7.1.7.7 One-Shot Object Detection, Single Shot Detector, and Segmentation

Chapter Contents (Back)
Object Detction. One-Shot Detection. Few-Shot Detection. Single Shot Detection. One Shot.
See also YOLO, You Only Look Once, Family Object Detection.
See also Semi-Supervised Object Detection.
See also Instance of Particular Object, Specified Object.
See also Dense Object Detection.

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Rahman, S.[Shafin], Khan, S.H.[Salman H.], Porikli, F.M.[Fatih M.],
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Chen, X., Wang, Y., Liu, J., Qiao, Y.,
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Object detection, low-shot learning, continuous learning, deep learning, transfer learning BibRef

Chen, F.Y.[Fang-Yi], Zhu, C.C.[Chen-Chen], Shen, Z.Q.[Zhi-Qiang], Zhang, H.[Han], Savvides, M.[Marios],
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Elsevier DOI 2010
Object detection, Norm calibration, Feature selection BibRef

Zhu, C.C.[Chen-Chen], He, Y.H.[Yi-Hui], Savvides, M.[Marios],
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Zhu, C.C.[Chen-Chen], Chen, F.Y.[Fang-Yi], Shen, Z.Q.[Zhi-Qiang], Savvides, M.[Marios],
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Li, Y., Pang, Y., Cao, J., Shen, J., Shao, L.,
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Feature extraction, Detectors, Object detection, Visualization, Semantics, Sports, Real-time systems, Object detection, feature erasing BibRef

Pambala, A.K.[Ayyappa Kumar], Dutta, T.[Titir], Biswas, S.[Soma],
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Few-shot learning, Object detection, Transfer learning, Visual reasoning, Data augmentation BibRef

Huang, X.[Xu], He, B.[Bokun], Tong, M.[Ming], Wang, D.W.[Ding-Wen], He, C.[Chu],
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Miao, S.Y.[Shu-Yu], Du, S.S.[Shan-Shan], Feng, R.[Rui], Zhang, Y.J.[Yue-Jie], Li, H.Y.[Hua-Yu], Liu, T.[Tianbi], Zheng, L.[Lin], Fan, W.G.[Wei-Guo],
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Elsevier DOI 2112
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Chen, P.Y.[Ping-Yang], Chang, M.C.[Ming-Ching], Hsieh, J.W.[Jun-Wei], Chen, Y.S.[Yong-Sheng],
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Training, Location awareness, Visualization, Purification, Fuses, Bidirectional control, Object detection, Feature pyramid network, feature fusion BibRef

Chen, T.[Tao], Xie, G.S.[Guo-Sen], Yao, Y.Z.[Ya-Zhou], Wang, Q.[Qiong], Shen, F.M.[Fu-Min], Tang, Z.M.[Zhen-Min], Zhang, J.[Jian],
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Image segmentation, Prototypes, Semantics, Training, Feature extraction, Task analysis, Testing, Image segmentation, semantically meaningful prototype BibRef

Vu, A.K.N.[Anh-Khoa Nguyen], Nguyen, N.D.[Nhat-Duy], Nguyen, K.D.[Khanh-Duy], Nguyen, V.T.[Vinh-Tiep], Ngo, T.D.[Thanh Duc], Do, T.T.[Thanh-Toan], Nguyen, T.V.[Tam V.],
Few-shot object detection via baby learning,
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Elsevier DOI 2204
Few-shot object detection, Few-shot learning, Baby learning BibRef

Cheng, M.[Meng], Wang, H.L.[Han-Li], Long, Y.[Yu],
Meta-Learning-Based Incremental Few-Shot Object Detection,
CirSysVideo(32), No. 4, April 2022, pp. 2158-2169.
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Object detection, Feature extraction, Detectors, Adaptation models, Training, Task analysis, Data models, Few-shot learning, object detection BibRef

Feng, H.T.[Hang-Tao], Zhang, L.[Lu], Yang, X.[Xu], Liu, Z.Y.[Zhi-Yong],
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Machine learning, Convolutional neural networks, Transfer learning, Incremental few-shot object detection BibRef

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Detectors, Location awareness, Feature extraction, Training, Maximum likelihood estimation, Object detection, Visualization, generalized linear model BibRef

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CVPR21(7359-7368)
IEEE DOI 2111
Training, Location awareness, Systematics, Codes, Object detection, Detectors BibRef

Wang, H.[Hao], Wang, Q.L.[Qi-Long], Zhang, H.Z.[Hong-Zhi], Hu, Q.H.[Qing-Hua], Zuo, W.M.[Wang-Meng],
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IEEE DOI 2205
Location awareness, Task analysis, Feature extraction, Object detection, Representation learning, Detectors, Proposals, feature interaction BibRef

Wang, H.[Hao], Jia, T.[Tong], Ma, B.[Bowen], Wang, Q.L.[Qi-Long], Zuo, W.M.[Wang-Meng],
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Wang, Y.[Yan], Xu, C.F.[Chao-Fei], Liu, C.W.[Cui-Wei], Li, Z.K.[Zhao-Kui],
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Chen, S.Q.[Shi-Qi], Zhang, J.[Jun], Zhan, R.H.[Rong-Hui], Zhu, R.Q.[Rong-Qiang], Wang, W.[Wei],
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Springer DOI 2211
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Earlier:
CRNet: Cross-Reference Networks for Few-Shot Segmentation,
CVPR20(4164-4172)
IEEE DOI 2008
Image segmentation, Task analysis, Training, Predictive models, Fuses, Testing, Data models BibRef

Xu, Q.X.[Qian-Xiong], Zhao, W.T.[Wen-Ting], Lin, G.S.[Guo-Sheng], Long, C.[Cheng],
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Meneghetti, L.[Laura], Demo, N.[Nicola], Rozza, G.[Gianluigi],
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ICIP22(2206-2210)
IEEE DOI 2211
Training, Image coding, Transfer learning, Object detection, Robustness, Real-time systems, Image Processing, Object Detection, Convolutional Neural Network BibRef

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Yang, Z.[Ze], Zhang, C.[Chi], Li, R.[Ruibo], Xu, Y.[Yi], Lin, G.S.[Guo-Sheng],
Efficient Few-Shot Object Detection via Knowledge Inheritance,
IP(32), 2023, pp. 321-334.
IEEE DOI 2301
Detectors, Feature extraction, Benchmark testing, Object detection, Training, Task analysis, Proposals, Few-shot object detection, meta learning BibRef

Sun, B.[Bo], Li, B.H.[Bang-Huai], Cai, S.C.[Sheng-Cai], Yuan, Y.[Ye], Zhang, C.[Chi],
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CVPR21(7348-7358)
IEEE DOI 2111
Training, Visualization, Pipelines, Object detection, Benchmark testing, Encoding, Power capacitors BibRef

Shi, X.W.[Xiang-Wen], Cui, Z.[Zhe], Zhang, S.B.[Shao-Bing], Cheng, M.[Miao], He, L.[Lian], Tang, X.[Xianghong],
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Li, Y.[Yuewen], Feng, W.[Wenquan], Lyu, S.C.[Shu-Chang], Zhao, Q.[Qi],
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CVIU(227), 2023, pp. 103600.
Elsevier DOI 2301
Few-shot object detection, Meta-learning, Metric learning, Feature representation, Pearson distance BibRef

Zhang, T.Y.[Tian-Yang], Zhang, X.R.[Xiang-Rong], Zhu, P.[Peng], Jia, X.P.[Xiu-Ping], Tang, X.[Xu], Jiao, L.C.[Li-Cheng],
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PandRS(195), 2023, pp. 353-364.
Elsevier DOI 2301
Generalized few-shot object detection, Remote sensing images, Transfer-learning, Metric learning BibRef

