3.6.3.1 Receptive Field Issues

Chapter Contents (Back)
Receptive Fields. Both human vision and CNN related implementatons.

Crettez, J.P., Simon, J.C.,
A Model for Cell Receptive Fields in the Visual Striate Cortex,
CGIP(20), No. 4, December 1982, pp. 299-318.
Elsevier DOI BibRef 8212

Perez, C.A., Salinas, C.A., Estevez, P.A., Valenzuela, P.M.,
Genetic design of biologically inspired receptive fields for neural pattern recognition,
SMC-B(33), No. 2, April 2003, pp. 258-270.
IEEE Abstract. 0308
BibRef

Brun, L.[Luc], Kropatsch, W.G.[Walter G.],
Receptive fields within the Combinatorial Pyramid framework,
GM(65), No. 1-3, May 2003, pp. 23-42.
Elsevier DOI 0309
BibRef
And:
Combinatorial pyramids,
ICIP03(II: 33-36).
IEEE DOI 0312
BibRef

Zhang, H.[Hui], Liu, Y.[Yi], Xie, B.[Bojun], Yu, J.[Jian],
Spatially constrained sparse coding scheme for natural scene categorization,
JVCIR(28), No. 1, 2015, pp. 28-35.
Elsevier DOI 1503
BibRef
Earlier:
A boosting approach to learning receptive fields for scene categorization,
ICIP13(265-269)
IEEE DOI 1402
Scene categorization. Accuracy BibRef

Mahmoodi, S.[Sasan],
Linear Neural Circuitry Model for Visual Receptive Fields,
JMIV(54), No. 2, February 2016, pp. 138-161.
WWW Link. 1602
BibRef

Wan, X.Q.[Xiao-Qing], Zhao, C.H.[Chun-Hui],
Local receptive field constrained stacked sparse autoencoder for classification of hyperspectral images,
JOSA-A(34), No. 6, June 2017, pp. 1011-1020.
DOI Link 1706
Image processing, Image analysis, Remote, sensing, and, sensors BibRef

Lu, Q.S.[Qi-Shuo], Jiang, Z.Q.[Zhu-Qing], Men, A.D.[Ai-Dong], Tang, P.L.[Peng-Liang],
Object detection using convolutional networks with adaptively adjusting receptive field of convolutional filter,
IET-CV(13), No. 6, September 2019, pp. 562-568.
DOI Link 1911
BibRef

Xie, S.R.[Shao-Rong], Liu, C.[Chang], Gao, J.T.[Jian-Tao], Li, X.M.[Xiao-Mao], Luo, J.[Jun], Fan, B.J.[Bao-Jie], Chen, J.H.[Jia-Hong], Pu, H.Y.[Hua-Yan], Peng, Y.[Yan],
Diverse receptive field network with context aggregation for fast object detection,
JVCIR(70), 2020, pp. 102770.
Elsevier DOI 2007
Object detection, Convolutional neural network, Context aggregation, Multi-scale contextual representations BibRef

Zhang, R.[Rui], Tang, S.[Sheng], Zhang, Y.D.[Yong-Dong], Li, J.T.[Jin-Tao], Yan, S.C.[Shui-Cheng],
Perspective-Adaptive Convolutions for Scene Parsing,
PAMI(42), No. 4, April 2020, pp. 909-924.
IEEE DOI 2003
Not uniform size receptive field in CNN. Shape, Standards, Strain, Proposals, Convolutional neural networks, Training, Task analysis, Scene parsing, context adaptive biases BibRef

Yuan, Z.C.[Zhi-Chao], Liu, Z.M.[Zi-Ming], Zhu, C.B.[Chun-Bo], Qi, J.[Jing], Zhao, D.[Danpei],
Object Detection in Remote Sensing Images via Multi-Feature Pyramid Network with Receptive Field Block,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Jeon, Y.H.[Yun-Ho], Kim, J.[Junmo],
Integrating Multiple Receptive Fields Through Grouped Active Convolution,
PAMI(43), No. 11, November 2021, pp. 3892-3903.
IEEE DOI 2110
Convolution, Shape, Task analysis, Semantics, Network architecture, Backpropagation, deep learning BibRef

Xia, P.F.[Peng-Fei], Niu, H.J.[Hong-Jing], Li, Z.Q.[Zi-Qiang], Li, B.[Bin],
On the receptive field misalignment in CAM-based visual explanations,
PRL(152), 2021, pp. 275-282.
Elsevier DOI 2112
Convolutional neural networks, Visual explanations, Class activation mapping, Receptive field misalignment, Adversarial marginal attack BibRef

Chai, E.[Enhui], Ta, L.[Lin], Ma, Z.F.[Zhan-Fei], Zhi, M.[Min],
ERF-YOLO: A YOLO algorithm compatible with fewer parameters and higher accuracy,
IVC(116), 2021, pp. 104317.
Elsevier DOI 2112
The effective receptive field, The activation function, The backbone network, Concat, The anchor box loss function BibRef

