Papers with Code 아카이브

2D Object Detection

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전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

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

https://github.com/xlang-ai/openagents

공식 코드

OpenAgents: An Open Platform for Language Agents in the Wild

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

https://github.com/yahidar/radarformer

공식 코드

RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model

https://github.com/yan-hao-tian/ConTNet

공식 코드

ConTNet: Why not use convolution and transformer at the same time?

https://github.com/yancie-yjr/StreamYOLO

공식 코드

Real-time Object Detection for Streaming Perception

https://github.com/yancie-yjr/dbq-ssd

공식 코드

DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection

https://github.com/yang-0201/MAF-YOLO

공식 코드

Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detection

https://github.com/yang-0201/mhaf-yolo

공식 코드

MHAF-YOLO: Multi-Branch Heterogeneous Auxiliary Fusion YOLO for accurate object detection

https://github.com/yang-song/APDLM

공식 코드

Training Deep Neural Networks via Direct Loss Minimization

https://github.com/yang-yk/NP-RepMet

공식 코드

Restoring Negative Information in Few-Shot Object Detection

https://github.com/yangcao1996/fanet

공식 코드

FakeMix Augmentation Improves Transparent Object Detection

https://github.com/yangcaoai/3dgs-det

공식 코드

3DGS-DET: Empower 3D Gaussian Splatting with Boundary Guidance and Box-Focused Sampling for 3D Object Detection

https://github.com/yangcaoai/CoDA_NeurIPS2023

공식 코드

Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object Detection

https://github.com/yangfan-hu/fast-snn

공식 코드

Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN

https://github.com/yanghai-1218/radet

공식 코드

Rigidity-Aware Detection for 6D Object Pose Estimation

https://github.com/yanghtr/ssgnet

공식 코드

LiDAR-Based 3D Object Detection via Hybrid 2D Semantic Scene Generation

https://github.com/yanglei18/Lite-FPN

공식 코드

Lite-FPN for Keypoint-based Monocular 3D Object Detection

https://github.com/yanglei18/mix-teaching

공식 코드

Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection

https://github.com/yanglei18/sgv3d

공식 코드

SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection

https://github.com/yangli18/pdnet

공식 코드

PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling

https://github.com/yangli18/vltvg

공식 코드

Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning

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