X.-Y., Lin, T.-Y., Kuo, W.-C., & Cui, Y. (2021) · International Conference on Learning Representations · doi:10.48550/arxiv.2104.13921 · Cited by 1,464
Frustratingly Simple Few-Shot Object DetectionPreprint
Wang, X., Huang, T. E., Darrell, T., Gonzalez, J. E., & Yu, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.06957 · Cited by 568
Detecting 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
Boundary IoU: Improving Object-Centric Image Segmentation Evaluation
Cheng, B., Girshick, R., Dollár, P., Berg, A. C., & Kirillov, A. (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/cvpr46437.2021.01508 · Cited by 598
Self-citationEVA: 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 655
Equalization Loss for Long-Tailed Object Recognition
Tan, J., Wang, C., Li, B., Li, Q., Ouyang, W., Yin, C., & 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/cvpr42600.2020.01168 · Cited by 528
Quasi-Dense Similarity Learning for Multiple Object Tracking
Pang, J., Qiu, L., Li, X., Chen, H., Li, Q., Darrell, T., & Yu, F. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00023 · Cited by 480
Balanced Meta-Softmax for Long-Tailed Visual RecognitionPreprint
Ren, J., Yu, C., sheng, S., Ma, X., Zhao, H., Yi, S., & Li, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.10740 · Cited by 449
Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review
Lu, Y., Chen, D., Olaniyi, E. O., & Huang, Y. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107208 · Cited by 415
Florence: A New Foundation Model for Computer Vision
Yuan, L., Chen, D., Chen, Y.-L., Codella, N., Dai, X., Gao, J., Hu, H., Huang, X., Li, B., Li, C., et al. (2021) · arXiv · doi:10.48550/arxiv.2111.11432 · Cited by 389
Meta-Learning to Detect Rare Objects
Wang, Y.-X., Ramanan, D., & Hebert, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01002 · Cited by 380
Learning 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 393
Simple 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 406
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
Fan, H., Bai, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Harshit, Huang, M., Liu, J., et al. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01387-y · Cited by 360
Effective Fusion Factor in FPN for Tiny Object Detection
Gong, Y., Yu, X., Ding, Y., Peng, X., Zhao, J., & Han, Z. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00120 · Cited by 361
FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
Wen, B., Yang, W., Kautz, J., & Birchfield, S. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01692 · Cited by 353
VirTex: Learning Visual Representations from Textual Annotations
Desai, K., & Johnson, 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/cvpr46437.2021.01101 · Cited by 376
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
Michaelis, C., Mitzkus, B., Geirhos, R., Rusak, E., Bringmann, O., Ecker, A. S., Bethge, M., & Brendel, W. (2019) · arXiv · doi:10.48550/arxiv.1907.07484 · Cited by 324
Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
Zhang, S., Li, Z., Yan, S., He, X., & Sun, 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/cvpr46437.2021.00239 · Cited by 322
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal EffectPreprint
Tang, K., Huang, J., & Zhang, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.12991 · Cited by 322
EVA-02: A visual representation for neon genesis
Fang, Y., Sun, Q., Wang, X., Huang, T., Wang, X., & Cao, Y. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105171 · Cited by 473
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
Xiong, Y., Varadarajan, B., Wu, L., Xiang, X., Xiao, F., Zhu, C., Dai, X., Wang, D., Sun, F., Iandola, F., et al. (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/cvpr52733.2024.01525 · Cited by 356
Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax
Li, Y., Wang, T., Kang, B., Tang, S., Wang, C., Li, J., & Feng, 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/cvpr42600.2020.01100 · Cited by 297
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts
Hou, J., Graham, B., Nießner, M., & Xie, S. (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/cvpr46437.2021.01533 · Cited by 299
Seesaw Loss for Long-Tailed Instance Segmentation
Wang, J., Zhang, W., Zang, Y., Cao, Y., Pang, J., Gong, T., Chen, K., Liu, Z., Loy, C. C., & Lin, 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/cvpr46437.2021.00957 · Cited by 291
Reviving Iterative Training with Mask Guidance for Interactive Segmentation
Sofiiuk, K., Petrov, I. A., & Konushin, A. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897365 · Cited by 285
