Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

39

평가표

124

보존된 평가 기록

977

코드

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

https://github.com/metu-kovan/METU-ALET

공식 코드

ALET (Automated Labeling of Equipment and Tools): A Dataset, a Baseline and a Usecase for Tool Detection in the Wild

https://github.com/mfigurnov/sact

공식 코드

Spatially Adaptive Computation Time for Residual Networks

https://github.com/mgholamikn/ETran

공식 코드

ETran: Energy-Based Transferability Estimation

https://github.com/mi3labucm/coooler

공식 코드

Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving Safety

https://github.com/michaelthoreau/SearchAndRescueNet

공식 코드

SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite Imagery

https://github.com/mickeykang16/ev3dod

공식 코드

Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras

https://github.com/microndla/mobilevitv3

공식 코드

MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features

https://github.com/microsoft/DynamicHead

공식 코드

Dynamic Head: Unifying Object Detection Heads with Attentions

https://github.com/microsoft/Focal-Transformer

공식 코드

Focal Self-attention for Local-Global Interactions in Vision Transformers

https://github.com/microsoft/FocalNet

공식 코드

Focal Modulation Networks

https://github.com/microsoft/GLIP

공식 코드

Grounded Language-Image Pre-training

https://github.com/microsoft/RelationNet2

공식 코드

RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder

https://github.com/microsoft/RepPoints

공식 코드

RepPoints: Point Set Representation for Object Detection

https://github.com/microsoft/SNR

공식 코드

Style Normalization and Restitution for Domain Generalization and Adaptation

https://github.com/microsoft/SPACH

공식 코드

When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism

https://github.com/microsoft/SoftTeacher

공식 코드

End-to-End Semi-Supervised Object Detection with Soft Teacher

https://github.com/microsoft/Swin-Transformer

공식 코드

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/microsoft/YOLaT-VectorGraphicsRecognition

공식 코드

Recognizing Vector Graphics without Rasterization

https://github.com/microsoft/activemlp

공식 코드

Active Token Mixer

https://github.com/microsoft/comphrdoc

공식 코드

Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure Analysis

https://github.com/microsoft/cream

공식 코드

Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search

https://github.com/microsoft/esvit

공식 코드

A Survey on Self-Supervised Representation Learning

https://github.com/microsoft/fiber

공식 코드

Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone

https://github.com/microsoft/hnms

공식 코드

Hashing-based Non-Maximum Suppression for Crowded Object Detection

https://github.com/microsoft/regionclip

공식 코드

RegionCLIP: Region-based Language-Image Pretraining

https://github.com/microsoft/scene_graph_benchmark

공식 코드

Image Scene Graph Generation (SGG) Benchmark

https://github.com/microsoft/swin3d

공식 코드

Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding

https://github.com/microsoft/table-transformer

공식 코드

PubTables-1M: Towards comprehensive table extraction from unstructured documents

https://github.com/microsoft/tutel

공식 코드

Tutel: Adaptive Mixture-of-Experts at Scale

https://github.com/microsoft/unilm/tree/master/beit

공식 코드

Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

https://github.com/microsoft/vision-longformer

공식 코드

Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding

https://github.com/miguelag99/efficient-instance-prediction

공식 코드

Fast and Efficient Transformer-based Method for Bird's Eye View Instance Prediction

https://github.com/mihirp1998/Disentangling-3D-Prototypical-Nets

공식 코드

Disentangling 3D Prototypical Networks For Few-Shot Concept Learning

https://github.com/mikasa3lili/ssd-monodetr

공식 코드

SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection

https://github.com/mikuhatsune/wsod_transfer

공식 코드

Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer

https://github.com/mileyan/Pseudo_Lidar_V2

공식 코드

Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving

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