Papers with Code 아카이브

2D Object Detection

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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/mjseong0414/CRT-Fusion

공식 코드

CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection

https://github.com/mjseong0414/RCM-Fusion

공식 코드

RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detection

https://github.com/mk-minchul/attend-and-compare

공식 코드

Learning Visual Context by Comparison

https://github.com/mkang315/CST-YOLO

공식 코드

CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer

https://github.com/mkang315/bgf-yolo

공식 코드

BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection

https://github.com/mkang315/pk-yolo

공식 코드

PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplanar MRI Slices

https://github.com/mkang315/rcs-yolo

공식 코드

RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection

https://github.com/mkchoi-0323/modified_refinedet

공식 코드

Development of Fast Refinement Detectors on AI Edge Platforms

https://github.com/ml-lab-htw/iterative-annotate

공식 코드

Feedback-driven object detection and iterative model improvement

https://github.com/mlcaepiee/simuav

공식 코드

A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive Fusion

https://github.com/mlpc-ucsd/CoaT

공식 코드

Co-Scale Conv-Attentional Image Transformers

https://github.com/mlvlab/RALF

공식 코드

Retrieval-Augmented Open-Vocabulary Object Detection

https://github.com/mlvlab/cpchoi

공식 코드

Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection

https://github.com/mlvlab/drone_ai_challenge

공식 코드

Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones

https://github.com/mlvlab/drone_task1

공식 코드

Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones

https://github.com/mlvlab/drone_task4

공식 코드

Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones

https://github.com/mlvlab/rony2_task3

공식 코드

Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones

https://github.com/mlzxy/devit

공식 코드

Detect Everything with Few Examples

https://github.com/mmaaz60/EdgeNeXt

공식 코드

EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications

https://github.com/mmaaz60/mvits_for_class_agnostic_od

공식 코드

Class-agnostic Object Detection with Multi-modal Transformer

https://github.com/mmic-lcl/datasets-and-benchmark-code

공식 코드

Tiny Object Tracking: A Large-scale Dataset and A Baseline

https://github.com/mmocking/riders

공식 코드

RIDERS: Radar-Infrared Depth Estimation for Robust Sensing

https://github.com/mmoradi-iut/bodem

공식 코드

Model-agnostic explainable artificial intelligence for object detection in image data

https://github.com/mmunir127/rapidnet-official

공식 코드

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone

https://github.com/moabarar/qna

공식 코드

Learned Queries for Efficient Local Attention

https://github.com/mocherson/ImageGCN

공식 코드

ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays

https://github.com/modeltc/eod

공식 코드

Equalized Focal Loss for Dense Long-Tailed Object Detection

https://github.com/modeltc/mqbench

공식 코드

QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

https://github.com/modeltc/united-perception

공식 코드

The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition

https://github.com/mohamedtev/daca

공식 코드

Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection

https://github.com/mohammedsb/harmony

공식 코드

Harmony: A Joint Self-Supervised and Weakly-Supervised Framework for Learning General Purpose Visual Representations

https://github.com/mohsenzand/objectbox

공식 코드

ObjectBox: From Centers to Boxes for Anchor-Free Object Detection

https://github.com/moinnabi/SelfPacedDeepLearning

공식 코드

Self Paced Deep Learning for Weakly Supervised Object Detection

https://github.com/molden/probkt

공식 코드

Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection

https://github.com/momo1986/robust_sam_iv

공식 코드

Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving

https://github.com/mondalanindya/ICCVW2021_GSCEventMOD

공식 코드

Moving Object Detection for Event-based Vision using k-means Clustering

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