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Lenet-5(1998), PyTorch Code [Google Colab / Blog Posting]
AlexNet(2012), PyTorch Code [Google Colab / Blog Posting]
PyTorch 구현 코드로 살펴보는 Knowledge Distillation(2014), PyTorch Code [Google Colab / Blog Posting], paper [pdf]
GoogLeNet(2014), PyTorch Code [Google Colab / Blog Posting]
VGGNet(2014), PyTorch Code [Google Colab / Blog Posting]
ResNet(2015), PyTorch Code [Google Colab / Blog Posting]
Pre-Activation ResNet(2016), PyTorch Code [Google Colab / Blog Posting]
WRN, Wide Residual Networks(2016), PyTorch Code [Google Colab / Blog Posting]
Inception-v4(2016), PyTorch Code [Google Colab / Blog Posting]
DenseNet(2017), PyTorch Code [Google Colab / Blog Posting]
Xception(2017), PyTorch Code [Google Colab / Blog Posting]
MobileNetV1(2017), PyTorch Code [Google Colab / Blog Posting]
ResNext(2017), PyTorch Code [Google Colab / Blog Posting]
Residual Attention Network(2017), PyTorch Code [Google Colab / Blog Posting]
Non-local Neural Network(2017), paper [pdf]
SENet(2018), PyTorch Code [Google Colab / Blog Posting]
CBAM(2018), paper [pdf]
EfficientNet(2019), PyTorch Code [Google Colab / Blog Posting]
SKNet(2019), paper [pdf]
Noise or Signal: The Role of Image Backgrounds in Object Recognition(2020), paper [pdf]
VIT(2020), paper [pdf], PyTorch Code [Google Colab / Blog Posting]
Deit(2020), paper [pdf]
Knowledge distillation: A good teacher is patient and consitent(2021), paper [pdf]
MLP-Mixer(2021), paper [odf]
CeiT(2021), paper [pdf]
Early Convolutions Help Transformers See Better(2021), paper [pdf]
BoTNet(2021), paper [pdf]
Conformer(2021), paper [pdf]
Delving Deep into the Generalization of Vision Transformers under Distribution Shifts(2021), paper [pdf]
Scaling Vision Transformers(2021), paper [pdf]
RetinaNet(2017) PyTorch Code [Google Colab / Blog Posting]
YOLO v3(2018), PyTorch Code [Google Colab / Blog Posting]
CenterNet(2019), paper [pdf]
Gaussian YOLOv3(2019), paper [pdf]
FCOS(2019), paper [pdf]
YOLOv4(2020), paper [pdf]
EfficientDet(2020), paper [pdf]
CSPNet(2020), paper [pdf]
DIoU Loss(2020), paper [pdf], Code
CircleNet(2020), paper [pdf]
DETR(2020), paper [pdf]
Deformable DETR(2020), paper [pdf]
Localization Distillation for Dense Object Detection(2102)
CenterNet2(2021), paper [pdf]
Swin Transformer(2021), paper [pdf]
YOLOr(2021), paper [pdf]
YOLOS(2021), paper [pdf]
Dynamic Head, Unifying Object Detection Heads with Attention(2021), paper [pdf]
Pix2Seq(2021), paper [pdf]
Anchor DETR, Query Design for Transformer-Based Object Detection(2021), paper [pdf]
DAB-DETR, Dynamic Anchor Boxes are Better Queries for DETR(2022), paper [pdf]
DN-DETR, Accelerate DETR Training by Introducing Query DeNoising(2022), paper [pdf]
DINO, DETR with Imporved DeNoising Anchor Boxes for End-to-End Object Detection(2022), paper [pdf]
DilatedNet(2015), paper [pdf]
PyTorch 구현 코드로 살펴보는 SegNet(2015), paper [pdf]
PSPNet(2016), paper [pdf]
DeepLabv3(2017), paper [pdf]
PANet(2018), paper [pdf]
Panoptic Segmentation(2018), paper [pdf]
Weakly- and Semi-Supervised Panoptic Segmentation(2018), paper [pdf]
Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network(2018), paper [pdf]
Single Network Panoptic Segmentation for Street Scene Understanding(2019), paper [pdf]
IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things(2019), paper [pdf]
Object-Contextual Representations for Semantic Segmentation(2019), paper [pdf]
CondInst, Conditional Convolution for Instance Segmentation(2020), paper [pdf]
Max-DeepLab, End-to-End Panoptic Segmentation wtih Mask Transformers, paper [pdf]
MaskFormer, Per-Pixel Classification is Not All You Need for Semantic Segmentation(2021), paper [pdf]
Open-World Entity Segmentation(2021), paper [pdf]
Prompt based Multi-modal Image Segmentation(2021), paper [pdf]
DenseCLIP, Language-Guided Dense Prediction with Context-Aware Prompting, paper [pdf]
Mask2Former, Masked-attention Mask Transformer for Universal Image Segmentation(2021)
SeMask<, Semantically Masked Transformers for Semantic Segmentation(2021)
Constrative Loss(2006), paper [pdf]
Exemplar-CNN(2014), paper [pdf]
