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/xinge008/SSN

공식 코드

SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds

https://github.com/xinghaochen/DECO

공식 코드

DECO: Query-Based End-to-End Object Detection with ConvNets

https://github.com/xinghaochen/slab

공식 코드

SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization

https://github.com/xingmingyu123456/knowledgeswapping

공식 코드

Knowledge Swapping via Learning and Unlearning

https://github.com/xingyizhou/CenterNet

공식 코드

Objects as Points

https://github.com/xingyizhou/CenterNet2

공식 코드

Probabilistic two-stage detection

https://github.com/xingyizhou/CenterTrack

공식 코드

Tracking Objects as Points

https://github.com/xingyizhou/ExtremeNet

공식 코드

Bottom-up Object Detection by Grouping Extreme and Center Points

https://github.com/xingyizhou/UniDet

공식 코드

Simple multi-dataset detection

https://github.com/xingyu-group/3d-density-resampling-sdg

공식 코드

Domain Generalization of 3D Object Detection by Density-Resampling

https://github.com/xinliu20/mec

공식 코드

Self-Supervised Learning via Maximum Entropy Coding

https://github.com/xinshuoweng/mono3D_PLiDAR

공식 코드

Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud

https://github.com/xinw1012/cycle-confusion

공식 코드

Robust Object Detection via Instance-Level Temporal Cycle Confusion

https://github.com/xinzhuma/3dodi-survey

공식 코드

3D Object Detection from Images for Autonomous Driving: A Survey

https://github.com/xinzhuma/monodle

공식 코드

Delving into Localization Errors for Monocular 3D Object Detection

https://github.com/xiuqhou/Salience-DETR

공식 코드

Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement

https://github.com/xiuqhou/relation-detr

공식 코드

Relation DETR: Exploring Explicit Position Relation Prior for Object Detection

https://github.com/xjtlu-cvlab/3drom

공식 코드

3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization

https://github.com/xjtu-haolin/point_assisted_sample_selection

공식 코드

Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection

https://github.com/xk-huang/segment-caption-anything

공식 코드

Segment and Caption Anything

https://github.com/xmuqimingxia/dota

공식 코드

Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels

https://github.com/xmuqimingxia/sp3d

공식 코드

SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts

https://github.com/xternalz/AE-WTN

공식 코드

Scaling Object Detection by Transferring Classification Weights

https://github.com/xudangliatiger/ape-loss

공식 코드

Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection

https://github.com/xuebinqin/DIS

공식 코드

Highly Accurate Dichotomous Image Segmentation

https://github.com/xueliancheng/hybridstereonet-main

공식 코드

Deep Laparoscopic Stereo Matching with Transformers

https://github.com/xuez-phd/efficient-rgb-t-early-fusion-detection

공식 코드

Rethinking Early-Fusion Strategies for Improved Multispectral Object Detection

https://github.com/xuez-phd/tfdet

공식 코드

TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection

https://github.com/xushilin1/dst-det

공식 코드

DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection

https://github.com/xuxw98/backtoreality

공식 코드

Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement

https://github.com/xuxw98/dspdet3d

공식 코드

3D Small Object Detection with Dynamic Spatial Pruning

https://github.com/xuyangbai/transfusion

공식 코드

TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers

https://github.com/xvjiarui/GCNet

공식 코드

GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

https://github.com/xwmaxwma/tinyvim

공식 코드

TinyViM: Frequency Decoupling for Tiny Hybrid Vision Mamba

https://github.com/xy-guo/LIGA-Stereo

공식 코드

LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector

https://github.com/y2863/MetaGraspNet

공식 코드

MetaGraspNet_v0: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis

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