Chen, C.F.[Chao-Fan], Yang, X.S.[Xiao-Shan], Zhang, J.P.[Jin-Peng], Dong, B.[Bo], Xu, C.S.[Chang-Sheng],
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IP(32), 2023, pp. 1092-1107.
IEEE DOI 2302
Detectors, Object detection, Training, Task analysis, Prototypes, Annotations, Reliability, Object detection, few-shot learning, graph neural network BibRef

Huang, G.[Gabriel], Laradji, I.[Issam], Vázquez, D.[David], Lacoste-Julien, S.[Simon], Rodríguez, P.[Pau],
A Survey of Self-Supervised and Few-Shot Object Detection,
PAMI(45), No. 4, April 2023, pp. 4071-4089.
IEEE DOI 2303
Survey, Few-Shot Object Detection. Object detection, Detectors, Transformers, Feature extraction, Task analysis, Head, Benchmark testing, Self-supervised, few-shot, instance segmentation BibRef

Cheng, G.[Gong], Lang, C.[Chunbo], Han, J.W.[Jun-Wei],
Holistic Prototype Activation for Few-Shot Segmentation,
PAMI(45), No. 4, April 2023, pp. 4650-4666.
IEEE DOI 2303
Prototypes, Image segmentation, Task analysis, Semantics, Feature extraction, Decoding, Training, Few-shot learning, cross-reference BibRef

Liu, S.[Shifan], Ma, A.[Ailong], Pan, S.[Shaoming], Zhong, Y.F.[Yan-Fei],
An Effective Task Sampling Strategy Based on Category Generation for Fine-Grained Few-Shot Object Recognition,
RS(15), No. 6, 2023, pp. 1552.
DOI Link 2304
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Xu, Y.Q.[Yun-Qiu], Zhou, C.L.[Chun-Luan], Yu, X.[Xin], Yang, Y.[Yi],
Cyclic Self-Training With Proposal Weight Modulation for Cross-Supervised Object Detection,
IP(32), 2023, pp. 1992-2002.
IEEE DOI 2304
Annotations, Proposals, Object detection, Labeling, Integrated circuits, Training, Detectors, cross supervision BibRef

Xu, Y.Q.[Yun-Qiu], Sun, Y.F.[Yi-Fan], Yang, Z.X.[Zong-Xin], Miao, J.X.[Jia-Xu], Yang, Y.[Yi],
H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-domain Weakly Supervised Object Detection,
CVPR22(14309-14319)
IEEE DOI 2210
Adaptation models, Head, Codes, Annotations, Pipelines, Object detection, Recognition: detection, categorization, Self- semi- meta- Transfer/low-shot/long-tail learning BibRef

Zheng, Z.[Zewen], Huang, G.[Guoheng], Yuan, X.C.[Xiao-Chen], Pun, C.M.[Chi-Man], Liu, H.R.[Hong-Rui], Ling, W.K.[Wing-Kuen],
Quaternion-Valued Correlation Learning for Few-Shot Semantic Segmentation,
CirSysVideo(33), No. 5, May 2023, pp. 2102-2115.
IEEE DOI 2305
Quaternions, Correlation, Convolution, Semantics, Semantic segmentation, Quantum cascade lasers, Task analysis, quaternion-valued convolution BibRef

Sun, H.L.[Hao-Liang], Lu, X.K.[Xian-Kai], Wang, H.C.[Hao-Chen], Yin, Y.L.[Yi-Long], Zhen, X.T.[Xian-Tong], Snoek, C.G.M.[Cees G.M.], Shao, L.[Ling],
Attentional prototype inference for few-shot segmentation,
PR(142), 2023, pp. 109726.
Elsevier DOI 2307
Few-shot segmentation, Variational inference, Probabilistic model, Latent attention BibRef

Wang, H.C.[Hao-Chen], Yang, Y.D.[Yan-Dan], Cao, X.B.[Xian-Bin], Zhen, X.T.[Xian-Tong], Snoek, C.G.M.[Cees G.M.], Shao, L.[Ling],
Variational Prototype Inference for Few-Shot Semantic Segmentation,
WACV21(525-534)
IEEE DOI 2106
Image segmentation, Uncertainty, Semantics, Prototypes, Benchmark testing, Probabilistic logic BibRef

Lu, Z.[Zhihe], He, S.[Sen], Li, D.[Da], Song, Y.Z.[Yi-Zhe], Xiang, T.[Tao],
Prediction Calibration for Generalized Few-Shot Semantic Segmentation,
IP(32), 2023, pp. 3311-3323.
IEEE DOI 2307
Transformers, Semantic segmentation, Calibration, Training, Task analysis, Prototypes, Adaptation models, feature-score cross-covariance transformer BibRef

Wang, B.[Bin], Ma, G.R.[Guo-Rui], Sui, H.G.[Hai-Gang], Zhang, Y.X.[Yong-Xian], Zhang, H.M.[Hai-Ming], Zhou, Y.[Yuan],
Few-Shot Object Detection in Remote Sensing Imagery via Fuse Context Dependencies and Global Features,
RS(15), No. 14, 2023, pp. 3462.
DOI Link 2307
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Lang, C.B.[Chun-Bo], Cheng, G.[Gong], Tu, B.F.[Bin-Fei], Li, C.[Chao], Han, J.W.[Jun-Wei],
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PAMI(45), No. 9, September 2023, pp. 10669-10686.
IEEE DOI 2309
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Zhou, D.W.[Da-Wei], Ye, H.J.[Han-Jia], Ma, L.[Liang], Xie, D.[Di], Pu, S.L.[Shi-Liang], Zhan, D.C.[De-Chuan],
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks,
PAMI(45), No. 11, November 2023, pp. 12816-12831.
IEEE DOI 2310
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Zhou, D.W.[Da-Wei], Wang, F.Y.[Fu-Yun], Ye, H.J.[Han-Jia], Ma, L.[Liang], Pu, S.L.[Shi-Liang], Zhan, D.C.[De-Chuan],
Forward Compatible Few-Shot Class-Incremental Learning,
CVPR22(9036-9046)
IEEE DOI 2210
Training, Learning systems, Computational modeling, Prototypes, Resists, Machine learning, Predictive models, Transfer/low-shot/long-tail learning BibRef

Lang, C.[Chunbo], Cheng, G.[Gong], Tu, B.[Binfei], Li, C.[Chao], Han, J.W.[Jun-Wei],
Retain and Recover: Delving Into Information Loss for Few-Shot Segmentation,
IP(32), 2023, pp. 5353-5365.
IEEE DOI Code:
WWW Link. 2310
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Xiong, P.W.[Peng-Wen], Tong, X.B.[Xiao-Bao], Liu, P.X.[Peter X.], Song, A.[Aiguo], Li, Z.J.[Zhi-Jun],
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SMCS(53), No. 10, October 2023, pp. 6119-6131.
IEEE DOI 2310
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Chen, Z.H.[Zhi-Hao], Mao, Y.C.[Ying-Chi], Qian, Y.[Yong], Pan, Z.X.[Zhen-Xiang], Xu, S.F.[Shu-Fang],
FRDet: Few-shot object detection via feature reconstruction,
IET-IPR(17), No. 12, 2023, pp. 3599-3615.
DOI Link 2310
object detection, few-shot learning, metric learning, feature reconstruction BibRef