Unsupervised Learning of Visual Representation using Videos, paper [pdf]
Context Prediction(2015), paper [pdf]
Jigsaw Puzzles(2016), paper [odf]
Colorful Image Coloriztion(2016), paper [pdf]
Deep InfoMax(2018), paper [pdf]
Deep Cluster(2018), paper [pdf]
Rotation(2018), paper [pdf]
Unsupervised Feature Learning via Non-Parametric Instance Discrimination(2018), paper [pdf]
ADMIN(2019), paper [pdf]
Contrastive Multiview Coding(2019), paper [pdf]
MoCo(2019), paper [pdf]
SeLa(2019), paper [pdf]
SimCLR(2020), paper [pdf]
MoCov2(2020), PyTorch Code [Google Colab / Blog Posting], paper [pdf]
SimSiam(2020), paper [pdf]
Understanding the Behaviour of Contrastive Loss(2020), paper [pdf]
BYOL(2020), paper [pdf]
SwAV(2020), paper [pdf]
SimCLRv2(2020), paper [pdf]
Supervised Contrastive Learning(2020), paper [pdf]
DenseCL(2020), Dense Contrastive Learning for Self-Supervised Visual Pre-Training, paper [pdf]
DetCo(2021), paper [pdf
SCRL(2021), paper [pdf]
MoCov3(2021), paper [pdf]
DINO(2021), paper [pdf]
EsViT(2021), paper [pdf]
Masked Autoencoders Are Scalable Vision Learners(2021), paper [pdf]
Self-supervised Learning for Video Correspondence Flow(2019), paper [pdf]
Learning Correspondence from the Cycle-consistency of Time(2019), paper [pdf]
Joint-task Self-supervised Learning for Temporal Correspondence(2019), paper [pdf]
Space-Time Correspondence as a Contrastive Random Walk(2020), paper [pdf]
Contrastive Transformation for Self-supervised Correspondence Learning(2020), paper [pdf]
Mining Better Samples for Contrastive Learning of Temporal Correspondence(2021), paper [pdf]
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency, paper [pdf]
ViCC(2021), paper [pdf]
Temporal ensembling for semi-supervised learning(2016) , paper [pdf]
Consistency-based Semi-supervised Learning for Object Detection(2019), paper [pdf]
PseudoSeg, Designing Pseudo Labels for Semantic Segmentation(2020), paper [pdf]
ReCo, Bootstrapping Semantic Segmentation with Regional Contrast(2021), paper [pdf]
Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision(2021), paper [pdf]
Soft Teacher(2021), End-to-End Semi-Supervised Object Detection with Soft Teacher, paper [pdf]
CaSP(2021), Class-agnostic Semi-Supervised Pretraining for Detection & Segmentation, paper [pdf]
Class Activation Map(CAM), Learning Deep Features for Discriminative Localization, paper [pdf]
Grad-CAM, Visual Explanations from Deep Networks via Gradient based Localization, paper [pdf]
Zoom-CAM, Generating Fine-grained Pixel Annotations from Image Labels(2020), paper [pdf]
GETAM: Gradient-weighted Element-wise Transformer Attention Map for Weakly-supervised Semantic Segmentation(2021), paper [pdf]
Learning Spatiotemporal Features with 3D Convolutional Network(2014), paper [pdf]
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset(2017), paper [pdf]
GCNet(2019), paper [pdf]
Drop an Octave(2019), paper [pdf]
TimeSformer(2021), paper [pdf], Youtube [link]
ViViT(2021), paper [pdf]
MViT(2021), paper [pdf]
X-ViT(2021), paper [pdf]
Video Swin Transformer(2021), paper [pdf]
Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition(2021), paper [pdf]
DeViSE, A Deep Visual-Semantic Embedding Model(2013), paper [pdf]
Zero-shot Learning via Shared-Reconstruction-Graph Pursuit(2017), paper [pdf]
A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts(2017), paper [pdf]
f-VAEGAN-D2, A Feature Generating Framework for Any Shot Learning(2019), paper [pdf]
TCN(2019), Transferable Contrastive Network for Generalized Zero-Shot Learning, paper [pdf]
Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective(2019), paper [pdf]
Convolutional Prototype Learning for Zero-Shot Recognition(2019), paper [pdf]
DRN, Class-Prototype Discriminative Network for Generalized Zero-Shot Learning(2020), paper [pdf]
DAZLE(2020), Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention, paper [pdf]
IPN(2021), Isometric Propagation Network for Generalized Zero-Shot Learning, paper [pdf]
CE-GZSL(2021), Contrastive Embedding for Generalized Zero-Shot Learning, paper [pdf]
Task-Independent Knowledge Makes for Transferable Represenatations for Generalized Zero-Shot Learning(2021), paper [pdf]
