Hsiao, E.[Edward],
Hebert, M.[Martial],
Occlusion Reasoning for Object Detection Under Arbitrary Viewpoint,
PAMI(36), No. 9, September 2014, pp. 1803-1815.
IEEE DOI
1408
BibRef
Earlier:
CVPR12(3146-3153).
IEEE DOI
1208
BibRef
And:
Coherent Occlusion Reasoning for Instance Recognition,
ACPR13(1-5)
IEEE DOI
1408
Approximation methods.
image classification
BibRef
Oramas Mogrovejo, J.A.[José Antonio],
Tuytelaars, T.[Tinne],
Recovering hard-to-find object instances by sampling context-based
object proposals,
CVIU(152), No. 1, 2016, pp. 118-130.
Elsevier DOI
1609
Object detection
BibRef
Cinbis, R.G.[Ramazan Gokberk],
Verbeek, J.[Jakob],
Schmid, C.[Cordelia],
Weakly Supervised Object Localization with Multi-Fold Multiple
Instance Learning,
PAMI(39), No. 1, January 2017, pp. 189-203.
IEEE DOI
1612
BibRef
Earlier:
Multi-fold MIL Training for Weakly Supervised Object Localization,
CVPR14(2409-2416)
IEEE DOI
1409
BibRef
Earlier:
Segmentation Driven Object Detection with Fisher Vectors,
ICCV13(2968-2975)
IEEE DOI
1403
Computational efficiency.
object detection; object localization; weakly supervised training.
fisher vectors; object detection
See also Action and Event Recognition with Fisher Vectors on a Compact Feature Set.
BibRef
Rodrigues, E.O.[Erick O.],
Liatsis, P.[Panos],
Satoru, L.[Luiz],
Conci, A.[Aura],
Fractal triangular search: a metaheuristic for image content search,
IET-IPR(12), No. 8, August 2018, pp. 1475-1484.
DOI Link
1808
Search for specific content in images.
BibRef
Wei, X.S.[Xiu-Shen],
Zhang, C.L.[Chen-Lin],
Wu, J.X.[Jian-Xin],
Shen, C.H.[Chun-Hua],
Zhou, Z.H.[Zhi-Hua],
Unsupervised object discovery and co-localization by deep descriptor
transformation,
PR(88), 2019, pp. 113-126.
Elsevier DOI
1901
Unsupervised object discovery, Object co-localization,
Deep descriptor transformation, Pre-trained CNN models
BibRef
Chen, P.X.[Pei-Xian],
Zhang, M.D.[Meng-Dan],
Shen, Y.H.[Yun-Hang],
Sheng, K.[Kekai],
Gao, Y.T.[Yu-Ting],
Sun, X.[Xing],
Li, K.[Ke],
Shen, C.H.[Chun-Hua],
Efficient Decoder-Free Object Detection with Transformers,
ECCV22(X:70-86).
Springer DOI
2211
BibRef
Rahnemoonfar, M.[Maryam],
Dobbs, D.[Dugan],
Yari, M.[Masoud],
Starek, M.J.[Michael J.],
DisCountNet: Discriminating and Counting Network for Real-Time
Counting and Localization of Sparse Objects in High-Resolution UAV
Imagery,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link
1905
Detect and count.
BibRef
Wang, R.[Rui],
Liang, Y.[Ying],
Xu, J.W.[Jing Wen],
He, Z.H.[Zhi Hai],
Cascading classifier with discriminative multi-features for a specific
3D object real-time detection,
VC(35), No. 3, March 2019, pp. 399-414.
Springer DOI
1906
Specific 3D object detection for service robots.
BibRef
Wang, R.[Rui],
Xu, J.W.[Jing-Wen],
Han, T.X.[Tony X.],
Object instance detection with pruned Alexnet and extended training
data,
SP:IC(70), 2019, pp. 145-156.
Elsevier DOI
1812
Object instance detection, Pruned Alexnet,
Binarized normed gradient, Data extension
BibRef
Wang, J.D.[Jin-Ding],
Hu, H.F.[Hai-Feng],
Lu, X.L.[Xin-Long],
ADN for object detection,
IET-CV(14), No. 2, March 2020, pp. 65-72.
DOI Link
2002
Attentional Detection Network.
BibRef
Chen, J.[Jin],
Wu, X.X.[Xin-Xiao],
Duan, L.X.[Li-Xin],
Chen, L.[Lin],
Sequential Instance Refinement for Cross-Domain Object Detection in
Images,
IP(30), 2021, pp. 3970-3984.
IEEE DOI
2104
Object detection, Feature extraction, Detectors,
Reinforcement learning, Proposals, Task analysis.