Huang, J.[Junying], Cao, J.H.[Jun-Hao], Lin, L.[Liang], Zhang, D.[Dongyu],
IRA-FSOD: Instant-Response and Accurate Few-Shot Object Detector,
CirSysVideo(33), No. 11, November 2023, pp. 6912-6923.
IEEE DOI 2311
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Yang, Z.Y.[Zhen-Yu], Zhang, Y.X.[Yong-Xin], Zheng, J.[Jv], Yu, Z.B.[Zhi-Bin], Zheng, B.[Bing],
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RS(15), No. 22, 2023, pp. 5372.
DOI Link 2311
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Zhao, X.W.[Xiao-Wei], Liu, X.L.[Xiang-Long], Ma, Y.Q.[Yu-Qing], Bai, S.H.[Shi-Hao], Shen, Y.F.[Yi-Fan], Hao, Z.[Zeyu], Liu, A.[Aishan],
Temporal Speciation Network for Few-Shot Object Detection,
MultMed(25), 2023, pp. 8267-8278.
IEEE DOI 2312
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Vu, A.K.N.[Anh-Khoa Nguyen], Do, T.T.[Thanh-Toan], Nguyen, N.D.[Nhat-Duy], Nguyen, V.T.[Vinh-Tiep], Ngo, T.D.[Thanh Duc], Nguyen, T.V.[Tam V.],
Instance-Level Few-Shot Learning With Class Hierarchy Mining,
IP(32), 2023, pp. 2374-2385.
IEEE DOI 2305
Feature extraction, Training, Data mining, Training data, Task analysis, Proposals, Object detection, Few-shot learning, hierarchical information BibRef

Xu, J.[Jinbo], Wang, Y.[Yong], He, X.Y.[Xiao-Yu], Zou, Y.Q.[Yi-Qun],
Support-Query Mutual Promotion and Classification Correction Network for Few-Shot Object Detection,
SPLetters(31), 2024, pp. 201-205.
IEEE DOI 2401
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Zhai, W.[Wei], Wu, P.Y.[Ping-Yu], Zhu, K.[Kai], Cao, Y.[Yang], Wu, F.[Feng], Zha, Z.J.[Zheng-Jun],
Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation,
IJCV(132), No. 3, March 2024, pp. 750-775.
Springer DOI 2402
BibRef
Earlier: A2, A1, A4, Only:
Background Activation Suppression for Weakly Supervised Object Localization,
CVPR22(14228-14237)
IEEE DOI 2210
Location awareness, Correlation, Codes, Generators, Pattern recognition, Task analysis, Recognition: detection, Self- semi- meta- unsupervised learning BibRef

Meng, M.[Meng], Zhang, T.Z.[Tian-Zhu], Tian, Q.[Qi], Zhang, Y.D.[Yong-Dong], Wu, F.[Feng],
Foreground Activation Maps for Weakly Supervised Object Localization,
ICCV21(3365-3375)
IEEE DOI 2203
Location awareness, Scalability, Computational modeling, Benchmark testing, Task analysis, Standards, Recognition and classification BibRef

Huang, Q.[Qihan], Zhang, H.[Haofei], Xue, M.Q.[Meng-Qi], Song, J.[Jie], Song, M.L.[Ming-Li],
A Survey of Deep Learning for Low-shot Object Detection,
Surveys(56), No. 5, November 2023, pp. xx-yy.
DOI Link 2402
meta-learning, transfer-learning, zero-shot object detection, one-shot object detection, Few-shot object detection BibRef

Ding, J.[Jun], Zhang, Z.[Zhen], Wang, Q.Y.[Qi-Yu], Wang, H.B.[Hui-Bin],
SCTrans: Self-align and cross-align transformer for few-shot segmentation,
IVC(142), 2024, pp. 104893.
Elsevier DOI 2402
Semantic segmentation, Few-shot learning, Few-shot segmentation BibRef

Lang, C.B.[Chun-Bo], Cheng, G.[Gong], Tu, B.F.[Bin-Fei], Han, J.W.[Jun-Wei],
Few-Shot Segmentation via Divide-and-Conquer Proxies,
IJCV(132), No. 1, January 2024, pp. 261-283.
Springer DOI 2402
BibRef

Chen, Y.[Yadang], Chen, S.[Sihan], Yang, Z.X.[Zhi-Xin], Wu, E.[Enhua],
Learning self-target knowledge for few-shot segmentation,
PR(149), 2024, pp. 110266.
Elsevier DOI 2403
Few-shot segmentation, Two-level similarity matching, Step-by-step mining, Attention mechanism BibRef

Lu, Y.[Yue], Chen, X.Y.[Xing-Yu], Wu, Z.X.[Zheng-Xing], Tan, M.[Min], Yu, J.Z.[Jun-Zhi],
Binary Similarity Few-Shot Object Detection With Modeling of Hard Negative Samples,
MultMed(26), 2024, pp. 4805-4818.
IEEE DOI 2403
Head, Finite element analysis, Object detection, Detectors, Feature extraction, Training, Proposals, Few-shot learning, deep learning BibRef


Moon, S.[Seonghyeon], Sohn, S.S.[Samuel S.], Zhou, H.[Honglu], Yoon, S.[Sejong], Pavlovic, V.[Vladimir], Khan, M.H.[Muhammad Haris], Kapadia, M.[Mubbasir],
MSI: Maximize Support-Set Information for Few-Shot Segmentation,
ICCV23(19209-19219)
IEEE DOI Code:
WWW Link. 2401
BibRef

Liu, J.[Jie], Du, Y.J.[Ying-Jun], Xiao, Z.[Zehao], Snoek, C.G.M.[Cees G.M], Sonke, J.J.[Jan-Jakob], Gavves, E.[Efstratios],
Memory-augmented Variational Adaptation for Online Few-shot Segmentation,
VCL23(3316-3325)
IEEE DOI 2401
BibRef

Shangguan, Z.[Zeyu], Rostami, M.[Mohammad],
Identification of Novel Classes for Improving Few-Shot Object Detection,
VCL23(3348-3358)
IEEE DOI Code:
WWW Link. 2401
BibRef

Du, J.H.[Jin-Hao], Zhang, S.[Shan], Chen, Q.[Qiang], Le, H.F.[Hai-Feng], Sun, Y.P.[Yan-Peng], Ni, Y.[Yao], Wang, J.[Jian], He, B.[Bin], Wang, J.D.[Jing-Dong],
sigma-Adaptive Decoupled Prototype for Few-Shot Object Detection,
ICCV23(18904-18914)
IEEE DOI 2401
BibRef

Yu, Z.M.[Zhi-Miao], Lin, T.C.[Tian-Cheng], Xu, Y.[Yi],
Background Clustering Pre-Training for Few-Shot Segmentation,
ICIP23(1695-1699)
IEEE DOI Code:
WWW Link. 2312
BibRef

Peng, B.[Bohao], Tian, Z.[Zhuotao], Wu, X.Y.[Xiao-Yang], Wang, C.Y.[Cheng-Yao], Liu, S.[Shu], Su, J.Y.[Jing-Yong], Jia, J.Y.[Jia-Ya],
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation,
CVPR23(23641-23651)
IEEE DOI 2309
BibRef

Ma, J.W.[Jia-Wei], Niu, Y.[Yulei], Xu, J.C.[Jin-Cheng], Huang, S.Y.[Shi-Yuan], Han, G.X.[Guang-Xing], Chang, S.F.[Shih-Fu],
DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection,
CVPR23(3208-3218)
IEEE DOI 2309
BibRef

