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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/Megvii-BaseDetection/GFSD

공식 코드

Generalized Few-Shot Object Detection without Forgetting

https://github.com/Megvii-BaseDetection/LLA

공식 코드

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection

https://github.com/Megvii-BaseDetection/OTA

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OTA: Optimal Transport Assignment for Object Detection

https://github.com/Megvii-BaseDetection/YOLOX

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YOLOX: Exceeding YOLO Series in 2021

https://github.com/Megvii-Nanjing/CR-DA-DET

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Exploring Categorical Regularization for Domain Adaptive Object Detection

https://github.com/MegviiDetection/FSCE

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FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding

https://github.com/Mehrdad-Noori/CAGNet

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CAGNet: Content-Aware Guidance for Salient Object Detection

https://github.com/Mehrdad-Noori/Saliency-Evaluation-Toolbox

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CAGNet: Content-Aware Guidance for Salient Object Detection

https://github.com/MelissaChen15/control-vc

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ControlVC: Zero-Shot Voice Conversion with Time-Varying Controls on Pitch and Speed

https://github.com/Menace-Dragon/VP-QOD

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HQOD: Harmonious Quantization for Object Detection

https://github.com/MengLcool/RichSem

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Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection

https://github.com/MenghaoGuo/-EANet

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Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks

https://github.com/MenghaoGuo/Awesome-Vision-Attentions

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Attention Mechanisms in Computer Vision: A Survey

https://github.com/Metalicana/Deblurring-With-Phase-and-Dark-Channels

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Blind Image Deblurring with FFT-ReLU Sparsity Prior

https://github.com/MhLiao/TextBoxes_plusplus

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TextBoxes++: A Single-Shot Oriented Scene Text Detector

https://github.com/Mhaiyang/CVPR2021_PFNet

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Camouflaged Object Segmentation with Distraction Mining

https://github.com/MichaelFan01/STDC-Seg

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Rethinking BiSeNet For Real-time Semantic Segmentation

https://github.com/Microsoft/human-pose-estimation.pytorch

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Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/MihaelaGaman/SaRoCo

공식 코드

SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles

https://github.com/MikeS96/3d_obj_detection

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High-level camera-LiDAR fusion for 3D object detection with machine learning

https://github.com/MingiJi/FRSKD

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Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation

https://github.com/Mirmix/muviecast

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MuVieCAST: Multi-View Consistent Artistic Style Transfer

https://github.com/MohamadDabboussi/Multiview-Xray-Matching

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Self-Supervised Multiview Xray Matching

https://github.com/MohammadSadeghSalehi/ISGD

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Bilevel Learning with Inexact Stochastic Gradients

https://github.com/MonashAI/LIT

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Less is More: Pay Less Attention in Vision Transformers

https://github.com/MosheEliasof/DRIP

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DRIP: Deep Regularizers for Inverse Problems

https://github.com/MovingKyu/RACoLN

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Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization

https://github.com/MrGiovanni/Nested-UNet

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UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/MuMiN-dataset/mumin-build

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MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset

https://github.com/Muzammal-Naseer/NRP

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A Self-supervised Approach for Adversarial Robustness

https://github.com/Mxbonn/anchor_pruning

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Anchor Pruning for Object Detection

https://github.com/MyeongJin-Kim/Learning-Texture-Invariant-Representation

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Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation

https://github.com/MysteryVaibhav/fake_news_semantics

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Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification

https://github.com/NC717/DeepRA

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DeepRA: Predicting Joint Damage From Radiographs Using CNN with Attention

https://github.com/NJU-LHRS/official-CMID

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CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding

https://github.com/NJUVISION/rho-vision

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Efficient Visual Computing with Camera RAW Snapshots

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