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/torpedo2648/pclmix

공식 코드

PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation

https://github.com/torrvision/caffe-tvg

공식 코드

Higher Order Conditional Random Fields in Deep Neural Networks

https://github.com/torrvision/crfasrnn

공식 코드

Conditional Random Fields as Recurrent Neural Networks

https://github.com/torrvision/straighttoshapes

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Straight to Shapes++: Real-time Instance Segmentation Made More Accurate

https://github.com/tpnam0901/QTSeg

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QTSeg: A Query Token-Based Architecture for Efficient 2D Medical Image Segmentation

https://github.com/tpopordanoska/calibration_and_bias

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On the relationship between calibrated predictors and unbiased volume estimation

https://github.com/tqwei05/PlantSeg

공식 코드

PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation

https://github.com/tranleanh/edn-gtm

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A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing

https://github.com/tranngocduvnvp/litenext

공식 코드

LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation

https://github.com/travisdriver/deep_monocular_hd

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Deep Monocular Hazard Detection for Safe Small Body Landing

https://github.com/trent-tangtao/autoview

공식 코드

Learning Self-Regularized Adversarial Views for Self-Supervised Vision Transformers

https://github.com/trile83/rqunetvae

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Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising

https://github.com/trustworthy-ai-uu-nki/cross-modal-segmentation

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Enhancing Cross-Modal Medical Image Segmentation through Compositionality

https://github.com/trustworthy-ai-uu-nki/federated-learning-disentanglement

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FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation

https://github.com/tsinghua-fib-lab/uv-sam

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UV-SAM: Adapting Segment Anything Model for Urban Village Identification

https://github.com/tsumansapkota/Input-Invex-Neural-Network

공식 코드

Input Invex Neural Network

https://github.com/tsunghan-wu/d2ada

공식 코드

D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation

https://github.com/tsunghan-wu/redal

공식 코드

ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation

https://github.com/tuat-novice/maprotonet

공식 코드

MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification

https://github.com/tub-rip/mousesis

공식 코드

MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Mice

https://github.com/tudelft3d/SUMS-Semantic-Urban-Mesh-Segmentation-public

공식 코드

SUM: A Benchmark Dataset of Semantic Urban Meshes

https://github.com/tue-mps/algm-segmenter

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ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain Vision Transformers

https://github.com/tue-mps/benchmark-vfm-ss

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How to Benchmark Vision Foundation Models for Semantic Segmentation?

https://github.com/tue-mps/cts-segmenter

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Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers

https://github.com/tue-mps/eomt

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Your ViT is Secretly an Image Segmentation Model

https://github.com/tue-mps/panoptic_parts

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Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding

https://github.com/tue-mps/tapps

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Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations

https://github.com/tue-mps/vfm-uda

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Exploring the Benefits of Vision Foundation Models for Unsupervised Domain Adaptation

https://github.com/tui-nicr/emsaformer

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Efficient Multi-Task Scene Analysis with RGB-D Transformers

https://github.com/tui-nicr/emsanet

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Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments

https://github.com/tui-nicr/nicr-scene-analysis-datasets

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Efficient Multi-Task Scene Analysis with RGB-D Transformers

https://github.com/tui-nicr/panoptic-mapping

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PanopticNDT: Efficient and Robust Panoptic Mapping

https://github.com/tum-vision/hierahyp

공식 코드

Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincaré Ball

https://github.com/tum-vision/mem

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Masked Event Modeling: Self-Supervised Pretraining for Event Cameras

https://github.com/tumftm/radargnn

공식 코드

RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception

https://github.com/tusen-ai/sst

공식 코드

FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels

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