Deep Image Harmonization
Deep Image Harmonization Yi-Hsuan Tsai, et al. “Deep Image Harmonization” Computer Vision and Pattern Recognition 2017
Posts by Category
- Data Augmentation 11
- Generative Adversarial Network 6
- Cnn architectures 5
- Image Recognition 4
- Segmentation 3
- Incremental or Continual Learning 3
- Face Detection 2
- Object Detection 1
- Anomaly Detection 1
- Class Activation Mapping 1
- Video GAN 1
- Contrastive Learning 1
Data Augmentation
RandAugment: Practical automated data augmentation with a reduced search space
RandAugment: Practical automated data augmentation with a reduced search space Ekin D. Cubuk, et al. “RandAugment: Practical automated data augmentation w...
AutoAugment: Learning Augmentation Strategies from Data
AutoAugment: Learning Augmentation Strategies from Data Ekin D. Cubuk, et al. “AutoAugment: Learning Augmentation Strategies from Data” Computer Vision an...
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery Gongjie Zhang, et al. “CAD-Net: A Context-Aware Detection Network for Obj...
Learning from Noisy Anchors for One-stage Object Detection
Learning from Noisy Anchors for One-stage Object Detection Hengduo Li, et al. “Learning from Noisy Anchors for One-stage Object Detection” Learning fro...
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Return of the Devil in the Details: Delving Deep into Convolutional Nets Ken Chatfield, et al. “Return of the Devil in the Details: Delving Deep into Conv...
NOISE OR SIGNAL: THE ROLE OF IMAGE BACKGROUNDS IN OBJECT RECOGNITION
NOISE OR SIGNAL: THE ROLE OF IMAGE BACKGROUNDS IN OBJECT RECOGNITION Kai Xiao, et al. “NOISE OR SIGNAL: THE ROLE OF IMAGE BACKGROUNDS IN OBJECT RECOGNITIO...
A survey on Image Data Augmentation for Deep Learning
A survey on Image Data Augmentation for Deep Learning Connor Shorten, et al. “A survey on Image Data Augmentation for Deep Learning”
KeepAugment: A Simple Information-Preserving Data Augmentation Approach
KeepAugment: A Simple Information-Preserving Data Augmentation Approach Chengyue Gong, et al. “KeepAugment: A Simple Information-Preserving Data Augmentat...
ReMix: Towards Image-to-Image Translation with Limited Data
ReMix: Towards Image-to-Image Translation with Limited Data Jie Cao, et al. “ReMix: Towards Image-to-Image Translation with Limited Data” Computer Vision ...
SuperMix: Supervising the Mixing Data Augmentation
SuperMix: Supervising the Mixing Data Augmentation Ali Dabouei, et al. “SuperMix: Supervising the Mixing Data Augmentation” Computer Vision and Pattern Re...
Generative Adversarial Network
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation Elad Richardson, et al. “Encoding in Style: a StyleGAN Encoder for Image-to-Image Tra...
RELGAN: RELATIONAL GENERATIVE ADVERSARIAL NETWORKS FOR TEXT GENERATION
RELGAN: RELATIONAL GENERATIVE ADVERSARIAL NETWORKS FOR TEXT GENERATION Weili Nie, et al. “RELGAN: RELATIONAL GENERATIVE ADVERSARIAL NETWORKS FOR TEXT GENE...
PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION
PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms Mahmoud Afif, et al. “HistoGAN: Controlling Colors of GAN-Generated and...
BigGAN paper review
BigGAN Andrew Brock, et al. “Large Scale GAN Training for High Fidelity Natural Image Synthesis” Computer Vision and Pattern Recognition
AdaIN paper review
AdaIN Xun Huang, et al. “Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.” Proceedings of the IEEE International Conference on ...
Cnn architectures
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation Xiangtai Li, et al. “PointFlow: Flowing Semantics Through Points for Aerial Imag...
RaScaNet: Learning Tiny Models by Raster-Scanning Images
RaScaNet: Learning Tiny Models by Raster-Scanning Images Yang Liu, et al. “RaScaNet: Learning Tiny Models by Raster-Scanning Images” Computer Vision and P...
