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/PanoAsh/F-360iSOD

공식 코드

A Fixation-based 360° Benchmark Dataset for Salient Object Detection

https://github.com/PanoAsh/SA-Net

공식 코드

Learning Synergistic Attention for Light Field Salient Object Detection

https://github.com/PanoAsh/SHD360

공식 코드

SHD360: A Benchmark Dataset for Salient Human Detection in 360° Videos

https://github.com/Panorama-Bill/F-360iSOD

공식 코드

A Fixation-based 360° Benchmark Dataset for Salient Object Detection

https://github.com/ParallelDots/generic-sku-detection-benchmark

공식 코드

Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection

https://github.com/Pealing/DFFT

공식 코드

Efficient Decoder-free Object Detection with Transformers

https://github.com/PeizeSun/OneNet

공식 코드

What Makes for End-to-End Object Detection?

https://github.com/PeizeSun/SparseR-CNN

공식 코드

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

https://github.com/PeizeSun/TransTrack

공식 코드

TransTrack: Multiple Object Tracking with Transformer

https://github.com/PengyiZhang/SlimYOLOv3

공식 코드

SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications

https://github.com/Pengyu-gis/Historical-Aerial-Photos

공식 코드

A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial Imagery

https://github.com/PerdonLiu/CSE-Autoloss

공식 코드

Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search

https://github.com/Peterande/D-FINE

공식 코드

D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement

https://github.com/PingoLH/Pytorch-HarDNet

공식 코드

HarDNet: A Low Memory Traffic Network

https://github.com/Pointcept/OpenIns3D

공식 코드

OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation

https://github.com/Pointcept/Pointcept

공식 코드

Point Transformer

https://github.com/PramaLLC/BEN

공식 코드

BEN: Using Confidence-Guided Matting for Dichotomous Image Segmentation

https://github.com/ProfHubert/Centroid

공식 코드

A novel centroid update approach for clustering-based superpixel methods and superpixel-based edge detection

https://github.com/ProfHubert/ICASSP

공식 코드

A novel centroid update approach for clustering-based superpixel methods and superpixel-based edge detection

https://github.com/Project-MONAI/MONAI

공식 코드

MONAI: An open-source framework for deep learning in healthcare

https://github.com/ProvenceStar/PartGLEE

공식 코드

PartGLEE: A Foundation Model for Recognizing and Parsing Any Objects

https://github.com/QingbeiGuo/SG-CNN

공식 코드

Self-grouping Convolutional Neural Networks

https://github.com/QingyongHu/SoTA-Point-Cloud

공식 코드

Deep Learning for 3D Point Clouds: A Survey

https://github.com/Qualcomm-AI-research/delta-distillation

공식 코드

Delta Distillation for Efficient Video Processing

https://github.com/Radar-Camera-Fusion/Awesome-Radar-Camera-Fusion

공식 코드

Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review

https://github.com/RalphMao/PatchNet

공식 코드

PatchNet -- Short-range Template Matching for Efficient Video Processing

https://github.com/RalphMao/VMetrics

공식 코드

A Delay Metric for Video Object Detection: What Average Precision Fails to Tell

https://github.com/Rishit-dagli/CPPE-Dataset

공식 코드

CPPE-5: Medical Personal Protective Equipment Dataset

https://github.com/RituYadav92/Radar-RGB-Attentive-Multimodal-Object-Detection

공식 코드

Radar+RGB Attentive Fusion for Robust Object Detection in Autonomous Vehicles

https://github.com/Robert-Mar/VFA

공식 코드

Voxelized 3D Feature Aggregation for Multiview Detection

https://github.com/Rock-100/MonoDet

공식 코드

Geometry-based Distance Decomposition for Monocular 3D Object Detection

https://github.com/Rokken-lab6/Failure-Analysis-and-Model-Repairment

공식 코드

A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging

https://github.com/Ruiyang-061X/UA3D

공식 코드

Harnessing Uncertainty-aware Bounding Boxes for Unsupervised 3D Object Detection

https://github.com/RunpeiDong/DGMS

공식 코드

Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks

https://github.com/RuoyuChen10/CCL-FSOD

공식 코드

Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection

https://github.com/RyanXLi/OneshotDet

공식 코드

One-Shot Object Detection without Fine-Tuning

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