Papers with Code 아카이브

Semantic Segmentation

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전체 기법전체 데이터셋

하위 태스크

29

평가표

150

보존된 평가 기록

1,918

코드

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

https://github.com/etrontech/vfmseg

공식 코드

Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

https://github.com/euphoria16/tl-align

공식 코드

Token-Label Alignment for Vision Transformers

https://github.com/evanmy/sae

공식 코드

An Auto-Encoder Strategy for Adaptive Image Segmentation

https://github.com/eventbasedvision/sevd

공식 코드

SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception

https://github.com/evinpinar/supergb-d

공식 코드

SupeRGB-D: Zero-shot Instance Segmentation in Cluttered Indoor Environments

https://github.com/evocargo/lidar-annotation-is-all-you-need

공식 코드

Lidar Annotation Is All You Need

https://github.com/explainableml/flair

공식 코드

FLAIR: VLM with Fine-grained Language-informed Image Representations

https://github.com/exploration-lab/rhetorical-roles

공식 코드

Semantic Segmentation of Legal Documents via Rhetorical Roles

https://github.com/ezefranca/Master-Exploder

공식 코드

Uma implementação do jogo Pedra, Papel e Tesoura utilizando Visao Computacional

https://github.com/f90/FactorGAN

공식 코드

Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators

https://github.com/fabianbalsiger/point-cloud-segmentation-miccai2019

공식 코드

Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation

https://github.com/fabianfalck/unet-design

공식 코드

A Unified Framework for U-Net Design and Analysis

https://github.com/facebookresearch/ContrastiveSceneContexts

공식 코드

Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts

https://github.com/facebookresearch/ConvNeXt

공식 코드

A ConvNet for the 2020s

https://github.com/facebookresearch/DepthContrast

공식 코드

Self-Supervised Pretraining of 3D Features on any Point-Cloud

https://github.com/facebookresearch/Generic-Grouping

공식 코드

Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity

https://github.com/facebookresearch/GloRe

공식 코드

Graph-Based Global Reasoning Networks

https://github.com/facebookresearch/HRViT

공식 코드

Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation

https://github.com/facebookresearch/Mask2Former

공식 코드

Masked-attention Mask Transformer for Universal Image Segmentation

https://github.com/facebookresearch/MaskFormer

공식 코드

Per-Pixel Classification is Not All You Need for Semantic Segmentation

https://github.com/facebookresearch/Replica-Dataset

공식 코드

The Replica Dataset: A Digital Replica of Indoor Spaces

https://github.com/facebookresearch/SparseConvNet

공식 코드

Submanifold Sparse Convolutional Networks

https://github.com/facebookresearch/convnext-v2

공식 코드

ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

https://github.com/facebookresearch/cutler

공식 코드

Cut and Learn for Unsupervised Object Detection and Instance Segmentation

https://github.com/facebookresearch/deepcluster

공식 코드

Deep Clustering for Unsupervised Learning of Visual Features

https://github.com/facebookresearch/deit

공식 코드

Augmenting Convolutional networks with attention-based aggregation

https://github.com/facebookresearch/detectron2

공식 코드

Panoptic Feature Pyramid Networks

https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend

공식 코드

PointRend: Image Segmentation as Rendering

https://github.com/facebookresearch/detectron2/tree/master/projects/PointSup

공식 코드

Pointly-Supervised Instance Segmentation

https://github.com/facebookresearch/detectron2/tree/master/projects/TensorMask

공식 코드

TensorMask: A Foundation for Dense Object Segmentation

https://github.com/facebookresearch/detr

공식 코드

End-to-End Object Detection with Transformers

https://github.com/facebookresearch/dino

공식 코드

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/facebookresearch/dinov2

공식 코드

DINOv2: Learning Robust Visual Features without Supervision

https://github.com/facebookresearch/guideddistillation

공식 코드

Guided Distillation for Semi-Supervised Instance Segmentation

https://github.com/facebookresearch/imagebind

공식 코드

ImageBind: One Embedding Space To Bind Them All

https://github.com/facebookresearch/long_seq_mae

공식 코드

Exploring Long-Sequence Masked Autoencoders

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