Invertible Denoising Network: A Light Solution for Real Noise Removal
Invertible Denoising Network: A Light Solution for Real Noise Removal Yang Liu, et al. “Invertible Denoising Network: A Light Solution for Real Noise Remo...
Quality-Agnostic Image Recognition via Invertible Decoder
Quality-Agnostic Image Recognition via Invertible Decoder Insoo Kim, et al. “Quality-Agnostic Image Recognition via Invertible Decoder” Computer Vision an...
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
CondConv: Conditionally Parameterized Convolutions for Efficient Inference Brandon Yang, et al. “CondConv: Conditionally Parameterized Convolutions for Ef...
Image Recognition
ResNeXt paper review
Aggregated Residual Transformations for Deep Neural Networks Saining Xie, Kaiming He, et al. “Aggregated Residual Transformations for Deep Neural Networks...
VGG paper review
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan, Andrew Zisserman. “Very Deep Convolutional Networks for Large-Scale Ima...
Transfer-learning paper review
How transferable are features in deep neuralnetworks? Jason Yosinski, et al. “How transferable are features in deep neuralnetworks?”. 2014.
ResNet paper review
Deep Residual Learning for Image Recognition Kaiming He, et al. “Deep Residual Learning for Image Recognition.” Proceedings of the IEEE conference on comp...
Segmentation
Mask R-CNN paper review
Mask R-CNN Kaiming He, et al. “Mask R-CNN.” Proceedings of the IEEE International Conference on Computer Vision (ICCV)2017.
PSPNET paper review
Pyramid Scene Parsing Network Hengshuang Zhao, et al. “Pyramid Scene Parsing Network” Proceedings of the IEEE Conference on Computer Vision and Pattern Re...
DeepLabV2 paper review
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Liang-Chieh Chen, et al. “DeepLab: Semanti...
Incremental or Continual Learning
IIRC: Incremental Implicitly-Refined Classification
IIRC: Incremental Implicitly-Refined Classification Mohamed Abdelsalam, et al. “IIRC: Incremental Implicitly-Refined Classification” Computer Vision and P...
EXTENDING CONDITIONAL CONVOLUTION STRUCTURES FOR ENHANCING MULTITASKING CONTINUAL LEARNING
EXTENDING CONDITIONAL CONVOLUTION STRUCTURES FOR ENHANCING MULTITASKING CONTINUAL LEARNING Cheng-Hao Tu, et al. “EXTENDING CONDITIONAL CONVOLUTION STRUCTU...
DER: Dynamically Expandable Representation for Class Incremental Learning
DER: Dynamically Expandable Representation for Class Incremental Learning Shipeng Yan, et al. “DER: Dynamically Expandable Representation for Class Increm...
Face Detection
ArcFace paper review
ArcFace Jiankang Deng, et al. “ArcFace: Additive Angular Margin Loss for Deep Face Recognition” Computer Vision and Pattern Recognition
RetinaFace paper review
RetinaFace Jiankang Deng, et al. “RetinaFace: Single-stage Dense Face Localisation in the Wild” Computer Vision and Pattern Recognition
Object Detection
R-CNN paper review
Rich feature hierarchies for accurate object detection and semantic segmentation Ross Girshick, et al. “Rich feature hierarchies for accurate object detec...
Anomaly Detection
Real world Anomaly Detection in Surveillance Video paper review
Real world Anomaly Detection in Surveillance Video Waqas Sultani, et al. “Real world Anomaly Detection in Surveillance Video” Computer Vision and Pattern ...
Class Activation Mapping
Relevance-CAM: Your Model Already Knows Where to Look
Relevance-CAM: Your Model Already Knows Where to Look Jeong Ryong Lee, et al. “Relevance-CAM: Your Model Already Knows Where to Look” Computer Vision and ...
Video GAN
Playable Video Generation
Playable Video Generation Willi Menapace, et al. [“Playable Video Generation”]https://arxiv.org/abs/2101.12195) Computer Vision and Pattern Recognition
Contrastive Learning
CLCC: Contrastive Learning for Color Constancy
CLCC: Contrastive Learning for Color Constancy Yi-Chen Lo, et al. “CLCC: Contrastive Learning for Color Constancy” CVPR2021