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코드

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

https://github.com/SunYM2020/UA-CMDet

공식 코드

Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning

https://github.com/Sunmi-AI-Lab/head-detection-and-tracking

공식 코드

Towards in-store multi-person tracking using head detection and track heatmaps

https://github.com/SwinTransformer/Feature-Distillation

공식 코드

Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation

https://github.com/SwinTransformer/Transformer-SSL

공식 코드

Self-Supervised Learning with Swin Transformers

https://github.com/SymbioticLab/FedScale

공식 코드

FedScale: Benchmarking Model and System Performance of Federated Learning at Scale

https://github.com/SysCV/qdtrack

공식 코드

Quasi-Dense Similarity Learning for Multiple Object Tracking

https://github.com/TAMU-VITA/UAV-NDFT

공식 코드

Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach

https://github.com/THU-MIG/yolov10

공식 코드

YOLOv10: Real-Time End-to-End Object Detection

https://github.com/THUrssq/Tianchi04

공식 코드

Sparse Adversarial Attack to Object Detection

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/BlockFusion

공식 코드

BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation

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/Tillyhere/TADT

공식 코드

Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution

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/Tombs98/ALGNet

공식 코드

Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring

https://github.com/Tombs98/CAPTNet

공식 코드

Prompt-based Ingredient-Oriented All-in-One Image Restoration

https://github.com/Tombs98/M3SNet

공식 코드

A Mountain-Shaped Single-Stage Network for Accurate Image Restoration

https://github.com/Tombs98/MSSFNet

공식 코드

Learning Accurate and Enriched Features for Stereo Image Super-Resolution

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

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