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

https://github.com/hanyue1648/reft

공식 코드

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

https://github.com/hanzhouliu/deblurdinat

공식 코드

DeblurDiNAT: A Compact Model with Exceptional Generalization and Visual Fidelity on Unseen Domains

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/haoyuc/A2N

공식 코드

Attention in Attention Network for Image Super-Resolution

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

https://github.com/harrytea/root

공식 코드

ROOT: VLM based System for Indoor Scene Understanding and Beyond

https://github.com/harsh-99/SCL

공식 코드

SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses

https://github.com/haru-zt/ddpls

공식 코드

Density-Guided Dense Pseudo Label Selection For Semi-supervised Oriented Object Detection

https://github.com/harveymannering/text-to-image-bias

공식 코드

Analysing Gender Bias in Text-to-Image Models using Object Detection

https://github.com/harveyp123/iccd_sptrn_slr

공식 코드

Towards Sparsification of Graph Neural Networks

https://github.com/hasanirtiza/Pedestron

공식 코드

Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection

https://github.com/hashmatshadab/apr

공식 코드

Adversarial Pixel Restoration as a Pretext Task for Transferable Perturbations

https://github.com/hashmatshadab/mambarobustness

공식 코드

Towards Evaluating the Robustness of Visual State Space Models

https://github.com/hasibzunair/peekaboo

공식 코드

PEEKABOO: Hiding parts of an image for unsupervised object localization

https://github.com/hassony2/homan

공식 코드

Towards unconstrained joint hand-object reconstruction from RGB videos

https://github.com/hatins/deoe

공식 코드

Detecting Every Object from Events

https://github.com/hayyubi/cur_vl

공식 코드

Learning from Children: Improving Image-Caption Pretraining via Curriculum

https://github.com/hb-stone/FC-SOD

공식 코드

Few-Cost Salient Object Detection with Adversarial-Paced Learning

https://github.com/hbilen/WSDDN

공식 코드

Weakly Supervised Deep Detection Networks

https://github.com/hcplab-sysu/causalvlr

공식 코드

Cross-Modal Causal Intervention for Medical Report Generation

https://github.com/hdeplaen/uotod

공식 코드

Unbalanced Optimal Transport: A Unified Framework for Object Detection

https://github.com/hdsullivan/ressr

공식 코드

ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images

https://github.com/heboyong/Diffusion-Domain-Teacher

공식 코드

Diffusion Domain Teacher: Diffusion Guided Domain Adaptive Object Detector

https://github.com/heboyong/Generalized-Diffusion-Detector

공식 코드

Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized Detection

https://github.com/heboyong/fitness-generalization-transferability

공식 코드

Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and Transferability

https://github.com/hehaodong530/dota-c

공식 코드

On the Robustness of Object Detection Models on Aerial Images

https://github.com/heitorcfelix/singleshot6Dpose

공식 코드

Squeezed Deep 6DoF Object Detection Using Knowledge Distillation

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