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2D Classification

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

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/michaelwilliamtang/golden-retriever

공식 코드

Renderers are Good Zero-Shot Representation Learners: Exploring Diffusion Latents for Metric Learning

https://github.com/michelecafagna26/CHANGE-IT

공식 코드

On the interaction of automatic evaluation and task framing in headline style transfer

https://github.com/michelemancusi/LQVAE-separation

공식 코드

Unsupervised Source Separation via Bayesian Inference in the Latent Domain

https://github.com/michiyasunaga/bifi

공식 코드

Break-It-Fix-It: Unsupervised Learning for Program Repair

https://github.com/mickeykang16/ev3dod

공식 코드

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

https://github.com/mickeymst/f3

공식 코드

Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation

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/ConfigNet

공식 코드

CONFIG: Controllable Neural Face Image Generation

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/HMNet

공식 코드

A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining

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

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Hashing-based Non-Maximum Suppression for Crowded Object Detection

https://github.com/microsoft/multiview-human-pose-estimation-pytorch

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Cross View Fusion for 3D Human Pose Estimation

https://github.com/microsoft/regionclip

공식 코드

RegionCLIP: Region-based Language-Image Pretraining

https://github.com/microsoft/renderformer

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RenderFormer: Transformer-based Neural Rendering of Triangle Meshes with Global Illumination

https://github.com/microsoft/scene_graph_benchmark

공식 코드

Image Scene Graph Generation (SGG) Benchmark

https://github.com/microsoft/speecht5

공식 코드

SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing

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

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