Xu, J.Y.[Jing-Yi], Le, H.[Hieu], Samaras, D.[Dimitris],
Generating Features with Increased Crop-Related Diversity for Few-Shot Object Detection,
CVPR23(19713-19722)
IEEE DOI 2309
BibRef

Lin, S.B.[Shao-Bo], Wang, K.[Kun], Zeng, X.Y.[Xing-Yu], Zhao, R.[Rui],
Explore the Power of Synthetic Data on Few-shot Object Detection,
GCV23(638-647)
IEEE DOI 2309
BibRef

Lin, S.B.[Shao-Bo], Wang, K.[Kun], Zeng, X.Y.[Xing-Yu], Zhao, R.[Rui],
An Effective Crop-Paste Pipeline for Few-shot Object Detection,
L3D-IVU23(4820-4828)
IEEE DOI 2309
BibRef

Demirel, B.[Berkan], Baran, O.B.[Orhun Bugra], Cinbis, R.G.[Ramazan Gokberk],
Meta-Tuning Loss Functions and Data Augmentation for Few-Shot Object Detection,
CVPR23(7339-7349)
IEEE DOI 2309
BibRef

Guirguis, K.[Karim], Meier, J.[Johannes], Eskandar, G.[George], Kayser, M.[Matthias], Yang, B.[Bin], Beyerer, J.[Jürgen],
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging,
CVPR23(24193-24202)
IEEE DOI 2309
BibRef

Wang, Y.[Yuan], Sun, R.[Rui], Zhang, T.Z.[Tian-Zhu],
Rethinking the Correlation in Few-Shot Segmentation: A Buoys View,
CVPR23(7183-7192)
IEEE DOI 2309
BibRef

Lin, S.B.[Shao-Bo], Zeng, X.Y.[Xing-Yu], Yan, S.L.[Shi-Lin], Zhao, R.[Rui],
Three-stage Training Pipeline with Patch Random Drop for Few-shot Object Detection,
ACCV22(VI:286-302).
Springer DOI 2307
BibRef

Fan, Q.[Qi], Tang, C.K.[Chi-Keung], Tai, Y.W.[Yu-Wing],
Few-Shot Object Detection with Model Calibration,
ECCV22(XIX:720-739).
Springer DOI 2211
BibRef

Fan, Q.[Qi], Zhuo, W., Tang, C.K.[Chi-Keung], Tai, Y.W.[Yu-Wing],
Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector,
CVPR20(4012-4021)
IEEE DOI 2008
Object detection, Training, Task analysis, Detectors, Proposals, Semantics BibRef

Huang, K.[Kai], Cheng, M.F.[Ming-Fei], Wang, Y.[Yang], Wang, B.C.[Bo-Chen], Xi, Y.[Ye], Wang, F.[Feigege], Chen, P.[Peng],
A Joint Framework Towards Class-aware and Class-Agnostic Alignment for Few-Shot Segmentation,
ACCV22(VII:431-447).
Springer DOI 2307
BibRef

Chen, S.[Song], Wang, C.[Chong], Liu, W.J.[Wei-Jie], Ye, Z.J.[Zheng-Jie], Deng, J.C.[Jia-Cheng],
Pseudo-label Diversity Exploitation for Few-shot Object Detection,
MMMod23(II: 289-300).
Springer DOI 2304
BibRef

Jiang, X.Y.[Xin-Yu], Li, Z.J.[Zheng-Jia], Tian, M.[Maoqing], Liu, J.B.[Jian-Bo], Yi, S.[Shuai], Miao, D.Q.[Duo-Qian],
Few-shot Object Detection via Improved Classification Features,
WACV23(5375-5384)
IEEE DOI 2302
Adaptation models, Computational modeling, Object detection, Benchmark testing, Feature extraction, visual reasoning BibRef

Guirguis, K.[Karim], Abdelsamad, M.[Mohamed], Eskandar, G.[George], Hendawy, A.[Ahmed], Kayser, M.[Matthias], Yang, B.[Bin], Beyerer, J.[Juergen],
Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors,
WACV23(3749-3758)
IEEE DOI 2302
Training, Annotations, Object detection, Detectors, Benchmark testing, Data models, visual reasoning BibRef

Kayabasi, A.[Alper], Tüfekci, G.[Gülin], Ulusoy, I.[Ilkay],
Elimination of Non-Novel Segments at Multi-Scale for Few-Shot Segmentation,
WACV23(2558-2566)
IEEE DOI 2302
Training, Image segmentation, Computational modeling, Predictive models, Benchmark testing, Ensemble learning, Biomedical/healthcare/medicine BibRef

Matcovici, S.[Stefan], Voinea, D.[Daniel], Popa, A.I.[Alin-Ionut],
k-NN embeded space conditioning for enhanced few-shot object detection,
Novelty23(401-410)
IEEE DOI 2302
Training, Visualization, Conferences, Object detection, Predictive models, Benchmark testing BibRef

Kim, S.[Sueyeon], Nam, W.J.[Woo-Jeoung], Lee, S.W.[Seong-Whan],
Few-Shot Object Detection with Proposal Balance Refinement,
ICPR22(4700-4707)
IEEE DOI 2212
Object detection, Detectors, Feature extraction, Proposals, few-shot learning, object detection BibRef

Guan, H.Y.[Hao-Yan], Michael, S.[Spratling],
CobNet: Cross Attention on Object and Background for Few-Shot Segmentation,
ICPR22(39-45)
IEEE DOI 2212
Image segmentation, Annotations, Benchmark testing, Feature extraction, Data mining, Object recognition, Standards BibRef

Wu, S.[Shuang], Pei, W.J.[Wen-Jie], Mei, D.[Dianwen], Chen, F.L.[Fang-Lin], Tian, J.[Jiandong], Lu, G.M.[Guang-Ming],
Multi-faceted Distillation of Base-Novel Commonality for Few-Shot Object Detection,
ECCV22(IX:578-594).
Springer DOI 2211
BibRef

Li, B.[Bowen], Wang, C.[Chen], Reddy, P.[Pranay], Kim, S.[Seungchan], Scherer, S.[Sebastian],
AirDet: Few-Shot Detection Without Fine-Tuning for Autonomous Exploration,
ECCV22(XXIX:427-444).
Springer DOI 2211
BibRef

Johnander, J.[Joakim], Edstedt, J.[Johan], Felsberg, M.[Michael], Khan, F.S.[Fahad Shahbaz], Danelljan, M.[Martin],
Dense Gaussian Processes for Few-Shot Segmentation,
ECCV22(XXIX:217-234).
Springer DOI 2211
BibRef

Pathiraja, B.[Bimsara], Gunawardhana, M.[Malitha], Khan, M.H.[Muhammad Haris],
Multiclass Confidence and Localization Calibration for Object Detection,
CVPR23(19734-19743)
IEEE DOI 2309
BibRef

Moon, S.[Seonghyeon], Sohn, S.S.[Samuel S.], Zhou, H.[Honglu], Yoon, S.[Sejong], Pavlovic, V.[Vladimir], Khan, M.H.[Muhammad Haris], Kapadia, M.[Mubbasir],
HM: Hybrid Masking for Few-Shot Segmentation,
ECCV22(XX:506-523).
Springer DOI 2211
BibRef

Zhang, S.[Shan], Murray, N.[Naila], Wang, L.[Lei], Koniusz, P.[Piotr],
Time-rEversed DiffusioN tEnsor Transformer: A New TENET of Few-Shot Object Detection,
ECCV22(XX:310-328).
Springer DOI 2211
BibRef

