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Open-vocabulary detection is a challenging task due to the requirement of detecting objects based on class names, including those not encountered during training.
C. F. Van Loan, “The ubiquitous kronecker product,” Journal of computational and applied mathematics , vol. 123, no. 1-2, pp. 85–100, 2000
2000
Earlier work this paper cites.
R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06) , vol. 2. IEEE, 2006, pp. 1735–1742
2006
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1440–1448
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems , vol. 28, 2015
2015
Earlier work this paper cites.
S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. L. Zitnick, and D. Parikh, “Vqa: Visual question answering,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2425–2433
2015
Earlier work this paper cites.
B. A. Plummer, L. Wang, C. M. Cervantes, J. C. Caicedo, J. Hockenmaier, and S. Lazebnik, “Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2641–2649
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li, “Yfcc100m: The new data in multimedia research,” Communications of the ACM , vol. 59, no. 2, pp. 64–73, 2016
2016
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Bansal, K. Sikka, G. Sharma, R. Chellappa, and A. Divakaran, “Zero-shot object detection,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 384–400
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
P. Sharma, N. Ding, S. Goodman, and R. Soricut, “Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2018, pp. 2556–2565
2018
Earlier work this paper cites.
T. Yao, Y. Pan, Y. Li, and T. Mei, “Exploring visual relationship for image captioning,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 684–699
2018
Earlier work this paper cites.
A. Gupta, P. Dollár, and R. Girshick, “Lvis: A dataset for large vocabulary instance segmentation,” Computer Vision and Pattern Recognition , 2019
2019
Earlier work this paper cites.
J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, and H. Lu, “Dual attention network for scene segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 3146–3154
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Lu, D. Batra, D. Parikh, and S. Lee, “Vilbert: Pretraining task-agnostic vision linguistic representations for vision-and-language tasks,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
P. Gao, Z. Jiang, H. You, P. Lu, S. C. Hoi, X. Wang, and H. Li, “Dynamic fusion with intra-and inter-modality attention flow for visual question answering,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 6639–6648
2019
Earlier work this paper cites.
G. I. O. Union, “A metric and a loss for bounding box regression,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 658–666
2019
Earlier work this paper cites.
S. Shao, Z. Li, T. Zhang, C. Peng, G. Yu, X. Zhang, J. Li, and J. Sun, “Objects365: A large-scale, high-quality dataset for object detection,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 8430–8439
2019
Cited alongside, same era.
2019
Cited alongside, same era.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16 . Springer, 2020, pp. 213–229
2020
Cited alongside, same era.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of machine learning research , vol. 21, no. 140, pp. 1–67, 2020
H. Zhang, P. Zhang, X. Hu, Y.-C. Chen, L. H. Li, X. Dai, L. Wang, L. Yuan, J.-N. Hwang, and J. Gao, “Glipv2: Unifying localization and vision-language understanding,” in Advances in Neural Information Processing Systems , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
C. Feng, Y. Zhong, Z. Jie, X. Chu, H. Ren, X. Wei, W. Xie, and L. Ma, “Promptdet: Towards open-vocabulary detection using uncurated images,” in European Conference on Computer Vision . Springer, 2022, pp. 701–717
2022
Later among the works it cites.
2022
Later among the works it cites.
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
L. Guo, J. Liu, X. Zhu, P. Yao, S. Lu, and H. Lu, “Normalized and geometry-aware self-attention network for image captioning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 10 327–10 336
2020
Cited alongside, same era.
X. Li, X. Yin, C. Li, P. Zhang, X. Hu, L. Zhang, L. Wang, H. Hu, L. Dong, F. Wei et al. , “Oscar: Object-semantics aligned pre-training for vision-language tasks,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXX 16 . Springer, 2020, pp. 121–137
2020
Cited alongside, same era.
A. Zareian, K. D. Rosa, D. H. Hu, and S.-F. Chang, “Open-vocabulary object detection using captions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 393–14 402
2021
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Cited alongside, same era.
C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. Le, Y.-H. Sung, Z. Li, and T. Duerig, “Scaling up visual and vision-language representation learning with noisy text supervision,” in International conference on machine learning . PMLR, 2021, pp. 4904–4916
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 012–10 022
2021
Cited alongside, same era.
P. Ren, C. Li, G. Wang, Y. Xiao, Q. Du, X. Liang, and X. Chang, “Beyond fixation: Dynamic window visual transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 11 987–11 997
2022
Later among the works it cites.
C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman et al. , “Laion-5b: An open large-scale dataset for training next generation image-text models,” Advances in Neural Information Processing Systems , vol. 35, pp. 25 278–25 294, 2022
2022
Later among the works it cites.
J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in International Conference on Machine Learning . PMLR, 2022, pp. 12 888–12 900
2022
Later among the works it cites.
Y. Zhong, J. Yang, P. Zhang, C. Li, N. Codella, L. H. Li, L. Zhou, X. Dai, L. Yuan, Y. Li et al. , “Regionclip: Region-based language-image pretraining,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 793–16 803
2022
Later among the works it cites.
J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds et al. , “Flamingo: a visual language model for few-shot learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 23 716–23 736, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
X. Zhou, R. Girdhar, A. Joulin, P. Krähenbühl, and I. Misra, “Detecting twenty-thousand classes using image-level supervision,” in European Conference on Computer Vision . Springer, 2022, pp. 350–368
2022
Later among the works it cites.
F. Li, H. Zhang, S. Liu, J. Guo, L. M. Ni, and L. Zhang, “Dn-detr: Accelerate detr training by introducing query denoising,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 13 619–13 627
2022
Later among the works it cites.
2023
Later among the works it cites.
Y. Long, Y. Wen, J. Han, H. Xu, P. Ren, W. Zhang, S. Zhao, and X. Liang, “Capdet: Unifying dense captioning and open-world detection pretraining,” 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Wu, W. Zhang, S. Jin, W. Liu, and C. C. Loy, “Aligning bag of regions for open-vocabulary object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 15 254–15 264
2023
Later among the works it cites.
J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” in International conference on machine learning . PMLR, 2023, pp. 19 730–19 742
2023
Later among the works it cites.
L. Yao, J. Han, X. Liang, D. Xu, W. Zhang, Z. Li, and H. Xu, “Detclipv2: Scalable open-vocabulary object detection pre-training via word-region alignment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 23 497–23 506
2023
Later among the works it cites.
Y. Xu, M. Zhang, C. Fu, P. Chen, X. Yang, K. Li, and C. Xu, “Multi-modal queried object detection in the wild,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
T. Cheng, L. Song, Y. Ge, W. Liu, X. Wang, and Y. Shan, “Yolo-world: Real-time open-vocabulary object detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR) , 2024
2024
Closest in time.
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” in International conference on machine learning . PMLR, 2015, pp. 2048–2057
2057
Closest in time.