Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

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/freeniliang/caffe-ssd

SSD: Single Shot MultiBox Detector

https://github.com/fregu856/deeplabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/frgfm/Holocron

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/fsafe/Capstone

Mask R-CNN

https://github.com/fsna2022/fsna

Frustratingly Simple Few-Shot Object Detection

https://github.com/fudan-zvg/DGMN2_MindSpore_Ascend

Dynamic Graph Message Passing Networks for Visual Recognition

https://github.com/fudan-zvg/gss

DeepInteraction: 3D Object Detection via Modality Interaction

https://github.com/fuenwang/3D-BoundingBox

3D Bounding Box Estimation Using Deep Learning and Geometry

https://github.com/fujianhai/mmdet-king

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/fundamentalvision/BEVFormer

BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

https://github.com/fupiao1998/res2net-keras

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/futureisatyourhand/Top-Related-Meta-Learning-Method-for-Few-Shot-Detection

Top-Related Meta-Learning Method for Few-Shot Object Detection

https://github.com/fxfviolet/FCN_for_segmantic_segmentation

Fully Convolutional Networks for Semantic Segmentation

https://github.com/fyangneil/Clustered-Object-Detection-in-Aerial-Image

Clustered Object Detection in Aerial Images

https://github.com/fyangneil/DSFPN

Dually Supervised Feature Pyramid for Object Detection and Segmentation

https://github.com/fzi-forschungszentrum-informatik/mrf

SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth

https://github.com/fzj-inm1-bda/neurips22-cell-seg

Contour Proposal Networks for Biomedical Instance Segmentation

https://github.com/fznsakib/great-ape-behaviour-detector

Focal Loss for Dense Object Detection

https://github.com/gabinsane/multi-vaes-in-robotics

Multimodal Generative Models for Scalable Weakly-Supervised Learning

https://github.com/gabinsane/multimodal-vae-comparison

Multimodal Generative Models for Scalable Weakly-Supervised Learning

https://github.com/gaborvecsei/Barlow-Twins

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/gabrieldernbach/barlow-twins

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/gabrielkasmi/pypvroof

Monitoring Spatial Sustainable Development: semi-automated analysis of Satellite and Aerial Images for Energy Transition and Sustainability Indicators

https://github.com/gaelmoccand/RoadSegmentation_CNN

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/gakkifan2020/YoloV3_From_calmisential_workspace

YOLOv3: An Incremental Improvement

https://github.com/gakkiri/AugFPN-detectron2

AugFPN: Improving Multi-scale Feature Learning for Object Detection

https://github.com/gakkiri/Filter-Response-Normalization

Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks

https://github.com/gallegi/T4E_MICCAI_BrainTumor

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

https://github.com/galprz/brain-tumor-segemntation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/galprz/brain-tumor-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/gamilton211/Object-detection-YOLO

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

https://github.com/gan3sh500/attention-augmented-conv

Attention Augmented Convolutional Networks

https://github.com/gan3sh500/custom-pooling

CBAM: Convolutional Block Attention Module

https://github.com/gan3sh500/dropblock

DropBlock: A regularization method for convolutional networks

https://github.com/gandolfczjh/3d2fool

Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches

https://github.com/ganeshpawar95/removebg1

U-Net: Convolutional Networks for Biomedical Image Segmentation

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