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

Pagès, J.[Jordi], Salvi, J.[Joaquim], Collewet, C.[Christophe], Forest, J.[Josep],
Optimised De Bruijn patterns for one-shot shape acquisition,
IVC(23), No. 8, 1 August 2005, pp. 707-720.
Elsevier DOI 0508
BibRef

Lampert, C.H.[Christoph H.], Nickisch, H.[Hannes], Harmeling, S.[Stefan],
Attribute-Based Classification for Zero-Shot Visual Object Categorization,
PAMI(36), No. 3, March 2014, pp. 453-465.
IEEE DOI 1403
BibRef
Earlier:
Learning to detect unseen object classes by between-class attribute transfer,
CVPR09(951-958).
IEEE DOI 0906
computer vision BibRef

Biswas, S.K.[Sujoy Kumar], Milanfar, P.[Peyman],
One Shot Detection with Laplacian Object and Fast Matrix Cosine Similarity,
PAMI(38), No. 3, March 2016, pp. 546-562.
IEEE DOI 1602
BibRef
Earlier:
Laplacian object: One-shot object detection by locality preserving projection,
ICIP14(4062-4066)
IEEE DOI 1502
Covariance matrices. Search for single query in larger target image. BibRef

Chen, S.Q.[Shi-Qi], Zhan, R.H.[Rong-Hui], Zhang, J.[Jun],
Geospatial Object Detection in Remote Sensing Imagery Based on Multiscale Single-Shot Detector with Activated Semantics,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Chen, Z.C.[Zheng-Chao], Lu, K.X.[Kai-Xuan], Gao, L.R.[Lian-Ru], Li, B.P.[Bai-Peng], Gao, J.W.[Jian-Wei], Yang, X.[Xuan], Yao, M.F.[Mu-Feng], Zhang, B.[Bing],
Automatic Detection of Track and Fields in China from High-Resolution Satellite Images Using Multi-Scale-Fused Single Shot MultiBox Detector,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
Complex, varied structures. BibRef

Li, X.Q.[Xiao-Qiang], Liu, C.W.[Chuan-Wei], Dai, S.M.[Song-Min], Lian, H.C.[Hui-Chen], Ding, G.T.[Guang-Tai],
Scale specified single shot multibox detector,
IET-CV(14), No. 2, March 2020, pp. 59-64.
DOI Link 2002
Detection at multiple scales. BibRef

Wang, P.J.[Pei-Jin], Sun, X.[Xian], Diao, W.H.[Wen-Hui], Fu, K.[Kun],
FMSSD: Feature-Merged Single-Shot Detection for Multiscale Objects in Large-Scale Remote Sensing Imagery,
GeoRS(58), No. 5, May 2020, pp. 3377-3390.
IEEE DOI 2005
Area-weighted, context information, one-stage, remote sensing imagery, small object detection BibRef

Yan, C.X.[Cai-Xia], Zheng, Q.H.[Qing-Hua], Chang, X.J.[Xiao-Jun], Luo, M.N.[Min-Nan], Yeh, C.H.[Chung-Hsing], Hauptman, A.G.[Alexander G.],
Semantics-Preserving Graph Propagation for Zero-Shot Object Detection,
IP(29), 2020, pp. 8163-8176.
IEEE DOI 2008
Semantics, Object detection, Task analysis, Visualization, Motorcycles, Bicycles, Correlation, Zero-shot object detection, graph propagation BibRef

Rahman, S.[Shafin], Khan, S.H.[Salman H.], Porikli, F.M.[Fatih M.],
Zero-Shot Object Detection: Joint Recognition and Localization of Novel Concepts,
IJCV(128), No. 12, December 2020, pp. 2979-2999.
Springer DOI 2010
BibRef

Chen, F.Y.[Fang-Yi], Zhu, C.C.[Chen-Chen], Shen, Z.Q.[Zhi-Qiang], Zhang, H.[Han], Savvides, M.[Marios],
NCMS: Towards accurate anchor free object detection through L2 norm calibration and multi-feature selection,
CVIU(200), 2020, pp. 103050.
Elsevier DOI 2010
Object detection, Norm calibration, Feature selection BibRef

Zhu, C.C.[Chen-Chen], He, Y.H.[Yi-Hui], Savvides, M.[Marios],
Feature Selective Anchor-Free Module for Single-Shot Object Detection,
CVPR19(840-849).
IEEE DOI 2002
BibRef

Zhu, C.C.[Chen-Chen], Chen, F.Y.[Fang-Yi], Shen, Z.Q.[Zhi-Qiang], Savvides, M.[Marios],
Soft Anchor-point Object Detection,
ECCV20(IX:91-107).
Springer DOI 2011
Boost performance of anchor-point, with same speed advantage. BibRef

Li, Y., Pang, Y., Cao, J., Shen, J., Shao, L.,
Improving Single Shot Object Detection With Feature Scale Unmixing,
IP(30), 2021, pp. 2708-2721.
IEEE DOI 2102
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],
SML: Semantic meta-learning for few-shot semantic segmentation?,
PRL(147), 2021, pp. 93-99.
Elsevier DOI 2106
Few-shot learning, Semantic segmentation, Attributes BibRef