Zero-Shot Learning via Contrastive Learning on Dual Knowledge Graphs(2021), paper [pdf]
FREE: Feature Refinement for Generalized Zero-Shot Learning(2021), paper [pdf]
ALIGN(2021), Scaling Up Visual and Vision-Language Representation Learning with Noisy Text Supervision, paper [pdf]
LiT: Zero-Shot Transfer with Locked-image Text Tuning(2021), paper [pdf]
Generalized Category Discovery(2022), paper [pdf]
Synthesizing the Unseen for Zero-shot Object Detection(2020), paper [pdf]
ViLD(2021), Open-Vocabulary Object Detection via Vision and Language Knowledge Distillation, paper [pdf]
Robust Region Feature Synthesizer for Zero-Shot Object Detection(2022), paper [pdf]
Detic(2022), Detecting Twenty-thousand Classes using Image-level Supervision, paper [pdf]
Zero-Shot Semantic Segmentation(2019), paper [pdf]
Semantic Projection Network for Zero- and Few-Label Semantic Segmentation(2020), paper [pdf]
Learning unbiased zero-shot semantic segmentation networks via transductive transfer(2020), paper [pdf]
A review of Generalized Zero-Shot Learning Methods(2020), paper [pdf]
Consistent Structural Relation Learning for Zero-Shot Segmentation(2020, paper [pdf]
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation(2020), paper [pdf]
Context-aware Feature Generation for Zero-shot Semantic Segmentation(2020), paper [pdf]
Recursive Training for Zero-Shot Semantic Segmentation(2021), paper [pdf]
Zero-Shot Instance Segmentation(2021), paper [pdf]
A Closer Look at Self-training for Zero-Label Segmantic Segmentation(2021), paper [pdf]
Prototypical Matching and Open Seg Rejection for Zero-Shot Semantic Segmentation(2021), paper [pdf]
SIGN(2021), Spatial-information Incorporated Generative Network for GGeneralized Zero-shot Semantic Segmentation, paper [pdf]
Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation(2021), paper [pdf]
Zero-Shot Semantic Segmentation via Spatial and Multi-Scale Aware Visual Class Embedding, paper [pdf]
DenseCLIP: Extract Free Dence Labels from CLIP(2021), paper [pdf]
Decoupling Zero-Shot Semantic Segmentation(2021), paper [pdf]
A Simple Baseline for Zero-Shot Semantic Segmentation with Pre-trained Vision-language Model(2021), paper [pdf]
cv
CPT, Colorful Prompt Tuning for Pre-trained Vision-Language Models
CLIP-Adapter, Better Vision-Language Models with Feature Adapters(2021)
Tip-Adapter, Training-free CLIP-Adapter for Better Vision-Language Modeling
DenseCLIP, Language-Guided Dense Prediction with Context-Aware Prompting(2021)
Prompting Visual-Language Models for Efficient Video Understanding, paper [pdf]
Conditianl Prompt Learning for Visiona-Language Models, paper [pdf]
nlp
PyTorch 구현 코드로 살펴보는 SRCNNe(2014), PyTorch Code [Google Colab / Blog Posting], paper [pdf]
FlowNet(2015), paper [pdf]
PWC-Net(2017), paper [pdf]
Residual Non-local Attention Networks for Image Restoration(2019), paper [pdf]
Convolutional-Recursive Deep Learning for 3D Object Classification(2012), paper [pdf]
PointNet(2016), paper [pdf]
Set Transformer(2018), paper [pdf]
Centroid Transformer(2021), paper [pdf]
PyTorch 코드로 살펴보는 Seq2Seq(2014), PyTorch Code [Google Colab / Blog Posting], paper [pdf]
PyTorch 코드로 살펴보는 Attention(2015), paper [odf]
PyTorch 코드로 살펴보는 Convolutional Sequence to Sequence Learning(2017), paper [pdf]
PyTorch 코드로 살펴보는 Transforemr(2017), paper [pdf]
BERT(2018), paper [pdf]
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators(2020), paper [pdf]
PyTorch 구현 코드로 살펴보는 GAN(2014), PyTorch Code [Google Colab / Blog Posting], paper [pdf]
PyTorch 구현 코드로 살펴보는 CGAN(2014), PyTorch Code [Google Colab / Blog Posting], paper [pdf]
PyTorch 구현 코드로 살펴보는 DCGAN(2015), PyTorch Code [Google Colab / Blog Posting], paper [pdf]
PyTorch 구현 코드로 살펴보는 Pix2Pix(2016), PyTorch Code [Google Colab / Blog Posting], paper [pdf]
Class-Balanced Loss(2019), paper [pdf]
Seesaw Loss for Long-Tailed Instance Segmentation(2020), paper [pdf]
CutMix(2019), paper [pdf]
Learning Data Augmentation Strategies for Object Detection(2019, paper [pdf]
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation(2020), paper [pdf]
Group Normalization(2018), paper [pdf]
Cross iteration BN(2020), paper [pdf]