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computer vision
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ICIP14(4062-4066)
IEEE DOI
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Covariance matrices. Search for single query in larger target image.
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Area-weighted, context information, one-stage,
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Semantics, Object detection, Task analysis, Visualization,
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Elsevier DOI
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Object detection, Norm calibration, Feature selection
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IEEE DOI
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BibRef
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Springer DOI
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Boost performance of anchor-point, with same speed advantage.
BibRef
Li, Y.,
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IEEE DOI
2102
Feature extraction, Detectors, Object detection, Visualization,
Semantics, Sports, Real-time systems, Object detection,
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2106
Few-shot learning, Semantic segmentation, Attributes
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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
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DOI Link
2110
BibRef
Miao, S.Y.[Shu-Yu],
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Feng, R.[Rui],
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Balanced single-shot object detection using cross-context
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Elsevier DOI
2112
Cross-context attention-guided network, Cross-context attention mechanism,
Accuracy and speed balance
BibRef
Chen, P.Y.[Ping-Yang],
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Hsieh, J.W.[Jun-Wei],
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Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate
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IP(30), 2021, pp. 9099-9111.
IEEE DOI
2112
Training, Location awareness, Visualization, Purification, Fuses,
Bidirectional control, Object detection, Feature pyramid network,
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Deng, J.Y.[Jing-Yu],
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Few-Shot Object Detection on Remote Sensing Images,
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IEEE DOI
2112
Object detection, Feature extraction, Remote sensing, Proposals,
Learning systems, Computer architecture, Training,
You-Only-Look-Once (YOLO)
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Chen, T.[Tao],
Xie, G.S.[Guo-Sen],
Yao, Y.Z.[Ya-Zhou],
Wang, Q.[Qiong],
Shen, F.M.[Fu-Min],
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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
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Nguyen, N.D.[Nhat-Duy],
Nguyen, K.D.[Khanh-Duy],
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Few-shot object detection via baby learning,
IVC(120), 2022, pp. 104398.
Elsevier DOI
2204
Few-shot object detection, Few-shot learning, Baby learning
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Cheng, M.[Meng],
Wang, H.[Hanli],
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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],
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A method of single-shot target detection with multi-scale feature
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IET-IPR(16), No. 6, 2022, pp. 1752-1763.
DOI Link
2204
BibRef
Qu, Z.[Zhong],
Gao, L.Y.[Le-Yuan],
Wang, S.Y.[Sheng-Ye],
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Yi, T.M.[Tu-Ming],
An improved YOLOv5 method for large objects detection with
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IVC(125), 2022, pp. 104518.
Elsevier DOI
2208
Object detection, Feature extraction, Feature fusion, , Autoanchor mechanism
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Feng, H.T.[Hang-Tao],
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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
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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
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Li, B.H.[Bo-Hao],
Yang, B.[Boyu],
Liu, C.[Chang],
Liu, F.[Feng],
Ji, R.R.[Rong-Rong],
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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
Wang, Y.[Yan],
Xu, C.F.[Chao-Fei],
Liu, C.W.[Cui-Wei],
Li, Z.K.[Zhao-Kui],
Context Information Refinement for Few-Shot Object Detection in
Remote Sensing Images,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Chen, S.Q.[Shi-Qi],
Zhang, J.[Jun],
Zhan, R.[Ronghui],
Zhu, R.Q.[Rong-Qiang],
Wang, W.[Wei],
Few Shot Object Detection for SAR Images via Feature Enhancement and
Dynamic Relationship Modeling,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Liu, S.[Sixu],
You, Y.[Yanan],
Su, H.Z.[Hao-Zheng],
Meng, G.[Gang],
Yang, W.[Wei],
Liu, F.[Fang],
Few-Shot Object Detection in Remote Sensing Image Interpretation:
Opportunities and Challenges,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Lu, X.K.[Xian-Kai],
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Van Gool, L.J.[Luc J.],
Segmenting Objects From Relational Visual Data,
PAMI(44), No. 11, November 2022, pp. 7885-7897.
IEEE DOI
2210
Image segmentation, Visualization, Integrated circuits,
Task analysis, Frequency selective surfaces, Semantics,
few-shot semantic segmentation
BibRef
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Tommasi, T.[Tatiana],
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Elsevier DOI
2210
Cross-domain learning, Object detection, Self-supervision,
Meta-learning, One-shot adaptation
BibRef
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Cappio Borlino, F.[Francesco],
Bucci, S.[Silvia],
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Tommasi, T.[Tatiana],
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ECCV20(XVI: 732-748).
Springer DOI
2010
BibRef
Liu, W.D.[Wei-De],
Zhang, C.[Chi],
Lin, G.S.[Guo-Sheng],
Liu, F.Y.[Fa-Yao],
CRCNet: Few-Shot Segmentation with Cross-Reference and Region-Global
Conditional Networks,
IJCV(130), No. 12, December 2022, pp. 3140-3157.
Springer DOI
2211
BibRef
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
Meneghetti, L.[Laura],
Demo, N.[Nicola],
Rozza, G.[Gianluigi],
A Proper Orthogonal Decomposition Approach for Parameters Reduction
of Single Shot Detector Networks,
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
Zeng, T.[Tao],
Xu, F.[Feng],
Lyu, X.[Xin],
Li, X.[Xin],
Wang, X.Y.[Xin-Yuan],
Chen, J.[Jiale],
Wu, C.[Caifeng],
Feature difference for single-shot object detection,
IET-IPR(16), No. 14, 2022, pp. 3876-3892.
DOI Link
2212
BibRef
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
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Sun, B.[Bo],
Li, B.H.[Bang-Huai],
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
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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IET-IPR(17), No. 1, 2023, pp. 204-214.
DOI Link
2301
BibRef
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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
Xia, R.Y.[Rui-Yang],
Li, G.Q.[Guo-Quan],
Huang, Z.W.[Zheng-Wen],
Meng, H.Y.[Hong-Ying],
Pang, Y.[Yu],
Bi-path Combination YOLO for Real-time Few-shot Object Detection,
PRL(165), 2023, pp. 91-97.
Elsevier DOI
2301
Few-shot object detection, Transfer learning, Real-time,
Bi-path Combination, You Only Look Once, Attentive DropBlock
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],
Generalized few-shot object detection in remote sensing images,
PandRS(195), 2023, pp. 353-364.
Elsevier DOI
2301
Generalized few-shot object detection, Remote sensing images,
Transfer-learning, Metric learning
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
Hong, S.[Sunghwan],
Cho, S.[Seokju],
Nam, J.[Jisu],
Lin, S.[Stephen],
Kim, S.[Seungryong],
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot
Segmentation,
ECCV22(XXIX:108-126).
Springer DOI
2211
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
Sui, L.[Lin],
Zhang, C.L.[Chen-Lin],
Wu, J.X.[Jian-Xin],
Salvage of Supervision in Weakly Supervised Object Detection,
CVPR22(14207-14216)
IEEE DOI
2210
Bridges, Visualization, Machine vision, Object detection,
Pattern recognition, Noise measurement, Recognition: detection,
Self- semi- meta- Vision applications and systems
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
Wu, P.Y.[Ping-Yu],
Zhai, W.[Wei],
Cao, Y.[Yang],
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
Xu, Y.Q.[Yun-Qiu],
Sun, Y.F.[Yi-Fan],
Yang, Z.X.[Zong-Xin],
Miao, J.[Jiaxu],
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
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
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
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.[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.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
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.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
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
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
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
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
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
Semi-Supervised Object Detection .