Kim, G.[Geonuk], Jung, H.G.[Hong-Gyu], Lee, S.W.[Seong-Whan],
Spatial reasoning for few-shot object detection,
PR(120), 2021, pp. 108118.
Elsevier DOI 2109
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],
Few-Shot Object Detection on Remote Sensing Images via Shared Attention Module and Balanced Fine-Tuning Strategy,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

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],
Balanced single-shot object detection using cross-context attention-guided network,
PR(122), 2022, pp. 108258.
Elsevier DOI 2112
Cross-context attention-guided network, Cross-context attention mechanism, Accuracy and speed balance BibRef

Chen, P.Y.[Ping-Yang], Chang, M.C.[Ming-Ching], Hsieh, J.W.[Jun-Wei], Chen, Y.S.[Yong-Sheng],
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection,
IP(30), 2021, pp. 9099-9111.
IEEE DOI 2112
Training, Location awareness, Visualization, Purification, Fuses, Bidirectional control, Object detection, Feature pyramid network, feature fusion BibRef

Li, X.[Xiang], Deng, J.Y.[Jing-Yu], Fang, Y.[Yi],
Few-Shot Object Detection on Remote Sensing Images,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI 2112
Object detection, Feature extraction, Remote sensing, Proposals, Learning systems, Computer architecture, Training, You-Only-Look-Once (YOLO) 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],
Semantically Meaningful Class Prototype Learning for One-Shot Image Segmentation,
MultMed(24), 2022, pp. 968-980.
IEEE DOI 2202
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,
IVC(120), 2022, pp. 104398.
Elsevier DOI 2204
Few-shot object detection, Few-shot learning, Baby learning BibRef

Cheng, M.[Meng], Wang, H.[Hanli], Long, Y.[Yu],
Meta-Learning-Based Incremental Few-Shot Object Detection,
CirSysVideo(32), No. 4, April 2022, pp. 2158-2169.
IEEE DOI 2204
Object detection, Feature extraction, Detectors, Adaptation models, Training, Task analysis, Data models, Few-shot learning, object detection BibRef

Qu, Z.[Zhong], Shang, X.[Xue], Xia, S.F.[Shu-Fang], Yi, T.M.[Tu-Ming], Zhou, D.Y.[Dong-Yang],
A method of single-shot target detection with multi-scale feature fusion and feature enhancement,
IET-IPR(16), No. 6, 2022, pp. 1752-1763.
DOI Link 2204
BibRef

Feng, H.[Hangtao], Zhang, L.[Lu], Yang, X.[Xu], Liu, Z.Y.[Zhi-Yong],
Incremental few-shot object detection via knowledge transfer,
PRL(156), 2022, pp. 67-73.
Elsevier DOI 2205
Machine learning, Convolutional neural networks, Transfer learning, Incremental few-shot object detection BibRef

Zhang, X.S.[Xiao-Song], Wan, F.[Fang], Liu, C.[Chang], Ji, X.Y.[Xiang-Yang], Ye, Q.X.[Qi-Xiang],
Learning to Match Anchors for Visual Object Detection,
PAMI(44), No. 6, June 2022, pp. 3096-3109.
IEEE DOI 2205
Detectors, Location awareness, Feature extraction, Training, Maximum likelihood estimation, Object detection, Visualization, generalized linear model BibRef

Li, B.H.[Bo-Hao], Yang, B.[Boyu], Liu, C.[Chang], Liu, F.[Feng], Ji, R.R.[Rong-Rong], Ye, Q.X.[Qi-Xiang],
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection,
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],
CrabNet: Fully Task-Specific Feature Learning for One-Stage Object Detection,
IP(31), 2022, pp. 2962-2974.
IEEE DOI 2205
Location awareness, Task analysis, Feature extraction, Object detection, Representation learning, Detectors, Proposals, feature interaction 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.[Jianan], 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.[Richard],
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

Sun, B.[Bo], Li, B.[Banghuai], Cai, S.C.[Sheng-Cai], Yuan, Y.[Ye], Zhang, C.[Chi],
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding,
CVPR21(7348-7358)
IEEE DOI 2111
Training, Visualization, Pipelines, Object detection, Benchmark testing, Encoding, Power capacitors 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

Hu, H.Z.[Han-Zhe], Bai, S.[Shuai], Li, A.[Aoxue], Cui, J.S.[Jin-Shi], Wang, L.[Liwei],
Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection,
CVPR21(10180-10189)
IEEE DOI 2111
Training, Deep learning, Adaptation models, Codes, Annotations, Object detection 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

Wang, H.C.[Hao-Chen], Yang, Y.D.[Yan-Dan], Cao, X.B.[Xian-Bin], Zhen, X.T.[Xian-Tong], Snoek, C.[Cees], 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

Cheng, Y.[Yuan], Yang, Y.[Yuchao], 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
BibRef

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
BibRef

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
BibRef

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

d'Innocente, A.[Antonio], Borlino, F.C.[Francesco Cappio], Bucci, S.[Silvia], Caputo, B.[Barbara], Tommasi, T.[Tatiana],
One-shot Unsupervised Cross-Domain Detection,
ECCV20(XVI: 732-748).
Springer DOI 2010
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
BibRef

Liu, W., Zhang, C., Lin, G., Liu, F.,
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

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.[Yuwei], Meng, F.[Fanman], 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
BibRef

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

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

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
Instance of Particular Object, Specified Object .