Collect some papers about few shot semantic segmentation.
Some papers for 2017-2021 refer to Few-Shot-Semantic-Segmentation-Papers.
Title | Venue | Dataset | CODE | |
---|---|---|---|---|
MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation | WACV | PASCAL & MS COCO | CODE | |
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation | WACV | PASCAL & MS COCO | - |
Title | Venue | CODE | |
---|---|---|---|
Part-Based Semantic Transform for Few-Shot Semantic Segmentation | TNNLS | CODE | |
Scale-Aware Detailed Matching for Few-Shot Aerial Image Semantic Segmentation | TGRS | CODE | |
Rich Embedding Features for One-Shot Semantic Segmentation | TNNLS | - | |
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer | ICCV | CODE | |
Hypercorrelation Squeeze for Few-Shot Segmenation | ICCV | CODE | |
Mining Latent Classes for Few-shot Segmentation | ICCV | CODE | |
Few-Shot Semantic Segmentation with Cyclic Memory Network | ICCV | - | |
Learning Meta-class Memory for Few-Shot Semantic Segmentation | ICCV | CODE | |
Progressive One-Shot Human Parsing | AAAI | CODE | |
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation | AAAI | CODE | |
Scale-Aware Graph Neural Network for Few-Shot Semantic Segmentation | CVPR | - | |
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation | CVPR | - | |
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation | CVPR | CODE | |
Self-Guided and Cross-Guided Learning for Few-Shot Segmentation | CVPR | CODE | |
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need? | CVPR | CODE | |
On the Texture Bias for Few-Shot CNN Segmentation | WACV | CODE | |
Harmonic Feature Activation for Few-shot Semantic Segmentation | TIP | CODE | |
A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation | ISBI | - | |
Dense Gaussian Processes for Few-Shot Segmentation | arXiv | - | |
End-to-end One-shot Human Parsing | arXiv | - | |
Few-Shot Segmentation with Global and Local Contrastive Learning | arXiv | - | |
Few-shot Segmentation with Optimal Transport Matching and Message Flow | arXiv | - | |
Uncertainty-Aware Semi-Supervised Few Shot Segmentation | arXiv | - | |
Cost Aggregation Is All You Need for Few-Shot Segmentation | arXiv | CODE |
Title | Venue | CODE | |
---|---|---|---|
Dynamic Extension Nets for Few-shot Semantic Segmentation | MM | CODE | |
Semi-supervised few-shot learning for medical image segmentation | arXiv | CODE | |
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need? | arXiv | CODE | |
Meta-Learning Initializations for Image Segmentation | NeurIPS-W | CODE | |
BriNet: Towards Bridging the Intra-class and Inter-class Gaps in One-Shot Segmentation | arXiv | CODE | |
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation | ECCV | CODE | |
Generalized Few-Shot Semantic Segmentation | arXiv | - | |
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation | CVPR | CODE | |
Few-Shot Semantic Segmentation with Democratic Attention Networks | ECCV | - | |
Prototype Mixture Models for Few-shot Semantic Segmentation | ECCV | CODE | |
PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation | TPAMI | CODE | |
Part-aware Prototype Network for Few-shot Semantic Segmentation | ECCV | CODE | |
SimPropNet: Improved Similarity Propagation for Few-shot Image Segmentation | IJCAI | - | |
Objectness-Aware One-Shot Semantic Segmentation | arXiv | - | |
Self-Supervised Tuning for Few-Shot Segmentation | arXiv | - | |
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation | TCYB | CODE | |
CAFENet: Class-Agnostic Few-Shot Edge Detection Network | arXiv | - | |
FGN: Fully Guided Network for Few-Shot Instance Segmentation | CVPR | - | |
CRNet: Cross-Reference Networks for Few-Shot Segmentation | CVPR | - | |
Differentiable Meta-learning Model for Few-shot Semantic Segmentation | AAAI | - | |
Prototype Refinement Network for Few-Shot Segmentation | arXiv | - | |
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings | IJCAI | - |
Title | Venue | CODE | |
---|---|---|---|
A deep one-shot network for query-based logo retrieval | PR | - | |
PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment | ICCV | CODE | |
Pyramid Graph Networks with Connection Attentions for Region-Based One-Shot Semantic Segmentation | ICCV | - | |
AMP: Adaptive Masked Proxies for Few-Shot Segmentation | ICCV | CODE | |
Feature Weighting and Boosting for Few-Shot Segmentation | ICCV | - | |
CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning | CVPR | CODE | |
Adaptive Masked Weight Imprinting for Few-Shot Segmentation | ICLRW | - | |
A New Local Transformation Module for Few-Shot Segmentation | MMMM | ||
A New Few-shot Segmentation Network Based on Class Representation | arXiv |
Title | Venue | CODE | |
---|---|---|---|
Conditional networks for few-shot semantic segmentation | ICLRW | CODE | |
Few-Shot Semantic Segmentation with Prototype Learning | BMVC | - |
Title | Venue | CODE | |
---|---|---|---|
One-Shot Learning for Semantic Segmentation | BMVC | CODE |