BibRef
Li, G.B.[Guan-Bin],
Yan, P.X.[Peng-Xiang],
Xie, Y.[Yuan],
Wang, G.S.[Gui-Sheng],
Lin, L.[Liang],
Yu, Y.Z.[Yi-Zhou],
Instance-Level Salient Object Segmentation,
CVIU(207), 2021, pp. 103207.
Elsevier DOI
2105
BibRef
Earlier: A1, A3, A5, A6, Only:
CVPR17(247-256)
IEEE DOI
1711
Salient object detection, Salient object contour detection,
Salient instance segmentation, Multiscale refinement network.
Image segmentation, Neural networks, Object detection, Proposals,
Semantics, Streaming, media
BibRef
Zhu, X.Y.[Xiao-Yu],
Wang, H.[Haodi],
Zhang, Z.Y.[Zhi-Yi],
Wu, X.P.[Xiu-Ping],
Guo, J.Q.[Jun-Qi],
Wu, H.[Hao],
A deep learning network based end-to-end image composition,
SP:IC(101), 2022, pp. 116570.
Elsevier DOI
2201
Image composition, End-to-end, Background retrieval,
Instance optimization, Double-sieving region location
BibRef
Zhao, C.Y.[Chen-Yang],
Hsiao, J.H.[Janet H.],
Chan, A.B.[Antoni B.],
Gradient-Based Instance-Specific Visual Explanations for Object
Specification and Object Discrimination,
PAMI(46), No. 9, September 2024, pp. 5967-5985.
IEEE DOI
2408
Object Detector Activation Maps (ODAM).
Detectors, Visualization, Heat maps, Task analysis, Object detection,
Predictive models, Transformers, Deep learning, explainable AI,
object specification
BibRef
Angles, B.[Baptiste],
Jin, Y.H.[Yu-He],
Kornblith, S.[Simon],
Tagliasacchi, A.[Andrea],
Yi, K.M.[Kwang Moo],
MIST: Multiple Instance Spatial Transformer,
CVPR21(2412-2422)
IEEE DOI
2111
Problems that involve detection of multiple object instances.
Training, Location awareness, Transformers, Image decomposition,
Proposals, Task analysis
BibRef
Chen, C.Q.[Chao-Qi],
Zheng, Z.B.[Ze-Biao],
Huang, Y.[Yue],
Ding, X.H.[Xing-Hao],
Yu, Y.Z.[Yi-Zhou],
I3Net: Implicit Instance-Invariant Network for Adapting One-Stage
Object Detectors,
CVPR21(12571-12580)
IEEE DOI
2111
Correlation, Semantics, Pipelines, Detectors, Object detection,
Benchmark testing, Feature extraction
BibRef
Dai, X.[Xing],
Jiang, Z.[Zeren],
Wu, Z.[Zhao],
Bao, Y.P.[Yi-Ping],
Wang, Z.C.[Zhi-Cheng],
Liu, S.[Si],
Zhou, E.[Erjin],
General Instance Distillation for Object Detection,
CVPR21(7838-7847)
IEEE DOI
2111
Adaptation models, Object detection,
Feature extraction, Task analysis
BibRef
Nie, Y.[Yinyu],
Hou, J.[Ji],
Han, X.G.[Xiao-Guang],
Nießner, M.[Matthias],
RfD-Net: Point Scene Understanding by Semantic Instance
Reconstruction,
CVPR21(4606-4616)
IEEE DOI
2111
Location awareness, Surface reconstruction,
Shape, Semantics, Object detection, Transformers
BibRef
Ren, Z.,
Yu, Z.,
Yang, X.,
Liu, M.,
Lee, Y.J.,
Schwing, A.G.,
Kautz, J.,
Instance-Aware, Context-Focused, and Memory-Efficient Weakly
Supervised Object Detection,
CVPR20(10595-10604)
IEEE DOI
2008
Proposals, Object detection, Training, Memory management, Detectors,
Task analysis, Face
BibRef
Fan, R.C.[Ruo-Chen],
Cheng, M.M.[Ming-Ming],
Hou, Q.B.[Qi-Bin],
Mu, T.J.[Tai-Jiang],
Wang, J.D.[Jing-Dong],
Hu, S.M.[Shi-Min],
S4Net: Single Stage Salient-Instance Segmentation,
CVPR19(6096-6105).
IEEE DOI
2002
BibRef
Lu, E.[Erika],
Xie, W.[Weidi],
Zisserman, A.[Andrew],
Class-Agnostic Counting,
ACCV18(III:669-684).
