Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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/visualaikhu/monowad

공식 코드

MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection

https://github.com/viswas1/TinyML

공식 코드

Real Time Object Detection System with YOLO and CNN Models: A Review

https://github.com/visym/keynet

공식 코드

Key-Nets: Optical Transformation Convolutional Networks for Privacy Preserving Vision Sensors

https://github.com/vita-epfl/butterflydetector

공식 코드

Perceiving Traffic from Aerial Images

https://github.com/vita-epfl/monoloco

공식 코드

MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation

https://github.com/vita-group/slak

공식 코드

More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity

https://github.com/vitae-transformer/bevsimdet

공식 코드

SimDistill: Simulated Multi-modal Distillation for BEV 3D Object Detection

https://github.com/vitae-transformer/mtp

공식 코드

MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining

https://github.com/vitae-transformer/qformer

공식 코드

Vision Transformer with Quadrangle Attention

https://github.com/vitae-transformer/remote-sensing-rvsa

공식 코드

Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model

https://github.com/vitae-transformer/rsp

공식 코드

An Empirical Study of Remote Sensing Pretraining

https://github.com/vitae-transformer/samrs

공식 코드

SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model

https://github.com/vitae-transformer/simdistill

공식 코드

SimDistill: Simulated Multi-modal Distillation for BEV 3D Object Detection

https://github.com/vitae-transformer/vitae-transformer-remote-sensing

공식 코드

An Empirical Study of Remote Sensing Pretraining

https://github.com/vitae-transformer/vitae-vsa

공식 코드

VSA: Learning Varied-Size Window Attention in Vision Transformers

https://github.com/vkakerbeck/FastConceptMapping

공식 코드

Fast Concept Mapping: The Emergence of Human Abilities in Artificial Neural Networks when Learning Embodied and Self-Supervised

https://github.com/vkalogeiton/yto-dataset

공식 코드

Analysing domain shift factors between videos and images for object detection

https://github.com/vladimirescu/robustdehazing

공식 코드

Fine-Tuning Adversarially-Robust Transformers for Single-Image Dehazing

https://github.com/vlbthambawita/divergent-nets

공식 코드

DivergentNets: Medical Image Segmentation by Network Ensemble

https://github.com/vlfom/rncdl

공식 코드

Learning to Discover and Detect Objects

https://github.com/vniclas/lidar_beam_selection

공식 코드

End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization

https://github.com/voyazici/visual-transformers-classification

공식 코드

Visual Transformers with Primal Object Queries for Multi-Label Image Classification

https://github.com/vs-cv/sf-yolo

공식 코드

Source-Free Domain Adaptation for YOLO Object Detection

https://github.com/walzimmer/3d-bat

공식 코드

TUMTraf V2X Cooperative Perception Dataset

https://github.com/wandahangfy/yolov11-rgbt

공식 코드

YOLOv11-RGBT: Towards a Comprehensive Single-Stage Multispectral Object Detection Framework

https://github.com/wang-chen/KCC

공식 코드

Kernel Cross-Correlator

https://github.com/wangbo-zhao/WSVSOD

공식 코드

Weakly Supervised Video Salient Object Detection

https://github.com/wangchen1801/fpd

공식 코드

Fine-Grained Prototypes Distillation for Few-Shot Object Detection

https://github.com/wangck20/globalmamba

공식 코드

GlobalMamba: Global Image Serialization for Vision Mamba

https://github.com/wangck20/opera

공식 코드

OPERA: Omni-Supervised Representation Learning with Hierarchical Supervisions

https://github.com/wangck20/v2m

공식 코드

V2M: Visual 2-Dimensional Mamba for Image Representation Learning

https://github.com/wanggrun/Adaptively-Connected-Neural-Networks

공식 코드

Adaptively Connected Neural Networks

https://github.com/wangguanan/denoiserep

공식 코드

DenoiseRep: Denoising Model for Representation Learning

https://github.com/wangguojun2018/CenterNet3d

공식 코드

CenterNet3D: An Anchor Free Object Detector for Point Cloud

https://github.com/wangguojun2018/MVAF-Net

공식 코드

Multi-View Adaptive Fusion Network for 3D Object Detection

https://github.com/wanghao9610/ov-dino

공식 코드

OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion

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