Lee, K.[Kibok], Yang, H.[Hao], Chakraborty, S.[Satyaki], Cai, Z.W.[Zhao-Wei], Swaminathan, G.[Gurumurthy], Ravichandran, A.[Avinash], Dabeer, O.[Onkar],
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark,
ECCV22(XX:366-382).
Springer DOI 2211
BibRef

Ma, T.X.[Tian-Xue], Bi, M.W.[Ming-Wei], Zhang, J.[Jian], Yuan, W.[Wang], Zhang, Z.Z.[Zhi-Zhong], Xie, Y.[Yuan], Ding, S.H.[Shou-Hong], Ma, L.Z.[Li-Zhuang],
Mutually Reinforcing Structure with Proposal Contrastive Consistency for Few-Shot Object Detection,
ECCV22(XX:400-416).
Springer DOI 2211
BibRef

Gao, Y.P.[Yi-Peng], Yang, L.X.[Ling-Xiao], Huang, Y.[Yunmu], Xie, S.[Song], Li, S.Y.[Shi-Yong], Zheng, W.S.[Wei-Shi],
AcroFOD: An Adaptive Method for Cross-Domain Few-Shot Object Detection,
ECCV22(XXXIII:673-690).
Springer DOI 2211
BibRef

Yoo, J.[Jayeon], Chung, I.[Inseop], Kwak, N.[Nojun],
Unsupervised Domain Adaptation for One-Stage Object Detector Using Offsets to Bounding Box,
ECCV22(XXXIII:691-708).
Springer DOI 2211
BibRef

Fan, Q.[Qi], Tang, C.K.[Chi-Keung], Tai, Y.W.[Yu-Wing],
Few-Shot Video Object Detection,
ECCV22(XX:76-98).
Springer DOI 2211
BibRef

Guirguis, K.[Karim], Hendawy, A.[Ahmed], Eskandar, G.[George], Abdelsamad, M.[Mohamed], Kayser, M.[Matthias], Beyerer, J.[Jürgen],
CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection,
L3D-IVU22(4038-4048)
IEEE DOI 2210
Learning systems, Adaptation models, Object detection, Search problems, Pattern recognition BibRef

Zhang, S.[Shan], Wang, L.[Lei], Murray, N.[Naila], Koniusz, P.[Piotr],
Kernelized Few-shot Object Detection with Efficient Integral Aggregation,
CVPR22(19185-19194)
IEEE DOI 2210
Image coding, Costs, Pipelines, Object detection, Detectors, Feature extraction, Representation learning, retrieval BibRef

Elezi, I.[Ismail], Yu, Z.[Zhiding], Anandkumar, A.[Anima], Leal-Taixé, L.[Laura], Alvarez, J.M.[Jose M.],
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection,
CVPR22(14472-14481)
IEEE DOI 2210
Training, Costs, Uncertainty, Neural networks, Object detection, Detectors, Robustness, Self- semi- meta- Emergency Reviews BibRef

Li, H.J.[Han-Jun], Pan, X.J.[Xing-Jia], Yan, K.[Ke], Tang, F.[Fan], Zheng, W.S.[Wei-Shi],
SIOD: Single Instance Annotated Per Category Per Image for Object Detection,
CVPR22(14177-14186)
IEEE DOI 2210
Location awareness, Costs, Annotations, Object detection, Solids, Reliability, Recognition: detection, categorization, retrieval, Self- semi- meta- unsupervised learning BibRef

Ma, J.W.[Jia-Wei], Han, G.X.[Guang-Xing], Huang, S.Y.[Shi-Yuan], Yang, Y.C.[Yun-Cong], Chang, S.F.[Shih-Fu],
Few-Shot End-to-End Object Detection via Constantly Concentrated Encoding Across Heads,
ECCV22(XXVI:57-73).
Springer DOI 2211
BibRef

Han, G.X.[Guang-Xing], Ma, J.W.[Jia-Wei], Huang, S.Y.[Shi-Yuan], Chen, L.[Long], Chang, S.F.[Shih-Fu],
Few-Shot Object Detection with Fully Cross-Transformer,
CVPR22(5311-5320)
IEEE DOI 2210
Training, Visualization, Head, Aggregates, Object detection, Benchmark testing, Feature extraction, Recognition: detection, Transfer/low-shot/long-tail learning BibRef

Kaul, P.[Prannay], Xie, W.[Weidi], Zisserman, A.[Andrew],
Label, Verify, Correct: A Simple Few Shot Object Detection Method,
CVPR22(14217-14227)
IEEE DOI 2210
Training, Detectors, Object detection, Predictive models, Benchmark testing, Solids, Recognition: detection, categorization, Self- semi- meta- Transfer/low-shot/long-tail learning BibRef

Yang, H.Q.[Han-Qing], Cai, S.[Sijia], Sheng, H.[Hualian], Deng, B.[Bing], Huang, J.Q.[Jian-Qiang], Hua, X.S.[Xian-Sheng], Tang, Y.[Yong], Zhang, Y.[Yu],
Balanced and Hierarchical Relation Learning for One-shot Object Detection,
CVPR22(7581-7590)
IEEE DOI 2210
Training, Deep learning, Computational modeling, Semantics, Detectors, Object detection, Boosting, Recognition: detection, Transfer/low-shot/long-tail learning BibRef

Huang, P.L.[Pei-Liang], Han, J.W.[Jun-Wei], Cheng, D.[De], Zhang, D.W.[Ding-Wen],
Robust Region Feature Synthesizer for Zero-Shot Object Detection,
CVPR22(7612-7621)
IEEE DOI 2210
Visualization, Synthesizers, Semantics, Object detection, Detectors, Feature extraction, Recognition: detection, categorization, Transfer/low-shot/long-tail learning BibRef

Yin, L.[Li], Perez-Rua, J.M.[Juan M], Liang, K.J.[Kevin J],
Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection,
CVPR22(9025-9035)
IEEE DOI 2210
Training, Location awareness, Deep learning, Fuses, Object detection, Detectors, retrieval, categorization, Recognition: detection BibRef

Hersche, M.[Michael], Karunaratne, G.[Geethan], Cherubini, G.[Giovanni], Benini, L.[Luca], Sebastian, A.[Abu], Rahimi, A.[Abbas],
Constrained Few-shot Class-incremental Learning,
CVPR22(9047-9057)
IEEE DOI 2210
Training, Representation learning, Costs, Computational modeling, Memory management, Interference, Representation learning BibRef

Tang, Y.M.[Yu-Ming], Peng, Y.X.[Yi-Xing], Zheng, W.S.[Wei-Shi],
Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data,
CVPR22(9539-9548)
IEEE DOI 2210
Training, Deep learning, Costs, Computational modeling, Semantics, Neural networks, Recognition: detection, categorization, retrieval BibRef

Wu, T.Y.[Tz-Ying], Swaminathan, G.[Gurumurthy], Li, Z.Z.[Zhi-Zhong], Ravichandran, A.[Avinash], Vasconcelos, N.M.[Nuno M.], Bhotika, R.[Rahul], Soatto, S.[Stefano],
Class-Incremental Learning with Strong Pre-trained Models,
CVPR22(9591-9600)
IEEE DOI 2210
Training, Adaptation models, Computational modeling, Cloning, Pattern recognition, Recognition: detection, categorization, retrieval BibRef

Dong, J.H.[Jia-Hua], Wang, L.[Lixu], Fang, Z.[Zhen], Sun, G.[Gan], Xu, S.C.[Shi-Chao], Wang, X.[Xiao], Zhu, Q.[Qi],
Federated Class-Incremental Learning,
CVPR22(10154-10163)
IEEE DOI 2210
Training, Privacy, Federated learning, Computational modeling, Prototypes, Benchmark testing, Propagation losses, Transfer/low-shot/long-tail learning BibRef

