Papers with Code 아카이브

Object Detection

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전체 기법전체 데이터셋

하위 태스크

39

평가표

124

보존된 평가 기록

977

코드

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

https://github.com/TJUMMG/DS-Net

공식 코드

DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection

https://github.com/TRAILab/CaDDN

공식 코드

Categorical Depth Distribution Network for Monocular 3D Object Detection

https://github.com/TRI-ML/permatrack

공식 코드

Learning to Track with Object Permanence

https://github.com/TRI-ML/sdflabel

공식 코드

Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors

https://github.com/TUMFTM/CameraRadarFusionNet

공식 코드

A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection

https://github.com/TUMFTM/RadarVoxelFusionNet

공식 코드

Radar Voxel Fusion for 3D Object Detection

https://github.com/Tajamul21/Grounded_Teacher

공식 코드

Context Aware Grounded Teacher for Source Free Object Detection

https://github.com/Tencent/tencent-ml-images

공식 코드

Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning

https://github.com/TencentYoutuResearch/PedestrianDetection-NohNMS

공식 코드

NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination

https://github.com/Tete-Xiao/ReSim

공식 코드

Region Similarity Representation Learning

https://github.com/TheShadow29/zsgnet-pytorch

공식 코드

Zero-Shot Grounding of Objects from Natural Language Queries

https://github.com/Thinklab-SJTU/CSL_RetinaNet_Tensorflow

공식 코드

On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited

https://github.com/Thinklab-SJTU/DCL_RetinaNet_Tensorflow

공식 코드

Dense Label Encoding for Boundary Discontinuity Free Rotation Detection

https://github.com/Thinklab-SJTU/R3Det_Tensorflow

공식 코드

R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

https://github.com/Thinklab-SJTU/S2TLD

공식 코드

SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing

https://github.com/TianshuXie/Cut-Thumbnail

공식 코드

Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network

https://github.com/TixXx1337/Federated-Learning-with-Heterogeneous-Data-Handling-for-Robust-Vehicular-Perception

공식 코드

Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection

https://github.com/TomG008/SkyNet

공식 코드

SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems

https://github.com/TotalVariation/Exchanger4SITS

공식 코드

Revisiting the Encoding of Satellite Image Time Series

https://github.com/TotalVariation/Flattenet

공식 코드

FlatteNet: A Simple Versatile Framework for Dense Pixelwise Prediction

https://github.com/ToyotaResearchInstitute/simnet

공식 코드

SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo

https://github.com/Traffic-X/MonoLSS

공식 코드

MonoLSS: Learnable Sample Selection For Monocular 3D Detection

https://github.com/Tung-I/Dual-awareness-Attention-for-Few-shot-Object-Detection

공식 코드

Dual-Awareness Attention for Few-Shot Object Detection

https://github.com/Tverous/HRCenterNet

공식 코드

HRCenterNet: An Anchorless Approach to Chinese Character Segmentation in Historical Documents

https://github.com/UARK-AICV/TrackGUI

공식 코드

TrackMe:A Simple and Effective Multiple Object Tracking Annotation Tool

https://github.com/UMBCvision/Contextual-Adversarial-Patches

공식 코드

Role of Spatial Context in Adversarial Robustness for Object Detection

https://github.com/USTC-IMCL/Task-driven-Semantic-Coding-via-RL

공식 코드

Task-driven Semantic Coding via Reinforcement Learning

https://github.com/UVA-Computer-Vision-Lab/ovmono3d

공식 코드

Open Vocabulary Monocular 3D Object Detection

https://github.com/Uehwan/SimVODIS

공식 코드

SimVODIS: Simultaneous Visual Odometry, Object Detection, and Instance Segmentation

https://github.com/VCCIV/BLVD

공식 코드

BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving

https://github.com/VDIGPKU/CBNetV2

공식 코드

CBNet: A Composite Backbone Network Architecture for Object Detection

https://github.com/VDIGPKU/DynamicDet

공식 코드

DynamicDet: A Unified Dynamic Architecture for Object Detection

https://github.com/VDIGPKU/OPANAS

공식 코드

OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection

https://github.com/VDIGPKU/OpenAD

공식 코드

OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection

https://github.com/VIPriors/vipriors-challenges-toolkit

공식 코드

VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

https://github.com/VITA-Group/21LPCV-UAV-Solution

공식 코드

E^2TAD: An Energy-Efficient Tracking-based Action Detector

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