Springer DOI
1906
BibRef
Atoum, Y.,
Roth, J.,
Bliss, M.,
Zhang, W.,
Liu, X.,
Monocular Video-Based Trailer Coupler Detection Using Multiplexer
Convolutional Neural Network,
ICCV17(5478-5486)
IEEE DOI
1802
cameras, convolution,
intelligent transportation systems, neural nets,
BibRef
Biparva, M.,
Tsotsos, J.K.[John K.],
STNet: Selective Tuning of Convolutional Networks for Object
Localization,
CogCV17(2715-2723)
IEEE DOI
1802
Biological system modeling, Computational modeling,
Streaming media, Visualization
BibRef
Jamalian, A.,
Bergelt, J.,
Dinkelbach, H.Ü.,
Spatial Attention Improves Object Localization: A Biologically
Plausible Neuro-Computational Model for Use in Virtual Reality,
CogCV17(2724-2729)
IEEE DOI
1802
Brain modeling, Lips, Neurons, Search problems, Solid modeling, Visualization
BibRef
Wang, B.[Bo],
Shao, J.[Jie],
He, C.K.[Cheng-Kun],
Hu, G.[Gang],
Xu, X.[Xing],
Spatial Verification via Compact Words for Mobile Instance Search,
MMMod17(II: 356-367).
Springer DOI
1701
specific instance (object, person, or location).
BibRef
Lokoc, J.[Jakub],
Kubon, D.[David],
Blažek, A.[Adam],
A Comparative Study for Known Item Visual Search Using Position Color
Feature Signatures,
MMMod17(II: 3-14).
Springer DOI
1701
BibRef
Huberman, I.[Inbar],
Fattal, R.[Raanan],
Detecting Repeating Objects Using Patch Correlation Analysis,
CVPR16(2903-2911)
IEEE DOI
1612
Detect and count.
BibRef
Yu, T.,
Wang, Z.,
Yuan, J.,
Compressive Quantization for Fast Object Instance Search in Videos,
ICCV17(726-735)
IEEE DOI
1802
Hamming codes, cryptography, feature extraction, image matching,
object detection, optimisation, query processing,
Videos
BibRef
Yu, T.,
Wu, Y.,
Yuan, J.,
HOPE: Hierarchical Object Prototype Encoding for Efficient Object
Instance Search in Videos,
CVPR17(3195-3204)
IEEE DOI
1711
Computational efficiency, Encoding, Proposals, Prototypes,
Search problems, Videos, Visualization
BibRef
Hayder, Z.,
He, X.,
Salzmann, M.,
Boundary-Aware Instance Segmentation,
CVPR17(587-595)
IEEE DOI
1711
Image segmentation, Proposals, Robustness, Semantics, Shape, Transforms
BibRef
Dwibedi, D.[Debidatta],
Misra, I.[Ishan],
Hebert, M.[Martial],
Cut, Paste and Learn:
Surprisingly Easy Synthesis for Instance Detection,
ICCV17(1310-1319)
IEEE DOI
1802
learning (artificial intelligence), object detection,
annotated instance datasets, benchmark datasets,
Visualization
BibRef
Liu, S.,
Jia, J.,
Fidler, S.,
Urtasun, R.,
SGN: Sequential Grouping Networks for Instance Segmentation,
ICCV17(3516-3524)
IEEE DOI
1802
image segmentation, neural nets, object detection, PASCAL VOC, SGN,
horizontal object breakpoints, line segments,
Semantics
BibRef
Liang, X.D.[Xiao-Dan],
Wei, Y.C.[Yun-Chao],
Shen, X.H.[Xiao-Hui],
Jie, Z.Q.[Ze-Qun],
Feng, J.S.[Jia-Shi],
Lin, L.[Liang],
Yan, S.C.[Shui-Cheng],
Reversible Recursive Instance-Level Object Segmentation,
CVPR16(633-641)
IEEE DOI
1612
BibRef
Batchelor, O.[Oliver],
Green, R.[Richard],
The role of focus in object instance recognition,
ICVNZ16(1-5)
IEEE DOI
1701
Context. CNN analysis. Focus on the object of interest.
BibRef
Nakano, H.[Hiroki],
Mori, Y.[Yumi],
Morita, C.[Chiaki],
Nagai, S.[Shingo],
Large Scale Specific Object Recognition by Using GIFTS Image Feature,
CIAP15(II:36-45).
Springer DOI
1511
BibRef
Takauji, H.[Hidenori],
Nakayama, I.[Io],
Kaneko, S.[Shun'ichi],
Tanaka, T.[Takayuki],
Autonomous and robust structuring of real environment by searching
complex regions,
IEVM06(xx-yy).
PDF File.
0609
Find specific objects (tags) to use for scale in stereo analysis.
BibRef
Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Fiducial Markers Design, Detection and Analysis .