Fetching the paper…
Reading the bibliography…
Deep features are a cornerstone of computer vision research, capturing image semantics and enabling the community to solve downstream tasks even in the zero- or few-shot regime.
wav2vec: Unsupervised pre-training for speech recognition, 2019
Steffen Schneider, Alexei Baevski, Ronan Collobert, and Michael Auli · 1904
Earlier work this paper cites.
Pixel-adaptive convolutional neural networks
Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik G. Learned-Miller, and Jan Kautz · 1904
Earlier work this paper cites.
Carafe: Content-aware reassembly of features
Jiaqi Wang, Kai Chen, Rui Xu, Ziwei Liu, Chen Change Loy, and Dahua Lin · 1905
Earlier work this paper cites.
Momentum contrast for unsupervised visual representation learning, 2019
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 1911
Earlier work this paper cites.
Cubic convolution interpolation for digital image processing
Robert Keys · 1981
Earlier work this paper cites.
Extensions of lipschitz maps into banach spaces
William B Johnson, Joram Lindenstrauss, and Gideon Schechtman · 1986
Earlier work this paper cites.
Nonlinear total variation based noise removal algorithms
Leonid I Rudin, Stanley Osher, and Emad Fatemi · 1992
Earlier work this paper cites.
Bilateral filtering for gray and color images
C. Tomasi and R. Manduchi · 1998
Earlier work this paper cites.
Shape recipes: Scene representations that refer to the image
William Freeman and Antonio Torralba · 2002
Earlier work this paper cites.
Semantically-guided representation learning for self-supervised monocular depth, 2020
Vitor Guizilini, Rui Hou, Jie Li, Rares Ambrus, and Adrien Gaidon · 2002
Earlier work this paper cites.
Nerf: Representing scenes as neural radiance fields for view synthesis, 2020
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng · 2003
Earlier work this paper cites.
Distinctive image features from scale-invariant keypoints
G LoweDavid · 2004
Earlier work this paper cites.
A non-local algorithm for image denoising
A. Buades, B. Coll, and J.-M. Morel · 2005
Earlier work this paper cites.
Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
Earlier work this paper cites.
Implicit neural representations with periodic activation functions, 2020a
Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein · 2006
Earlier work this paper cites.
Fourier features let networks learn high frequency functions in low dimensional domains, 2020
Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng · 2006
Earlier work this paper cites.
Joint bilateral upsampling
Johannes Kopf, Michael F. Cohen, Dani Lischinski, and Matt Uyttendaele · 2007
Earlier work this paper cites.
Adaptive confidence thresholding for monocular depth estimation, 2020
Hyesong Choi, Hunsang Lee, Sunkyung Kim, Sunok Kim, Seungryong Kim, Kwanghoon Sohn, and Dongbo Min · 2009
Earlier work this paper cites.
Sift flow: Dense correspondence across scenes and its applications
Ce Liu, Jenny Yuen, and Antonio Torralba · 2010
Earlier work this paper cites.
Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
Earlier work this paper cites.
Fast image dehazing using guided joint bilateral filter
Chunxia Xiao and Jiajia Gan · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Transfer learning for visual categorization: A survey
Ling Shao, Fan Zhu, and Xuelong Li · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
The guided bilateral filter: When the joint/cross bilateral filter becomes robust
Laurent Caraffa, Jean-Philippe Tarel, and Pierre Charbonnier · 2015
Earlier work this paper cites.
Image super-resolution using deep convolutional networks, 2015
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2015
Earlier work this paper cites.
Superpixel convolutional networks using bilateral inceptions, 2015
Raghudeep Gadde, Varun Jampani, Martin Kiefel, Daniel Kappler, and Peter V. Gehler · 2015
Earlier work this paper cites.
Conditional generative adversarial nets for convolutional face generation
Jon Gauthier · 2015
Earlier work this paper cites.
Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
Cited alongside, same era.
Learning deconvolution network for semantic segmentation
Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Cited alongside, same era.
Understanding intermediate layers using linear classifier probes, 2016
Guillaume Alain and Yoshua Bengio · 2016
Cited alongside, same era.
Semantic understanding of scenes through the ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2019
Later among the works it cites.
