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보존된 평가 기록

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코드

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

https://github.com/cv-zhangxin/akconv

공식 코드

LDConv: Linear deformable convolution for improving convolutional neural networks

https://github.com/cv-zhangxin/ldconv

공식 코드

LDConv: Linear deformable convolution for improving convolutional neural networks

https://github.com/cv516buaa/ov-vg

공식 코드

OV-VG: A Benchmark for Open-Vocabulary Visual Grounding

https://github.com/cviaai/dasha

공식 코드

DASHA: Decentralized Autofocusing System with Hierarchical Agents

https://github.com/cvignac/top-n

공식 코드

Top-N: Equivariant set and graph generation without exchangeability

https://github.com/cvlab-epfl/mvflow

공식 코드

Multi-view Tracking Using Weakly Supervised Human Motion Prediction

https://github.com/cvlab-stonybrook/DocIIW

공식 코드

Intrinsic Decomposition of Document Images In-the-Wild

https://github.com/cvlab-stonybrook/SID

공식 코드

Physics-based Shadow Image Decomposition for Shadow Removal

https://github.com/cvlab-stonybrook/Weighting-Pseudo-Labels

공식 코드

Weighting Pseudo-Labels via High-Activation Feature Index Similarity and Object Detection for Semi-Supervised Segmentation

https://github.com/cvlab-yonsei/HVPR

공식 코드

HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection

https://github.com/cvmi-lab/codet

공식 코드

CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection

https://github.com/cvmi-lab/fs3d

공식 코드

Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection

https://github.com/cvmi-lab/sparsekd

공식 코드

Towards Efficient 3D Object Detection with Knowledge Distillation

https://github.com/cvmi-lab/syncood

공식 코드

Can OOD Object Detectors Learn from Foundation Models?

https://github.com/cxliu0/oa-mil

공식 코드

Robust Object Detection With Inaccurate Bounding Boxes

https://github.com/cxsghost/cnnsum

공식 코드

CNNSum: Exploring Long-Context Summarization with Large Language Models in Chinese Novels

https://github.com/cxt98/ACF_perception

공식 코드

Manipulation-Oriented Object Perception in Clutter through Affordance Coordinate Frames

https://github.com/cxy1997/3D_adapt_auto_driving

공식 코드

Train in Germany, Test in The USA: Making 3D Object Detectors Generalize

https://github.com/cxy1997/DiffuBox

공식 코드

DiffuBox: Refining 3D Object Detection with Point Diffusion

https://github.com/cy94/sceneformer

공식 코드

SceneFormer: Indoor Scene Generation with Transformers

https://github.com/cybercore-co-ltd/CoLAD

공식 코드

Improving Object Detection by Label Assignment Distillation

https://github.com/cybercore-co-ltd/Single-Stage-Common-Object-Detection

공식 코드

Single Stage Class Agnostic Common Object Detection: A Simple Baseline

https://github.com/cybercore-co-ltd/colad_paper

공식 코드

Improving Object Detection by Label Assignment Distillation

https://github.com/cyprienquemeneur/fedpylot

공식 코드

FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehicles

https://github.com/czczup/vit-adapter

공식 코드

Vision Transformer Adapter for Dense Predictions

https://github.com/czhang0528/MosaicOS

공식 코드

MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection

https://github.com/czm369/mixpl

공식 코드

Mixed Pseudo Labels for Semi-Supervised Object Detection

https://github.com/d-li14/HBONet

공식 코드

HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions

https://github.com/d-robotics-ai-lab/dosod

공식 코드

A Light-Weight Framework for Open-Set Object Detection with Decoupled Feature Alignment in Joint Space

https://github.com/dadung/dge-cda

공식 코드

Assessing Domain Gap for Continual Domain Adaptation in Object Detection

https://github.com/daeunfullgrace/dyra

공식 코드

DyRA: Portable Dynamic Resolution Adjustment Network for Existing Detectors

https://github.com/daijifeng001/r-fcn

공식 코드

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/dair-iitd/degpr

공식 코드

DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting

https://github.com/daishiresearch/transnext

공식 코드

TransNeXt: Robust Foveal Visual Perception for Vision Transformers

https://github.com/daitranskku/aic2024-track4-team15

공식 코드

Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets

https://github.com/daixinghome/Distill_GID_detectron2

공식 코드

General Instance Distillation for Object Detection

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