Jamal, M. A., Brown, M., Yang, M., Wang, L., & Gong, B. (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/cvpr42600.2020.00763 · Cited by 270
TIDE: A General Toolbox for Identifying Object Detection Errors
Bolya, D., Foley, S. R., Hays, J. H., & Hoffman, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_33 · Cited by 263
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
Li, R., Zhang, S., Wan, B., & He, X. (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/cvpr46437.2021.01096 · Cited by 265
RelTR: Relation Transformer for Scene Graph Generation
Cong, Y., Yang, M. Y., & Rosenhahn, B. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3268066 · Cited by 272
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation
Araslanov, N., & Roth, S. (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/cvpr46437.2021.01513 · Cited by 255
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-Language Model
Xu, M., Zhang, Z., Wei, F., Lin, Y., Cao, Y., Hu, H., & Bai, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19818-2_42 · Cited by 296
A review on 2D instance segmentation based on deep neural networks
Gu, W., Bai, S., & Kong, L. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2022.104401 · Cited by 258
F-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation
Sofiiuk, K., Petrov, I. A., Barinova, O., & Konushin, A. (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/cvpr42600.2020.00865 · Cited by 239
Open-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
Zhou, X., Yin, T., 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/cvpr52688.2022.00857 · Cited by 230
Sharma, V., & Mir, R. N. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100301 · Cited by 230
A survey on few-shot class-incremental learning
Tian, S., Li, L., Li, W., Ran, H., Ning, X., & Tiwari, P. (2024) · Neural Networks · doi:10.1016/j.neunet.2023.10.039 · Cited by 220
Probabilistic two-stage detectionPreprint
Zhou, X., Koltun, V., & Krähenbühl, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07461 · Cited by 219
Balanced MSE for Imbalanced Visual Regression
Ren, J., Zhang, M., Yu, C., & 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/cvpr52688.2022.00777 · Cited by 227
Cheng, H. K., Tai, Y., & Tang, C. (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/cvpr46437.2021.00551 · Cited by 209
Wang, Y.-R., Yang, K., Wen, Y., Wang, P., Hu, Y., Lai, Y., Wang, Y., Zhao, K., Tang, S., Zhang, A., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02971-2 · Cited by 212
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA
Marino, K., Chen, X., Parikh, D., Gupta, A., & Rohrbach, M. (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/cvpr46437.2021.01389 · Cited by 211
Self-citationGLaMM: 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 302
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation
Zhang, G., Luo, Z., Cui, K., Lu, S., & Xing, E. P. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3195735 · Cited by 203
Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection
Tan, J., Lu, X., Zhang, G., Yin, C., & Li, 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/cvpr46437.2021.00173 · Cited by 210
Cut 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 226
Wu, 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 246
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation
Gadre, S. Y., Wortsman, M., Ilharco, G., Schmidt, L., & Song, 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/cvpr52729.2023.02219 · Cited by 278
Towards Open Vocabulary Learning: A Survey
Wu, J., Li, X., Xu, S., Yuan, H., Ding, H., Yang, Y., Li, X., Zhang, J., Tong, Y., Jiang, X., et al. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3361862 · Cited by 218
OASIS: Only Adversarial Supervision for Semantic Image Synthesis
Sushko, V., Schönfeld, E., Zhang, D., Gall, J., Schiele, B., & Khoreva, A. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01673-x · Cited by 204
Language-Grounded Indoor 3D Semantic Segmentation in the Wild
Rozenberszki, D., Litany, O., & Dai, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19827-4_8 · Cited by 207
The Devil Is in Classification: A Simple Framework for Long-Tail Instance Segmentation
Wang, T., Yu, L. Z., Kang, B., Li, J., Liew, J. H., Tang, S., Hoi, S. C. H., & Feng, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58568-6_43 · Cited by 185
Mask Transfiner for High-Quality Instance Segmentation
Ke, L., Danelljan, M., Li, X., Tai, Y., Tang, C., & Yu, F. (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/cvpr52688.2022.00437 · Cited by 193
FocalClick: Towards Practical Interactive Image Segmentation
Chen, X., Zhao, Z., Zhang, Y., Duan, M., Qi, D., & Zhao, H. (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/cvpr52688.2022.00136 · Cited by 190
MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation
Lambert, J., Liu, Z., Şener, O., Hays, J. H., & Koltun, V. (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/cvpr42600.2020.00295 · Cited by 182
PromptDet: Towards Open-Vocabulary Detection Using Uncurated Images
Feng, C., Zhong, Y., Jie, Z., Chu, X., Ren, H., Wei, X. K., Xie, W., & Ma, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20077-9_41 · Cited by 191
On Bridging Generic and Personalized Federated Learning for Image ClassificationPreprint
Chen, H.-Y., & Chao, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.00778 · Cited by 215
SiamMOT: Siamese Multi-Object Tracking
Shuai, B., Berneshawi, A., Li, X., Modolo, D., & Tighe, 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/cvpr46437.2021.01219 · Cited by 178
A 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 179
TAO: A Large-Scale Benchmark for Tracking Any Object
Dave, A., Khurana, T., Tokmakov, P., Schmid, C., & Ramanan, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58558-7_26 · Cited by 185
RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features
Zhang, G., Lu, X., Tan, J., Li, J., Zhang, Z., Li, Q., & Hu, X. (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/cvpr46437.2021.00679 · Cited by 169
Occluded Video Instance Segmentation: A Benchmark
Qi, J., Gao, Y., Hu, Y. G., Wang, X., Liu, X., Bai, X., Belongie, S., Yuille, A., Torr, P. H. S., & Bai, S. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01629-1 · Cited by 191
MixFormer: Mixing Features across Windows and Dimensions
Chen, Q., Wu, Q., Wang, J., Hu, Q., Hu, T., Ding, E., Cheng, J., & Wang, J. (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/cvpr52688.2022.00518 · Cited by 170
QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking
Fischer, T., Huang, T., Pang, J., Qiu, L., Chen, H., Darrell, T., & Yu, F. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3301975 · Cited by 179
ABO: Dataset and Benchmarks for Real-World 3D Object Understanding
Collins, J., Goel, S., Deng, K., Luthra, A., Xu, L. L., Gündoğdu, E., Zhang, X., Vicente, T. F. Y., Dideriksen, T. L., Arora, H., 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/cvpr52688.2022.02045 · Cited by 206
Long- Tailed Recognition via Weight Balancing
Alshammari, S., Wang, Y.-X., Ramanan, D., & Kong, 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/cvpr52688.2022.00677 · Cited by 169
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering
Hyun Cho, J., Mall, U., Bala, K., & Hariharan, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01652 · Cited by 159
Vision-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
Few-Shot Object Detection: A Comprehensive Survey
Köhler, M., Eisenbach, M., & Groß, H. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3265051 · Cited by 164
CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching
Wu, X., Zhu, F., Zhao, R., & Li, 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/cvpr52729.2023.00679 · Cited by 163
Reconstructing Hand-Object Interactions in the Wild
Cao, Z., Radosavovic, I., Kanazawa, A., & Malik, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01219 · Cited by 163
Pointly-Supervised Instance Segmentation
Cheng, B., Parkhi, O., & Kirillov, A. (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/cvpr52688.2022.00264 · Cited by 165
Universal 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 174
Equalized Focal Loss for Dense Long-Tailed Object Detection
Li, B., Yao, Y., Tan, J., Zhang, G., Yu, F., Lu, J., & Luo, 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/cvpr52688.2022.00686 · Cited by 156
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 185
Deep Polarization Cues for Transparent Object Segmentation
Kalra, A., Taamazyan, V., Rao, S. K., Venkataraman, K., Raskar, R., & Kadambi, A. (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/cvpr42600.2020.00863 · Cited by 152
Metadata
- Authors
- Gupta, A., Dollar, P., & Girshick, R.
- Affiliations
- Meta (United States) (US)
- FAIR
- ORCID
- Gupta, Agrim
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 5351-5359
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2948672349
- Cited by
- 1,274
- References
- 28
- Citations by year
- 2026: 153 · 2025: 246 · 2024: 285 · 2023: 219 · 2022: 164 · 2021: 141 · 2020: 55 · 2019: 11
- FWCI
- 30.32
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (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
- lymphovascular invasion, instance segmentation masks, object categories, object detection, benchmark dataset, complex scenes
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
