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2D Classification

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/LarryJiang134/Image_manipulation_detection

Learning Rich Features for Image Manipulation Detection

https://github.com/Larryliu912/Vincent-2.0

A Neural Algorithm of Artistic Style

https://github.com/Launch-on-Titania/DynOPETs

DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera Scenarios

https://github.com/LaurentBerger/neuralstyle

A Neural Algorithm of Artistic Style

https://github.com/Lavender105/DFF

CASENet: Deep Category-Aware Semantic Edge Detection

https://github.com/LdDl/go-darknet

YOLOv3: An Incremental Improvement

https://github.com/LdDl/odam

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/LdDl/rust-road-traffic

YOLOv3: An Incremental Improvement

https://github.com/LeHoaiLinh278/mmdetection_dev

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/LeMU-Haruka/reconet-mindspore

ReCoNet: Real-time Coherent Video Style Transfer Network

https://github.com/LearningHx/StyA2K

All-to-key Attention for Arbitrary Style Transfer

https://github.com/Lebhoryi/keras-YOLOv3-model-set

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/LebronGG/PointNet

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/Lechatelia/detection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/LeeDongYeun/keras-m2det

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

https://github.com/LeeGitaek/Kaggle_Solving

Mish: A Self Regularized Non-Monotonic Activation Function

https://github.com/LeeJunHyun/Image_Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Leeroo-AI/mergoo

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/Lees001/FastPhotoStyle

A Closed-form Solution to Photorealistic Image Stylization

https://github.com/Leg-end/Detection

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/Legoons/Melanoma_classification

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/Legoons/Whale_Classification

Squeeze-and-Excitation Networks

https://github.com/Leinadh/avatar-image-generator

XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings

https://github.com/Leminhbinh0209/AAAI22-ADD

Deferred Neural Rendering: Image Synthesis using Neural Textures

https://github.com/LeoXuZH/CervicalCancerDetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/LeonSun0101/CD-SD

Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional Networks

https://github.com/Leonardo-Blanger/detr_tensorflow

End-to-End Object Detection with Transformers

https://github.com/LeslieZhoa/DCT-NET.Pytorch

DCT-Net: Domain-Calibrated Translation for Portrait Stylization

https://github.com/Li-Chongyi/cmMS-ECCV20

RGB-D Salient Object Detection with Cross-Modality Modulation and Selection

https://github.com/Li-ai-cell/Interpretation_DETR

End-to-End Object Detection with Transformers

https://github.com/LiYingwei/ShapeTextureDebiasedTraining

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

https://github.com/LiangHann/USAR

Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects

https://github.com/LiangSiyuan21/Adversarial-Attacks-for-Image-and-Video-Object-Detection/tree/master/img_attack_with_attention

Transferable Adversarial Attacks for Image and Video Object Detection

https://github.com/LiangSiyuan21/BadCLIP

BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning

https://github.com/LibrarristShalinward/while_true_involute-Cream_of_the_Crop

Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search

https://github.com/LilitYolyan/CutPaste

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.