Papers with Code 아카이브

2D Object Detection

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전체 기법전체 데이터셋

하위 태스크

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평가표

20

보존된 평가 기록

57

코드

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

https://github.com/blisgard/bucketedrankingbasedlosses

공식 코드

Bucketed Ranking-based Losses for Efficient Training of Object Detectors

https://github.com/bloomberry/lada

공식 코드

Semi-Supervised Domain Adaptation for Wildfire Detection

https://github.com/blueblue4/object-detection-confidence-bias

공식 코드

The Box Size Confidence Bias Harms Your Object Detector

https://github.com/blvlab/pimae

공식 코드

PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection

https://github.com/bnlnlp/ppn_model

공식 코드

An extensible point-based method for data chart value detection

https://github.com/bob-cheng/cl-fusionattack

공식 코드

Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detection

https://github.com/bobleegogogo/building_height

공식 코드

Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation

https://github.com/boguss1225/ObjectDetectionGUI

공식 코드

TensorFlow with user friendly Graphical Framework for object detection API

https://github.com/bohao-lee/pdc

공식 코드

Proposal Distribution Calibration for Few-Shot Object Detection

https://github.com/bomps4/multi_resolution_rescored_bytetrack

공식 코드

Multi-resolution Rescored ByteTrack for Video Object Detection on Ultra-low-power Embedded Systems

https://github.com/boschresearch/hyp-ow

공식 코드

Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection

https://github.com/boschresearch/metanas

공식 코드

Meta-Learning of Neural Architectures for Few-Shot Learning

https://github.com/bosszhe/emiff

공식 코드

EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection

https://github.com/bosszhe/vimi

공식 코드

VIMI: Vehicle-Infrastructure Multi-view Intermediate Fusion for Camera-based 3D Object Detection

https://github.com/bowen-upenn/Multi-Agent-VQA

공식 코드

Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering

https://github.com/bowenc0221/Decoupled-Classification-Refinement

공식 코드

Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection

https://github.com/bozcani/COSMO

공식 코드

COSMO: Contextualized Scene Modeling with Boltzmann Machines

https://github.com/bradleybin/locate-globally-segment-locally-a-progressive-architecture-with-knowledge-review-network-for-sod

공식 코드

Learning Video Salient Object Detection Progressively from Unlabeled Videos

https://github.com/braindatalab/xai-tris

공식 코드

XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance

https://github.com/bravegroup/dcd

공식 코드

Densely Constrained Depth Estimator for Monocular 3D Object Detection

https://github.com/bravegroup/fullysparsefusion

공식 코드

Fully Sparse Fusion for 3D Object Detection

https://github.com/bravegroup/pointsam-for-mixsup

공식 코드

MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detection

https://github.com/bravezzzzzz/iou-enhanced-attention

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IoU-Enhanced Attention for End-to-End Task Specific Object Detection

https://github.com/bravezzzzzz/recursivedet

공식 코드

RecursiveDet: End-to-End Region-based Recursive Object Detection

https://github.com/brian-h-wang/pseudolidar-tree-detection

공식 코드

Detecting and Mapping Trees in Unstructured Environments with a Stereo Camera and Pseudo-Lidar

https://github.com/brycexu/BNN_Kernel

공식 코드

A Computing Kernel for Network Binarization on PyTorch

https://github.com/bupt-ai-cz/wuda

공식 코드

WUDA: Unsupervised Domain Adaptation Based on Weak Source Domain Labels

https://github.com/bwittmann/transoar

공식 코드

Focused Decoding Enables 3D Anatomical Detection by Transformers

https://github.com/byangderek/CCF

공식 코드

Convolutional Channel Features

https://github.com/byangderek/CRAFT

공식 코드

CRAFT Objects from Images

https://github.com/bytedance/dq-det

공식 코드

Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation

https://github.com/bytedance/ibot

공식 코드

iBOT: Image BERT Pre-Training with Online Tokenizer

https://github.com/bytedance/kmax-deeplab

공식 코드

kMaX-DeepLab: k-means Mask Transformer

https://github.com/caddyless/ailurus

공식 코드

AiluRus: A Scalable ViT Framework for Dense Prediction

https://github.com/caiguohui123/msdet

공식 코드

MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule

https://github.com/caiqi/cascasde-3d

공식 코드

3D Cascade RCNN: High Quality Object Detection in Point Clouds

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