Liu, H.[Huan], Gu, L.[Li], Chi, Z.X.[Zhi-Xiang], Wang, Y.[Yang], Yu, Y.H.[Yuan-Hao], Chen, J.[Jun], Tang, J.[Jin],
Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay,
ECCV22(XXIV:146-162).
Springer DOI 2211
BibRef

Chi, Z.X.[Zhi-Xiang], Gu, L.[Li], Liu, H.[Huan], Wang, Y.[Yang], Yu, Y.H.[Yuan-Hao], Tang, J.[Jin],
MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning,
CVPR22(14146-14155)
IEEE DOI 2210
Training, Adaptation models, Protocols, Modulation, Bidirectional control, Power capacitors, Pattern recognition, Recognition: detection BibRef

Xie, J.W.[Jiang-Wei], Yan, S.P.[Shi-Peng], He, X.M.[Xu-Ming],
General Incremental Learning with Domain-aware Categorical Representations,
CVPR22(14331-14340)
IEEE DOI 2210
Learning systems, Mixture models, Benchmark testing, Pattern recognition, Complexity theory, Faces, retrieval, Recognition: detection BibRef

Kang, M.S.[Min-Soo], Park, J.[Jaeyoo], Han, B.H.[Bo-Hyung],
Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation,
CVPR22(16050-16059)
IEEE DOI 2210
Representation learning, Deep learning, Upper bound, Neural networks, Linear programming, Robustness, Representation learning BibRef

Toldo, M.[Marco], Ozay, M.[Mete],
Bring Evanescent Representations to Life in Lifelong Class Incremental Learning,
CVPR22(16711-16720)
IEEE DOI 2210
Training, Representation learning, Deep learning, Analytical models, Computational modeling, Semantics, Data models, Transfer/low-shot/long-tail learning BibRef

Villa, A.[Andrés], Alhamoud, K.[Kumail], Escorcia, V.[Victor], Heilbron, F.C.[Fabian Caba], Alcázar, J.L.[Juan León], Ghanem, B.[Bernard],
vCLIMB: A Novel Video Class Incremental Learning Benchmark,
CVPR22(19013-19022)
IEEE DOI 2210
Learning systems, Analytical models, Codes, Training data, Benchmark testing, Pattern recognition, Datasets and evaluation BibRef

Madhu, P.[Prathmesh], Meyer, A.[Anna], Zinnen, M.[Mathias], Mührenberg, L.[Lara], Suckow, D.[Dirk], Bendschus, T.[Torsten], Reinhardt, C.[Corinna], Bell, P.[Peter], Verstegen, U.[Ute], Kosti, R.[Ronak], Maier, A.[Andreas], Christlein, V.[Vincent],
One-Shot Object Detection in Heterogeneous Artwork Datasets,
IPTA22(1-6)
IEEE DOI 2206
Training, Adaptation models, Visualization, Archeology, Art, Semantics, Object detection, one-shot, object detection, digital humanities, data augmentation BibRef

Kobayashi, D.[Daisuke],
Self-supervised Prototype Conditional Few-Shot Object Detection,
CIAP22(II:681-692).
Springer DOI 2205
BibRef

Bailer, W.[Werner],
Making Few-Shot Object Detection Simpler and Less Frustrating,
MMMod22(II:445-451).
Springer DOI 2203
BibRef

Wu, A.[Aming], Han, Y.[Yahong], Zhu, L.C.[Lin-Chao], Yang, Y.[Yi],
Universal-Prototype Enhancing for Few-Shot Object Detection,
ICCV21(9547-9556)
IEEE DOI 2203
Representation learning, Visualization, Prototypes, Object detection, Feature extraction, Detection and localization in 2D and 3D BibRef

Han, G.X.[Guang-Xing], He, Y.C.[Yi-Cheng], Huang, S.Y.[Shi-Yuan], Ma, J.W.[Jia-Wei], Chang, S.F.[Shih-Fu],
Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks,
ICCV21(3243-3252)
IEEE DOI 2203
Measurement, Adaptation models, Computational modeling, Message passing, Image edge detection, Prototypes, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Min, J.H.[Ju-Hong], Kang, D.[Dahyun], Cho, M.[Minsu],
Hypercorrelation Squeeze for Few-Shot Segmenation,
ICCV21(6921-6932)
IEEE DOI 2203
Visualization, Image segmentation, Correlation, Tensors, Semantics, Benchmark testing, Feature extraction, Segmentation, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Qiao, L.M.[Li-Meng], Zhao, Y.X.[Yu-Xuan], Li, Z.Y.[Zhi-Yuan], Qiu, X.[Xi], Wu, J.A.[Jian-An], Zhang, C.[Chi],
DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection,
ICCV21(8661-8670)
IEEE DOI 2203
Location awareness, Visualization, Object detection, Detectors, Benchmark testing, Multitasking, Feature extraction, BibRef

Yang, S.[Shu], Zhang, L.[Lu], Qi, J.Q.[Jin-Qing], Lu, H.C.[Hu-Chuan], Wang, S.[Shuo], Zhang, X.X.[Xiao-Xing],
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation,
ICCV21(1544-1553)
IEEE DOI 2203
Training, Codes, Fuses, Collaboration, Object segmentation, Interference, Video analysis and understanding, BibRef

Shaban, A.[Amirreza], Rahimi, A.[Amir], Ajanthan, T.[Thalaiyasingam], Boots, B.[Byron], Hartley, R.I.[Richard I.],
Few-shot Weakly-Supervised Object Detection via Directional Statistics,
WACV22(1040-1049)
IEEE DOI 2202
Location awareness, Training, Semantics, Prototypes, Object detection, Gaussian distribution, Transfer, Few-shot, Semi- and Un- supervised Learning Object Detection/Recognition/Categorization BibRef

Lee, H.[Hojun], Lee, M.G.[Myung-Gi], Kwak, N.[Nojun],
Few-Shot Object Detection by Attending to Per-Sample-Prototype,
WACV22(1101-1110)
IEEE DOI 2202
Support vector machines, Codes, Prototypes, Object detection, Benchmark testing, Feature extraction, Transfer, Semi- and Un- supervised Learning Object Detection/Recognition/Categorization BibRef

Lee, Y.H.[Yuan-Hao], Yang, F.E.[Fu-En], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation,
WACV22(1607-1617)
IEEE DOI 2202
Training, Image segmentation, Computational modeling, Semantics, Benchmark testing, Semi- and Un- supervised Learning BibRef

Amac, M.S.[Mustafa Sercan], Sencan, A.[Ahmet], Baran, O.B.[Orhun Bugra], Ikizler-Cinbis, N.[Nazli], Cinbis, R.G.[Ramazan Gokberk],
MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation,
WACV22(428-438)
IEEE DOI 2202
Training, Image segmentation, Adaptation models, Semantics, Training data, Prototypes, Estimation, Transfer, Few-shot, Grouping and Shape BibRef

Zhong, C.L.[Chao-Liang], Wang, J.[Jie], Feng, C.[Cheng], Zhang, Y.[Ying], Sun, J.[Jun], Yokota, Y.[Yasuto],
PICA: Point-Wise Instance and Centroid Alignment Based Few-Shot Domain Adaptive Object Detection with Loose Annotations,
WACV22(398-407)
IEEE DOI 2202
Training, Adaptation models, Annotations, Computational modeling, Object detection, Predictive models, Object Detection/Recognition/Categorization BibRef

