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/idea-research/dino-x-api

공식 코드

DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding

https://github.com/idea-research/dn-detr

공식 코드

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

https://github.com/idea-research/dq-detr

공식 코드

DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding

https://github.com/idea-research/grounding-dino-1.5-api

공식 코드

Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection

https://github.com/idea-research/groundingdino

공식 코드

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/idea-research/lite-detr

공식 코드

Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR

https://github.com/idea-research/maskdino

공식 코드

Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

https://github.com/idea-research/rexseek

공식 코드

Referring to Any Person

https://github.com/idea-research/t-rex

공식 코드

T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy

https://github.com/idejie/KAD

공식 코드

Active Object Detection with Knowledge Aggregation and Distillation from Large Models

https://github.com/idiosyncracies/Sebica

공식 코드

Sebica: Lightweight Spatial and Efficient Bidirectional Channel Attention Super Resolution Network

https://github.com/idrl-lab/Full-coverage-camouflage-adversarial-attack

공식 코드

FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack

https://github.com/idstcv/DR_loss

공식 코드

DR Loss: Improving Object Detection by Distributional Ranking

https://github.com/iduta/coconv

공식 코드

Contextual Convolutional Neural Networks

https://github.com/iduta/iresnet

공식 코드

Improved Residual Networks for Image and Video Recognition

https://github.com/iduta/pyconv

공식 코드

Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition

https://github.com/ies-research/uncertainty-object-detection

공식 코드

A Review of Uncertainty Calibration in Pretrained Object Detectors

https://github.com/ifzhang/FairMOT

공식 코드

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/ignc-research/habitatdyn

공식 코드

HabitatDyn Dataset: Dynamic Object Detection to Kinematics Estimation

https://github.com/igor-morawski/nod

공식 코드

NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset

https://github.com/igor-morawski/raw-nod

공식 코드

GenISP: Neural ISP for Low-Light Machine Cognition

https://github.com/iim-ttij/mva2023smallobjectdetection4spottingbirds

공식 코드

MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results

https://github.com/iit-pavis/look_around_and_learn

공식 코드

Look Around and Learn: Self-Training Object Detection by Exploration

https://github.com/ika-rwth-aachen/multicorrupt

공식 코드

MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection

https://github.com/ikatsamenis/cone-detection

공식 코드

TraCon: A novel dataset for real-time traffic cones detection using deep learning

https://github.com/iliiliiliili/tanet-near-far

공식 코드

Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems

https://github.com/imankgoyal/NonDeepNetworks

공식 코드

Non-deep Networks

https://github.com/imatge-upc/detection-2016-nipsws

공식 코드

Hierarchical Object Detection with Deep Reinforcement Learning

https://github.com/imatge-upc/retrieval-2016-deepvision

공식 코드

Faster R-CNN Features for Instance Search

https://github.com/imatif17/Prototype-Mean-Teacher

공식 코드

Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher

https://github.com/imatif17/acia

공식 코드

Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors

https://github.com/imfinethanks/esbnn

공식 코드

Expanding-and-Shrinking Binary Neural Networks

https://github.com/imnotprepared/airshot

공식 코드

AirShot: Efficient Few-Shot Detection for Autonomous Exploration

https://github.com/imoonlab/hyper-yolo

공식 코드

Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation

https://github.com/imoonlab/yolov13

공식 코드

YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception

https://github.com/implabunipr/unbiased-teacher

공식 코드

Improving Localization for Semi-Supervised Object Detection

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