Papers with Code 아카이브

2D Object Detection

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하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

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

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

공식 코드

YOLOX: Exceeding YOLO Series in 2021

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

공식 코드

Exploring Categorical Regularization for Domain Adaptive Object Detection

https://github.com/MegviiDetection/FSCE

공식 코드

FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding

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

공식 코드

CAGNet: Content-Aware Guidance for Salient Object Detection

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

공식 코드

CAGNet: Content-Aware Guidance for Salient Object Detection

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

공식 코드

HQOD: Harmonious Quantization for Object Detection

https://github.com/MengLcool/RichSem

공식 코드

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

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

공식 코드

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

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

공식 코드

Attention Mechanisms in Computer Vision: A Survey

https://github.com/MengyangPu/RINDNet

공식 코드

RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth

https://github.com/MhLiao/TextBoxes_plusplus

공식 코드

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

https://github.com/Mhaiyang/CVPR2021_PFNet

공식 코드

Camouflaged Object Segmentation with Distraction Mining

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

공식 코드

Rethinking BiSeNet For Real-time Semantic Segmentation

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

공식 코드

Deep High-Resolution Representation Learning for Human Pose Estimation

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

공식 코드

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

https://github.com/MingiJi/FRSKD

공식 코드

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

https://github.com/Miraclelzk/STAR-Edge

공식 코드

STAR-Edge: Structure-aware Local Spherical Curve Representation for Thin-walled Edge Extraction from Unstructured Point Clouds

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

공식 코드

Self-Supervised Multiview Xray Matching

https://github.com/MonashAI/LIT

공식 코드

Less is More: Pay Less Attention in Vision Transformers

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

공식 코드

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

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

공식 코드

A Self-supervised Approach for Adversarial Robustness

https://github.com/Mxbonn/anchor_pruning

공식 코드

Anchor Pruning for Object Detection

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

공식 코드

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

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

공식 코드

Efficient Visual Computing with Camera RAW Snapshots

https://github.com/NUAAXQ/MLCVNet

공식 코드

MLCVNet: Multi-Level Context VoteNet for 3D Object Detection

https://github.com/NUST-Machine-Intelligence-Laboratory/HFAN

공식 코드

Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation

https://github.com/NVlabs/BundleSDF

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BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects

https://github.com/NVlabs/CenterPose

공식 코드

Single-Stage Keypoint-Based Category-Level Object Pose Estimation from an RGB Image

https://github.com/NVlabs/DICOD

공식 코드

Distilling Image Classifiers in Object Detectors

https://github.com/NVlabs/FasterViT

공식 코드

FasterViT: Fast Vision Transformers with Hierarchical Attention

https://github.com/NVlabs/FocalFormer3D

공식 코드

FocalFormer3D : Focusing on Hard Instance for 3D Object Detection

https://github.com/NVlabs/FoundationStereo

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FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

https://github.com/NVlabs/GroupViT

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GroupViT: Semantic Segmentation Emerges from Text Supervision

https://github.com/NVlabs/SegFormer

공식 코드

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/NVlabs/wetectron

공식 코드

Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection

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