Tsironis, V., Stentoumis, C., Lekkas, N., Nikopoulos, A.,
Scale-awareness for More Accurate Object Detection Using Modified Single Shot Detectors,
ISPRS21(B2-2021: 801-808).
DOI Link 2201
BibRef

Chu, J.H.[Jing-Hui], Feng, J.W.[Jia-Wei], Jing, P.G.[Pei-Guang], Lu, W.[Wei],
Joint Co-Attention and Co-Reconstruction Representation Learning for One-Shot Object Detection,
ICIP21(2229-2233)
IEEE DOI 2201
Training, Degradation, Correlation, Object detection, Feature extraction, Proposals, Object detection, one-shot learning, low-rank co-reconstruction BibRef

Zheng, Y.[Ye], Cui, L.[Li],
Zero-Shot Object Detection With Transformers,
ICIP21(444-448)
IEEE DOI 2201
Deep learning, Head, Image processing, Object detection, Benchmark testing, Natural language processing, Zero-Shot Learning BibRef

Luo, X.L.[Xiao-Liu], Zhang, T.P.[Tai-Ping],
Graph Affinity Network for Few-Shot Segmentation,
ICIP21(609-613)
IEEE DOI 2201
Image segmentation, Annotations, Semantics, graph convolutional network, graph affinity, few-shot segmentation BibRef

Wang, Y.[Yu], Zhang, Y.[Ye], Zhai, S.H.[Shao-Hua], Chen, H.[Hao], Shi, S.Q.[Shao-Qi], Wang, G.[Gang],
Deep Sensor Fusion Based on Frustum Point Single Shot Multibox Detector for 3D Object Detection,
ICIP21(674-678)
IEEE DOI 2201
Location awareness, Degradation, Image segmentation, Semantics, Detectors, Semantic segmentation, frustum point cloud, object detection BibRef

Erabati, G.K.[Gopi Krishna], Araujo, H.[Helder],
SL3D: Single Look 3D Object Detection based on RGB-D Images,
DICTA20(1-8)
IEEE DOI 2201
Fuses, Shape, Object detection, Feature extraction, Real-time systems, Sun, Object detection, RGB-D, CNN BibRef

Wolf, S.[Stefan], Meier, J.[Jonas], Sommer, L.[Lars], Beyerer, J.[Jürgen],
Double Head Predictor based Few-Shot Object Detection for Aerial Imagery,
LUAI21(721-731)
IEEE DOI 2112

WWW Link. Code, Object Detection. Training, Head, Codes, Annotations, Training data BibRef

Fan, Z.B.[Zhi-Bo], Ma, Y.[Yuchen], Li, Z.[Zeming], Sun, J.[Jian],
Generalized Few-Shot Object Detection without Forgetting,
CVPR21(4525-4534)
IEEE DOI 2111
Measurement, Transfer learning, Object detection, Detectors, Benchmark testing, Reliability engineering BibRef

Li, A.[Aoxue], Li, Z.G.[Zhen-Guo],
Transformation Invariant Few-Shot Object Detection,
CVPR21(3093-3101)
IEEE DOI 2111
Object detection, Detectors, Predictive models, Boosting, Data models, Pattern recognition BibRef

Zhu, C.C.[Chen-Chen], Chen, F.[Fangyi], Ahmed, U.[Uzair], Shen, Z.Q.[Zhi-Qiang], Savvides, M.[Marios],
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection,
CVPR21(8778-8787)
IEEE DOI 2111
Visualization, Protocols, Semantics, Object detection, Detectors, Image representation BibRef

Zhang, L.[Lu], Zhou, S.[Shuigeng], Guan, J.H.[Ji-Hong], Zhang, J.[Ji],
Accurate Few-shot Object Detection with Support-Query Mutual Guidance and Hybrid Loss,
CVPR21(14419-14427)
IEEE DOI 2111
Measurement, Training, Training data, Object detection, Detectors, Pattern recognition BibRef

Li, Y.T.[Yi-Ting], Zhu, H.Y.[Hai-Yue], Cheng, Y.[Yu], Wang, W.X.[Wen-Xin], Teo, C.S.[Chek Sing], Xiang, C.[Cheng], Vadakkepat, P.[Prahlad], Lee, T.H.[Tong Heng],
Few-Shot Object Detection via Classification Refinement and Distractor Retreatment,
CVPR21(15390-15398)
IEEE DOI 2111
Training, Degradation, Filtering, Detectors, Object detection, Boosting BibRef

Khandelwal, S.[Siddhesh], Goyal, R.[Raghav], Sigal, L.[Leonid],
UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation,
CVPR21(5947-5957)
IEEE DOI 2111
Training, Visualization, Image segmentation, Computational modeling, Taxonomy, Refining BibRef

Zhang, W.[Weilin], Wang, Y.X.[Yu-Xiong],
Hallucination Improves Few-Shot Object Detection,
CVPR21(13003-13012)
IEEE DOI 2111
Training, Computational modeling, Training data, Object detection, Detectors, Benchmark testing BibRef

Chen, D.J.[Ding-Jie], Hsieh, H.Y.[He-Yen], Liu, T.L.[Tyng-Luh],
Adaptive Image Transformer for One-Shot Object Detection,
CVPR21(12242-12251)
IEEE DOI 2111
Adaptation models, Training data, Object detection, Predictive models, Feature extraction, Transformers, Pattern recognition BibRef

Ying, X.W.[Xiao-Wen], Li, X.[Xin], Chuah, M.C.[Mooi Choo],
Weakly-Supervised Object Representation Learning for Few-Shot Semantic Segmentation,
WACV21(1496-1505)
IEEE DOI 2106
Training, Image segmentation, Annotations, Semantics, Benchmark testing BibRef

Cheng, Y.[Yuan], Yang, Y.C.[Yu-Chao], Chen, H.B.[Hai-Bao], Wong, N.[Ngai], Yu, H.[Hao],
S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation,
WACV21(3328-3336)
IEEE DOI 2106
Quantization (signal), Computational modeling, Semantics, Graphics processing units, Streaming media, Feature extraction, Rendering (computer graphics) BibRef

Agarwal, S.[Shivang], Jurie, F.[Frederic],
Hierarchical Head Design for Object Detectors,
ICPR21(4981-4988)
IEEE DOI 2105
Training, Head, Detectors, Object detection, Performance gain, Feature extraction, 2D Object Detection, Deep Learning BibRef

Orfanidis, G.[Georgios], Ioannidis, K.[Konstantinos], Vrochidis, S.[Stefanos], Tefas, A.[Anastasios], Kompatsiaris, I.[Ioannis],
A modified Single-Shot multibox Detector for beyond Real-Time Object Detection,
ICPR21(3977-3984)
IEEE DOI 2105
Detectors, Object detection, Real-time systems, Timing BibRef

Zhang, S.[Shan], Luo, D.W.[Da-Wei], Wang, L.[Lei], Koniusz, P.[Piotr],
Few-shot Object Detection by Second-order Pooling,
ACCV20(IV:369-387).
Springer DOI 2103
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Zheng, Y.[Ye], Huang, R.[Ruoran], Han, C.Q.[Chuan-Qi], Huang, X.[Xi], Cui, L.[Li],
Background Learnable Cascade for Zero-shot Object Detection,
ACCV20(III:107-123).
Springer DOI 2103
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Hayat, N.[Nasir], Hayat, M.[Munawar], Rahman, S.[Shafin], Khan, S.[Salman], Zamir, S.W.[Syed Waqas], Khan, F.S.[Fahad Shahbaz],
Synthesizing the Unseen for Zero-shot Object Detection,
ACCV20(III:155-170).
Springer DOI 2103
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Osokin, A.[Anton], Sumin, D.[Denis], Lomakin, V.[Vasily],
Os2d: One-stage One-shot Object Detection by Matching Anchor Features,
ECCV20(XV:635-652).
Springer DOI 2011
detecting objects defined by a single demonstration. BibRef

