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/felixcaae/aligndetr

공식 코드

Align-DETR: Enhancing End-to-end Object Detection with Aligned Loss

https://github.com/felixcaae/crowdsam

공식 코드

Crowd-SAM: SAM as a Smart Annotator for Object Detection in Crowded Scenes

https://github.com/fengfu-chris/caffe-twns

공식 코드

Ternary Weight Networks

https://github.com/fengli-ust/dn-detr

공식 코드

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

https://github.com/fengzicai/s2s

공식 코드

Shape2Scene: 3D Scene Representation Learning Through Pre-training on Shape Data

https://github.com/fengziyue/FusionDepth

공식 코드

Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR

https://github.com/feobi1999/tdd

공식 코드

Cross Domain Object Detection by Target-Perceived Dual Branch Distillation

https://github.com/ferry-li/si-sod

공식 코드

Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection

https://github.com/fesvhtr/DocMSU

공식 코드

DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm Understanding

https://github.com/filapro/unidet3d

공식 코드

UniDet3D: Multi-dataset Indoor 3D Object Detection

https://github.com/finger-monkey/Data-Augmentation

공식 코드

Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

https://github.com/fishandwasabi/yolo-ms

공식 코드

YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection

https://github.com/fishsoup0/autonomous-driving-perception

공식 코드

A Comprehensive Review of 3D Object Detection in Autonomous Driving: Technological Advances and Future Directions

https://github.com/fistyee/mixpro

공식 코드

MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer

https://github.com/fiveai/MoCaE

공식 코드

MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection

https://github.com/fiveai/detection_calibration

공식 코드

On Calibration of Object Detectors: Pitfalls, Evaluation and Baselines

https://github.com/fiveai/hardest

공식 코드

Query-based Hard-Image Retrieval for Object Detection at Test Time

https://github.com/fiveai/saod

공식 코드

Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration

https://github.com/fkthi/OpenTrafficMonitoringPlus

공식 코드

Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles

https://github.com/flkraus/bayesian-yolov3

공식 코드

Uncertainty Estimation in One-Stage Object Detection

https://github.com/floatingstarZ/GeRSP

공식 코드

Generic Knowledge Boosted Pre-training For Remote Sensing Images

https://github.com/floatingstarz/mutdet

공식 코드

MutDet: Mutually Optimizing Pre-training for Remote Sensing Object Detection

https://github.com/flyakon/Geographical-Knowledge-driven-Representaion-Learning

공식 코드

Geographical Knowledge-driven Representation Learning for Remote Sensing Images

https://github.com/fnozarian/ReliableStudent

공식 코드

Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection

https://github.com/fod-unomaha/fodanomalydata

공식 코드

Foreign Object Debris Detection for Airport Pavement Images based on Self-supervised Localization and Vision Transformer

https://github.com/forAcadamic/T-CAD

공식 코드

TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance

https://github.com/forawardstar/pedger

공식 코드

Practical Edge Detection via Robust Collaborative Learning

https://github.com/fordevoted/uiess

공식 코드

Domain Adaptation for Underwater Image Enhancement via Content and Style Separation

https://github.com/forresti/caffe

공식 코드

DenseNet: Implementing Efficient ConvNet Descriptor Pyramids

https://github.com/foundationvision/generateu

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Generative Region-Language Pretraining for Open-Ended Object Detection

https://github.com/fpv-iplab/DA-RetinaNet

공식 코드

An Unsupervised Domain Adaptation Scheme for Single-Stage Artwork Recognition in Cultural Sites

https://github.com/fpv-iplab/MECCANO

공식 코드

The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain

https://github.com/fpv-iplab/STMDA-RetinaNet

공식 코드

A Multi Camera Unsupervised Domain Adaptation Pipeline for Object Detection in Cultural Sites through Adversarial Learning and Self-Training

https://github.com/francescocappio/oshot-meta-learning

공식 코드

Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection

https://github.com/franchesoni/iadet

공식 코드

IAdet: Simplest human-in-the-loop object detection

https://github.com/franciszzj/openpsg

공식 코드

OpenPSG: Open-set Panoptic Scene Graph Generation via Large Multimodal Models

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