Papers with Code 아카이브

Object

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Replace the cat with a British Shorthair cat of the breed with bulging yellow eyes

코드

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

https://github.com/Yonder-OSS/D3M-Primitives

Focal Loss for Dense Object Detection

https://github.com/Yongjjun/singleshotpose

Real-Time Seamless Single Shot 6D Object Pose Prediction

https://github.com/YoruCathy/USDSeg-FCOS

Learning Universal Shape Dictionary for Realtime Instance Segmentation

https://github.com/Yotonctu/densefusion_torch1.0

PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

https://github.com/YoungXIAO13/FewShotDetection

Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild

https://github.com/YoungXIAO13/PoseContrast

Pose from Shape: Deep Pose Estimation for Arbitrary 3D Objects

https://github.com/YoungXIAO13/PoseFromShape

Learning Local RGB-to-CAD Correspondences for Object Pose Estimation

https://github.com/YuZiHanorz/stacked_capsule_autoencoders

Stacked Capsule Autoencoders

https://github.com/YudeWang/UNet-Satellite-Image-Segmentation

Feature Pyramid Networks for Object Detection

https://github.com/YukangWang/DeepFlux

DeepFlux for Skeletons in the Wild

https://github.com/Yuma82/Paper-Survey

ODAM: Gradient-based instance-specific visual explanations for object detection

https://github.com/YutingZhang/lmdis-rep

Unsupervised Discovery of Object Landmarks as Structural Representations

https://github.com/Yuxiang1995/ICDAR2021_MFD

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Yuzi0123/MindSpore_Yolov5

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Yzichen/FlashOCC

UltimateDO: An Efficient Framework to Marry Occupancy Prediction with 3D Object Detection via Channel2height

https://github.com/ZF4444/MMAL-Net

Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization

https://github.com/ZJUT-ERCISS/zjut_mindvideo/blob/master/msvideo/models/fairmot.py

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/ZQPei/deep_sort_pytorch

https://github.com/ZTao-z/multiflow-resnet-ssd

Focal Loss for Dense Object Detection

https://github.com/ZUCCBBQ/yoloV3-

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/ZZWENG/holistic_mesh

Holistic 3D Human and Scene Mesh Estimation from Single View Images

https://github.com/Zanderzt/TensorRT_Mask_RCNN

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Zeng555/EMV-YOLO

Getting to Know Low-light Images with The Exclusively Dark Dataset

https://github.com/Zengyi-Qin/TLNet

Triangulation Learning Network: from Monocular to Stereo 3D Object Detection

https://github.com/ZhZiKai/VisDrone_FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Zhang-Jing-Xuan/MaskRCNN

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/ZhangGongjie/CAD-Net

CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery

https://github.com/ZhangJiajun1995/cs231n-camp

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

https://github.com/ZhangwenguangHikvision/YoloV5_JDE_TensorRT_for_Track

Towards Real-Time Multi-Object Tracking

https://github.com/Zhangyongtao123/maskrcnn_benchmark

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/ZhenJie-Zhang/darknet_shoe

YOLO9000: Better, Faster, Stronger

https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/ZhiqiWang12/try_darknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/ZhixinLai/3D-detection-with-monocular-RGB-image

3D Bounding Box Estimation Using Deep Learning and Geometry

https://github.com/ZhouKai90/face-detection-ssd-mxnet

SSD: Single Shot MultiBox Detector

https://github.com/Zoushuang86/quiz_w8

SSD: Single Shot MultiBox Detector

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