Yan, Q.[Qi], Zheng, Y.J.[Ya-Jing], Jia, S.S.[Shan-Shan], Zhang, Y.C.[Yi-Chen], Yu, Z.F.[Zhao-Fei], Chen, F.[Feng], Tian, Y.H.[Yong-Hong], Huang, T.J.[Tie-Jun], Liu, J.K.[Jian K.],
Revealing Fine Structures of the Retinal Receptive Field by Deep-Learning Networks,
Cyber(52), No. 1, January 2022, pp. 39-50.
IEEE DOI 2201
Biological system modeling, Retina, Integrated circuit modeling, Computational modeling, Visualization, Data models, Training, visual coding BibRef

Kim, B.J.[Bum Jun], Koo, G.[Gyogwon], Choi, H.[Hyeyeon], Kim, S.W.[Sang Woo],
Extending class activation mapping using Gaussian receptive field,
CVIU(231), 2023, pp. 103663.
Elsevier DOI 2305
Deep learning, Convolutional neural networks, Visualization, Saliency map, Explainable artificial intelligence, Class activation mapping BibRef

Gao, S.[Shanghua], Li, Z.Y.[Zhong-Yu], Han, Q.[Qi], Cheng, M.M.[Ming-Ming], Wang, L.[Liang],
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks,
PAMI(45), No. 3, March 2023, pp. 2984-3002.
IEEE DOI 2302
Task analysis, Hidden Markov models, Object detection, Iterative methods, Speech synthesis, Semantics, temporal action segmentation BibRef


Kobayashi, T.[Takumi],
Disentangled convolution for optimizing receptive field,
PRL(169), 2023, pp. 67-74.
Elsevier DOI 2305
Convolution, Receptive field, Gaussian smoothing, CNN, Image classification BibRef

Dong, P.[Peijie], Niu, X.[Xin], Wei, Z.[Zimian], Pan, H.Y.[Heng-Yue], Li, D.S.[Dong-Sheng], Huang, Z.[Zhen],
Autorf: Auto Learning Receptive Fields with Spatial Pooling,
MMMod23(II: 683-694).
Springer DOI 2304
BibRef

Imamura, A.[Akihiro], Arizumi, N.[Nana],
Revisiting Spatial Inductive Bias with MLP-Like Model,
ICIP22(921-925)
IEEE DOI 2211
Training, Convolution, Neural networks, Task analysis, token mixing, inductive bias, local receptive field, locality BibRef

Jang, D.H.[Dong-Hwan], Chu, S.[Sanghyeok], Kim, J.[Joonhyuk], Han, B.H.[Bo-Hyung],
Pooling Revisited: Your Receptive Field is Suboptimal,
CVPR22(539-548)
IEEE DOI 2210
Deep learning, Image segmentation, Image resolution, Shape, Computational modeling, Neural networks, Semantics, retrieval BibRef

Babicz, D.[Dóra], Kontár, S.[Soma], Peto, M.[Márk], Fülöp, A.[András], Szabó, G.[Gergely], Horváth, A.[András],
Receptive Field Size Optimization with Continuous Time Pooling,
WACV21(1448-1457)
IEEE DOI 2106
Training, Quantization (signal), Perturbation methods, Neurons, Differential equations, Network architecture BibRef

Zhang, J.C.[Jia-Cheng], Zhao, Z.C.[Zhi-Cheng], Su, F.[Fei],
Efficient-Receptive Field Block with Group Spatial Attention Mechanism for Object Detection,
ICPR21(3248-3255)
IEEE DOI 2105
Radio frequency, Heating systems, Strips, Head, Semantics, Object detection, Network architecture BibRef

Lee, Y., Jung, H., Han, D., Kim, K., Kim, J.,
Learning Receptive Field Size by Learning Filter Size,
WACV19(1203-1212)
IEEE DOI 1904
backpropagation, convolutional neural nets, image classification, learning (artificial intelligence), resource allocation BibRef

Liu, S.T.[Song-Tao], Huang, D.[Di], Wang, Y.H.[Yun-Hong],
Receptive Field Block Net for Accurate and Fast Object Detection,
ECCV18(XI: 404-419).
Springer DOI 1810
BibRef

Shen, Y.[Yu], Chen, J.Y.[Jian-Yu], Xiao, L.[Liang],
Supervised Classification of Hyperspectral Images Using Local-Receptive-Fields-Based Kernel Extreme Learning Machine,
ICIP17(3120-3124)
IEEE DOI 1803
Convolution, Hyperspectral imaging, Kernel, Mathematical model, Neurons, Principal component analysis, LRF-KELM, SVM, random convolution nodes BibRef

Wei, Z.[Zhen], Sun, Y.[Yao], Wang, J.Q.[Jin-Qiao], Lai, H.J.[Han-Jiang], Liu, S.[Si],
Learning Adaptive Receptive Fields for Deep Image Parsing Network,
CVPR17(3947-3955)
IEEE DOI 1711
Face, Interpolation, Kernel, Manuals, Training BibRef

Jacobsen, J.H., van Gemert, J.C.[Jan C.], Lou, Z., Smeulders, A.W.M.,
Structured Receptive Fields in CNNs,
CVPR16(2610-2619)
IEEE DOI 1612
BibRef

Ghosh, K.[Kuntal], Sarkar, S.[Sandip], Bhaumik, K.[Kamales],
Early Vision and Image Processing: Evidences Favouring a Dynamic Receptive Field Model,
ICCVGIP06(216-227).
Springer DOI 0612
BibRef

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Last update:Apr 18, 2024 at 11:38:49