Single-stage semantic segmentation from image labels
Nikita Araslanov and Stefan Roth · 2020
Later among the works it cites.
Learning affinity-aware upsampling for deep image matting, 2020
Yutong Dai, Hao Lu, and Chunhua Shen · 2020
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Later among the works it cites.
It is likely that your loss should be a likelihood
Mark Hamilton, Evan Shelhamer, and William T Freeman · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Cited alongside, same era.
Superpixel convolutional networks using bilateral inceptions
Raghudeep Gadde, Varun Jampani, Martin Kiefel, Daniel Kappler, and Peter V Gehler · 2016
Cited alongside, same era.
Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
Cited alongside, same era.
Deconvolution and checkerboard artifacts
Augustus Odena, Vincent Dumoulin, and Chris Olah · 2016
Cited alongside, same era.
Is the deconvolution layer the same as a convolutional layer?, 2016
Wenzhe Shi, Jose Caballero, Lucas Theis, Ferenc Huszar, Andrew Aitken, Christian Ledig, and Zehan Wang · 2016
Cited alongside, same era.
A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang · 2016
Cited alongside, same era.
Unet 3+: A full-scale connected unet for medical image segmentation
Huimin Huang, Lanfen Lin, Ruofeng Tong, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xianhua Han, Yen-Wei Chen, and Jian Wu · 2020
Later among the works it cites.
Index networks
Hao Lu, Yutong Dai, Chunhua Shen, and Songcen Xu · 2020
Later among the works it cites.
Attention-based transformers for instance segmentation of cells in microstructures
Tim Prangemeier, Christoph Reich, and Heinz Koeppl · 2020
Later among the works it cites.
Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun · 2020
Later among the works it cites.
Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
Later among the works it cites.
Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2020
Later among the works it cites.
Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation
Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen · 2020
Later among the works it cites.
Squeezesegv3: Spatially-adaptive convolution for efficient point-cloud segmentation
Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka · 2020
Later among the works it cites.
Deep vit features as dense visual descriptors, 2021
Shir Amir, Yossi Gandelsman, Shai Bagon, and Tali Dekel · 2021
Later among the works it cites.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
Later among the works it cites.
Learning continuous image representation with local implicit image function
Yinbo Chen, Sifei Liu, and Xiaolong Wang · 2021
Later among the works it cites.
Selfdeco: Self-supervised monocular depth completion in challenging indoor environments
Jaehoon Choi, Dongki Jung, Yonghan Lee, Deokhwa Kim, Dinesh Manocha, and Donghwan Lee · 2021
Later among the works it cites.
Learning affinity-aware upsampling for deep image matting
Yutong Dai, Hao Lu, and Chunhua Shen · 2021
Later among the works it cites.
Hubert: Self-supervised speech representation learning by masked prediction of hidden units, 2021
Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, and Abdelrahman Mohamed · 2021
Later among the works it cites.
Relevance-cam: Your model already knows where to look
Jeong Ryong Lee, Sewon Kim, Inyong Park, Taejoon Eo, and Dosik Hwang · 2021
Later among the works it cites.
Blending anti-aliasing into vision transformer
Shengju Qian, Hao Shao, Yi Zhu, Mu Li, and Jiaya Jia · 2021
Later among the works it cites.
High-resolution image synthesis with latent diffusion models, 2021
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
Later among the works it cites.
Segformer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 2021
Later among the works it cites.
Unsupervised semantic segmentation by distilling feature correspondences
Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, and William T Freeman · 2022
Later among the works it cites.
Learning implicit feature alignment function for semantic segmentation, 2022
Hanzhe Hu, Yinbo Chen, Jiarui Xu, Shubhankar Borse, Hong Cai, Fatih Porikli, and Xiaolong Wang · 2022
Later among the works it cites.
Decomposing nerf for editing via feature field distillation
Sosuke Kobayashi, Eiichi Matsumoto, and Vincent Sitzmann · 2022
Later among the works it cites.
Splicing vit features for semantic appearance transfer, 2022
Narek Tumanyan, Omer Bar-Tal, Shai Bagon, and Tali Dekel · 2022
Later among the works it cites.
Learning to upsample by learning to sample, 2023
Wenze Liu, Hao Lu, Hongtao Fu, and Zhiguo Cao · 2023
Later among the works it cites.