Wu, J.X.[Jia-Xi], Liu, S.T.[Song-Tao], Huang, D.[Di], Wang, Y.H.[Yun-Hong],
Multi-scale Positive Sample Refinement for Few-shot Object Detection,
ECCV20(XVI: 456-472).
Springer DOI 2010
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Jang, H., Woo, S., Benz, P., Park, J., Kweon, I.S.,
Propose-and-Attend Single Shot Detector,
WACV20(804-813)
IEEE DOI 2006
Detectors, Training, Convolution, Proposals, Feature extraction, Standards, Computational modeling BibRef

Raza, H., Ravanbakhsh, M., Klein, T., Nabi, M.,
Weakly Supervised One Shot Segmentation,
MDALC19(1401-1406)
IEEE DOI 2004
image representation, image segmentation, learning (artificial intelligence), one-shot learning, semantic segmentation BibRef

Siam, M., Oreshkin, B., Jagersand, M.,
AMP: Adaptive Masked Proxies for Few-Shot Segmentation,
ICCV19(5248-5257)
IEEE DOI 2004
Code, Segmentation.
WWW Link. image fusion, image motion analysis, image segmentation, learning (artificial intelligence), AMP, adaptive masked proxies, Feature extraction BibRef

Yang, Y.W.[Yu-Wei], Meng, F.M.[Fan-Man], Li, H.L.[Hong-Liang], Wu, Q.B.[Qing-Bo], Xu, X.L.[Xiao-Long], Chen, S.[Shuai],
A New Local Transformation Module for Few-shot Segmentation,
MMMod20(II:76-87).
Springer DOI 2003
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Pérez-Rúa, J., Zhu, X., Hospedales, T.M., Xiang, T.,
Incremental Few-Shot Object Detection,
CVPR20(13843-13852)
IEEE DOI 2008
Object detection, Training, Feature extraction, Detectors, Heating systems, Generators, Robots BibRef

Wang, S., Cao, S., Wei, D., Wang, R., Ma, K., Wang, L., Meng, D., Zheng, Y.,
LT-Net: Label Transfer by Learning Reversible Voxel-Wise Correspondence for One-Shot Medical Image Segmentation,
CVPR20(9159-9168)
IEEE DOI 2008
Image segmentation, Machine learning, Medical diagnostic imaging, Training BibRef

Kang, B., Liu, Z., Wang, X., Yu, F., Feng, J., Darrell, T.J.,
Few-Shot Object Detection via Feature Reweighting,
ICCV19(8419-8428)
IEEE DOI 2004
convolutional neural nets, feature extraction, learning (artificial intelligence), object detection, Training data BibRef

Chen, S., Wang, X.,
Single-Shot Detector with Multiple Inference Paths,
ICIP19(2005-2009)
IEEE DOI 1910
Object detection, resource-constrained, deep networks BibRef

Li, W., Liu, G.,
A Single-Shot Object Detector with Feature Aggregation and Enhancement,
ICIP19(3910-3914)
IEEE DOI 1910
Real-Time object detection, feature enhancement, feature aggregation BibRef

Li, S.[Shuai], Yang, L.X.[Ling-Xiao], Huang, J.Q.[Jian-Qiang], Hua, X.S.[Xian-Sheng], Zhang, L.[Lei],
Dynamic Anchor Feature Selection for Single-Shot Object Detection,
ICCV19(6608-6617)
IEEE DOI 2004
feature extraction, feature selection, image fusion, object detection, regression analysis BibRef

He, L.Q.[Li-Qiang], Todorovic, S.[Sinisa],
DESTR: Object Detection with Split Transformer,
CVPR22(9367-9376)
IEEE DOI 2210
Visualization, Privacy, Object detection, Detectors, Performance gain, Transformers, Decoding, Recognition: detection, retrieval BibRef

Nguyen, K.[Khoi], Todorovic, S.[Sinisa],
Feature Weighting and Boosting for Few-Shot Segmentation,
ICCV19(622-631)
IEEE DOI 2004
foreground objects in images. convolutional neural nets, feature extraction, image classification, image segmentation, inference mechanisms, Computer architecture BibRef

Qiao, S.Y.[Si-Yuan], Chen, L.C.[Liang-Chieh], Yuille, A.L.[Alan L.],
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution,
CVPR21(10208-10219)
IEEE DOI 2111
Philosophical considerations, Codes, Convolution, Detectors, Switches, Object detection BibRef

Zhang, Z., Qiao, S., Xie, C., Shen, W., Wang, B., Yuille, A.L.,
Single-Shot Object Detection with Enriched Semantics,
CVPR18(5813-5821)
IEEE DOI 1812
Semantics, Feature extraction, Object detection, Image segmentation, Detectors, Task analysis, Visualization BibRef

Zhang, L.[Lu], Yang, X.[Xu], Liu, Z.Y.[Zhi-Yong], Qi, L.[Lu], Zhou, H.[Hao], Chiu, C.[Charles],
Single Shot Feature Aggregation Network for Underwater Object Detection,
ICPR18(1906-1911)
IEEE DOI 1812
Feature extraction, Object detection, Detectors, Task analysis, Training, Semantics, Convolutional neural networks BibRef

Xu, P., Zhao, X., Huang, K.,
Densely Connected Single-Shot Detector,
ICPR18(2178-2183)
IEEE DOI 1812
Feature extraction, Detectors, Object detection, Convolution, Transforms, Task analysis, Pattern recognition BibRef

Rahman, S.[Shafin], Khan, S.[Salman], Barnes, N.,
Deep0Tag: Deep Multiple Instance Learning for Zero-Shot Image Tagging,
MultMed(22), No. 1, January 2020, pp. 242-255.
IEEE DOI 2001
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Earlier: A1, A2, Only:
Deep Multiple Instance Learning for Zero-Shot Image Tagging,
ACCV18(I:530-546).
Springer DOI 1906
Deep learning, Multiple instance learning, Feature pooling, Object detection, Zero-shot tagging BibRef

Xiang, W., Zhang, D.Q., Yu, H., Athitsos, V.,
Context-Aware Single-Shot Detector,
WACV18(1784-1793)
IEEE DOI 1806
SSD object detector. convolution, object detection, ubiquitous computing, CSSD, SSD, VGGNet, context layers, Radio frequency BibRef

Woo, S.[Sanghyun], Hwang, S.[Soonmin], Kweon, I.S.[In So],
StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection,
WACV18(1093-1102)
IEEE DOI 1806
feature extraction, image classification, image representation, object detection, PASCAL VOC 2012 datasets, SSD framework, Visualization Compare to SSD and YOLO. BibRef

Hu, H., Lan, S., Jiang, Y., Cao, Z., Sha, F.,
FastMask: Segment Multi-scale Object Candidates in One Shot,
CVPR17(2280-2288)
IEEE DOI 1711
Feature extraction, Head, Image segmentation, Neck, Proposals, Semantics BibRef

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
YOLO, You Only Look Once, Family Object Detection .


Last update:Mar 16, 2024 at 20:36:19