Papers with Code 아카이브

2D Object Detection

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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/gyyang23/afpn

공식 코드

AFPN: Asymptotic Feature Pyramid Network for Object Detection

https://github.com/h751410234/datr

공식 코드

DATR: Unsupervised Domain Adaptive Detection Transformer with Dataset-Level Adaptation and Prototypical Alignment

https://github.com/haarburger/multi-scale-curriculum

공식 코드

Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification

https://github.com/hadi-ghnd/accidentdetection

공식 코드

Real-Time Accident Detection in Traffic Surveillance Using Deep Learning

https://github.com/hadi-ghnd/scrap-classification

공식 코드

Ammunition Component Classification Using Deep Learning

https://github.com/hadipardis/svc

공식 코드

Learned Scalable Video Coding For Humans and Machines

https://github.com/haibao-yu/ffnet-vic3d

공식 코드

Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Prediction

https://github.com/haihabi/generativecrb

공식 코드

Learning to Bound: A Generative Cramér-Rao Bound

https://github.com/hailanyi/cpd

공식 코드

Commonsense Prototype for Outdoor Unsupervised 3D Object Detection

https://github.com/hailanyi/virconv

공식 코드

Virtual Sparse Convolution for Multimodal 3D Object Detection

https://github.com/haiyang-w/cagroup3d

공식 코드

CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds

https://github.com/haiyang-w/dsvt

공식 코드

DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets

https://github.com/haiyang-w/rbgnet

공식 코드

RBGNet: Ray-based Grouping for 3D Object Detection

https://github.com/haiyang-w/unitr

공식 코드

UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation

https://github.com/hama-dl-dev/sesame

공식 코드

SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics

https://github.com/hamarh/HMNet_pth

공식 코드

Hierarchical Neural Memory Network for Low Latency Event Processing

https://github.com/hamperdredes/sood

공식 코드

SOOD: Towards Semi-Supervised Oriented Object Detection

https://github.com/hanchaa/meformer

공식 코드

Robust Multimodal 3D Object Detection via Modality-Agnostic Decoding and Proximity-based Modality Ensemble

https://github.com/hanchaoleng/shapeconv

공식 코드

ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation

https://github.com/hancyran/RepSurf

공식 코드

Surface Representation for Point Clouds

https://github.com/hangg7/deformable-kernels/

공식 코드

Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation

https://github.com/hangxu-cv/cvpr24acm

공식 코드

Rethinking Boundary Discontinuity Problem for Oriented Object Detection

https://github.com/hanktseng131415go/ramem

공식 코드

Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels

https://github.com/hanyue1648/reft

공식 코드

Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation

https://github.com/hao-b-shu/sdped

공식 코드

More precise edge detections

https://github.com/haochen-rye/ModEnsNet

공식 코드

Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNet

https://github.com/haoning724/obbstacking

공식 코드

OBBStacking: An Ensemble Method for Remote Sensing Object Detection

https://github.com/haoqinhong/ccspnet-joint

공식 코드

CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions

https://github.com/haoranzhuexplorer/ad-l-jepa-release

공식 코드

AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR Data

https://github.com/haotian-liu/maskpoint

공식 코드

Masked Discrimination for Self-Supervised Learning on Point Clouds

https://github.com/haoy945/demf

공식 코드

Boosting 3D Object Detection via Object-Focused Image Fusion

https://github.com/happinesslz/EPNet

공식 코드

EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection

https://github.com/happinesslz/LION

공식 코드

LION: Linear Group RNN for 3D Object Detection in Point Clouds

https://github.com/happinesslz/epnetv2

공식 코드

EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection

https://github.com/happinesslz/fbmnet

공식 코드

Multi-Modal 3D Object Detection by Box Matching

https://github.com/harish2006/cntxt_likelihood

공식 코드

Deep neural networks can be improved using human-derived contextual expectations

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