Resolution learning in deep convolutional networks using scale-space theory

7 June 2021

Papers citing "Resolution learning in deep convolutional networks using scale-space theory"

25 / 25 papers shown

Title
Scale generalisation properties of extended scale-covariant and scale-invariant Gaussian derivative networks on image datasets with spatial scaling variations Andrzej Perzanowski Tony Lindeberg 47 1 0 17 Sep 2024
Scaling Continuous Kernels with Sparse Fourier Domain Learning Clayton A. Harper Luke Wood Peter Gerstoft Eric C. Larson 26 0 0 15 Sep 2024
Approximation properties relative to continuous scale space for hybrid discretizations of Gaussian derivative operators Tony Lindeberg 34 2 0 08 May 2024
Deep Continuous Networks Nergis Tomen S. Pintea J. C. V. Gemert 92 14 0 02 Feb 2024
Discrete approximations of Gaussian smoothing and Gaussian derivatives Tony Lindeberg 24 12 0 19 Nov 2023
As large as it gets: Learning infinitely large Filters via Neural Implicit Functions in the Fourier Domain Julia Grabinski J. Keuper M. Keuper 21 6 0 19 Jul 2023
Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture Mehraveh Javan Matthew Toews M. Pedersoli 31 1 0 21 Jun 2023
Dilated Convolution with Learnable Spacings: beyond bilinear interpolation Ismail Khalfaoui-Hassani Thomas Pellegrini T. Masquelier 16 3 0 01 Jun 2023
Rotation-Scale Equivariant Steerable Filters Yi-Lun Yang S. Dasmahapatra S. Mahmoodi 19 3 0 10 Apr 2023
Scale-Equivariant UNet for Histopathology Image Segmentation Yi-Lun Yang S. Dasmahapatra S. Mahmoodi 22 11 0 10 Apr 2023
Covariance properties under natural image transformations for the generalized Gaussian derivative model for visual receptive fields T. Lindeberg 21 11 0 17 Mar 2023
Scale-aware neural calibration for wide swath altimetry observations Q. Febvre C. Ubelmann Julien Le Sommer Ronan Fablet 16 4 0 09 Feb 2023
Dynamic Sparse Network for Time Series Classification: Learning What to "see'' Qiao Xiao Boqian Wu Yu Zhang Shiwei Liu Mykola Pechenizkiy Elena Mocanu D. Mocanu AI4TS 35 28 0 19 Dec 2022
Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance Mats L. Richter C. Pal 19 3 0 26 Nov 2022
Towards Multi-spatiotemporal-scale Generalized PDE Modeling Jayesh K. Gupta Johannes Brandstetter AI4CE 53 117 0 30 Sep 2022
Designing with Non-Finite Output Dimension via Fourier Coefficients of Neural Waveforms Jonathan S. Kent 17 0 0 17 Aug 2022
Fully trainable Gaussian derivative convolutional layer Valentin Penaud-Polge Santiago Velasco-Forero Jesús Angulo 19 9 0 18 Jul 2022
Efficient Representation Learning via Adaptive Context Pooling Chen Huang Walter A. Talbott Navdeep Jaitly J. Susskind 22 6 0 05 Jul 2022
Generative Modelling With Inverse Heat Dissipation Severi Rissanen Markus Heinonen Arno Solin DiffM 11 108 0 21 Jun 2022
Pooling Revisited: Your Receptive Field is Suboptimal Dong-Hwan Jang Sanghyeok Chu Joonhyuk Kim Bohyung Han 22 11 0 30 May 2022
Dilated convolution with learnable spacings Ismail Khalfaoui-Hassani Thomas Pellegrini T. Masquelier 8 31 0 07 Dec 2021
$Frequency learning for structured CNN filters with Gaussian fractional derivatives$ Frequency learning for structured CNN filters with Gaussian fractional derivatives Nikhil Saldanha S. Pintea J. C. V. Gemert Nergis Tomen 22 7 0 12 Nov 2021
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes David W. Romero Robert-Jan Bruintjes Jakub M. Tomczak Erik J. Bekkers Mark Hoogendoorn J. C. V. Gemert 80 82 0 15 Oct 2021
Learning Dynamic Routing for Semantic Segmentation Yanwei Li Lin Song Yukang Chen Zeming Li X. Zhang Xingang Wang Jian-jun Sun SSeg 85 163 0 23 Mar 2020
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification Wensi Tang Guodong Long Lu Liu Tianyi Zhou Michael Blumenstein Jing Jiang AI4TS 16 99 0 24 Feb 2020