Shot Segmentation

Shot segmentation, particularly few-shot segmentation (FSS), focuses on segmenting images with limited labeled data, aiming to improve model generalization to unseen classes. Current research emphasizes enhancing feature extraction and matching mechanisms, often employing transformer-based architectures and leveraging knowledge from foundation models like DINOv2 and SAM, along with techniques such as prototype learning and prompt engineering. This field is crucial for applications where annotated data is scarce, such as medical imaging, remote sensing, and industrial inspection, enabling more efficient and effective object recognition and analysis in these domains.

Papers

May 30, 2024

Learning Robust Correlation with Foundation Model for Weakly-Supervised Few-Shot Segmentation
Xinyang Huang, Chuang Zhu, Kebin Liu, Ruiying Ren, Shengjie Liu
Foundation Model Segmentation Based Approach Shot Segmentation Pre Trained ViT Correlation Network Correlation Transformer

May 22, 2024

Embedding Generalized Semantic Knowledge into Few-Shot Remote Sensing Segmentation
Yuyu Jia, Wei Huang, Junyu Gao, Qi Wang, Qiang Li
Image Segmentation Remote Sensing Shot Segmentation Class Embeddings Image Speak Generalized Knowledge

May 14, 2024

April 16, 2024

March 25, 2024

SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging
Lingdong Shen, Fangxin Shang, Xiaoshuang Huang, Yehui Yang, Haifeng Huang, Shiming Xiang
Medical Image Segmentation Medical Imaging Segmentation Task Segmentation Performance Shot Segmentation Multimodal in Context Learning Enhanced Segmentation

March 22, 2024

IFSENet : Harnessing Sparse Iterations for Interactive Few-shot Segmentation Excellence
Shreyas Chandgothia, Ardhendu Sekhar, Amit Sethi
Shot Segmentation Interactive Segmentation

March 17, 2024

LERENet: Eliminating Intra-class Differences for Metal Surface Defect Few-shot Semantic Segmentation
Hanze Ding, Zhangkai Wu, Jiyan Zhang, Ming Ping, Yanfang Liu
Shot Segmentation Local Feature Shot Semantic Segmentation Industrial Defect Intra Class Variation Defect Datasets Steel Surface Defect

March 11, 2024

Toward Robust Canine Cardiac Diagnosis: Deep Prototype Alignment Network-Based Few-Shot Segmentation in Veterinary Medicine
Jun-Young Oh, In-Gyu Lee, Tae-Eui Kam, Ji-Hoon Jeong
Segmentation Based Approach Image Segmentation Artificial Intelligence Research Shot Segmentation Accurate Segmentation Medical Artificial Intelligence Prototype Alignment Veterinary Medicine

March 5, 2024

DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation
Lev Ayzenberg, Raja Giryes, Hayit Greenspan
Deep Learning Model Computer Vision Self Supervised Medical Image Segmentation Medical Image Analysis Shot Segmentation DINOv2 Model

February 27, 2024

Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation
Jonas Herzog
Segmentation Based Approach Consistent Comparison Segmentation Network Shot Segmentation New Perspective Training Domain Cross Domain Few Shot

January 18, 2024

Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention
Li Guo, Haoming Liu, Yuxuan Xia, Chengyu Zhang, Xiaochen Lu
Segmentation Based Approach Segmentation Performance Shot Segmentation Instance Augmentation

December 26, 2023

Task-Disruptive Background Suppression for Few-Shot Segmentation
Suho Park, SuBeen Lee, Sangeek Hyun, Hyun Seok Seong, Jae-Pil Heo
Segmentation Based Approach Shot Segmentation Background Generation Background Suppression

December 25, 2023

Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement
Jing Wang, Jinagyun Li, Chen Chen, Yisi Zhang, Haoran Shen, Tianxiang Zhang
Segmentation Task Long Form Novel Shot Segmentation Level Representation Segmentation Framework Annotated Image Prototype Quality

December 8, 2023

DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation
Haoran Fan, Qi Fan, Maurice Pagnucco, Yang Song
Segmentation Based Approach Shot Segmentation Cross Domain Few Shot Dynamic Adaptation Label Rich Source

December 6, 2023

Background Clustering Pre-training for Few-shot Segmentation
Zhimiao Yu, Tiancheng Lin, Yi Xu
Shot Learning Shot Segmentation Online Clustering Pixel Embeddings Moving Camera Background Model

November 29, 2023

Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation
Yuan Wang, Naisong Luo, Tianzhu Zhang
Segmentation Based Approach Query Information Shot Segmentation Human Driving Focus Transformer Based Generative Adversarial Query Image Segmentation Mining Transformer

November 28, 2023

LLaFS: When Large Language Models Meet Few-Shot Segmentation
Lanyun Zhu, Tianrun Chen, Deyi Ji, Jieping Ye, Jun Liu
Large Language Model Segmentation Based Approach Shot Segmentation Segmentation Result Shot Recognition

November 20, 2023

Few-shot Multispectral Segmentation with Representations Generated by Reinforcement Learning
Dilith Jayakody, Thanuja Ambegoda
Reinforcement Learning Meaningful Representation Shot Segmentation Multispectral Image Multispectral Datasets Hyperspectral Image Segmentation Shot Hyperspectral Image Classification

Shot Segmentation

Papers

Learning Robust Correlation with Foundation Model for Weakly-Supervised Few-Shot Segmentation

Embedding Generalized Semantic Knowledge into Few-Shot Remote Sensing Segmentation

Image to Pseudo-Episode: Boosting Few-Shot Segmentation by Unlabeled Data

Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation

Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation

Learnable Prompt for Few-Shot Semantic Segmentation in Remote Sensing Domain

SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging

IFSENet : Harnessing Sparse Iterations for Interactive Few-shot Segmentation Excellence

LERENet: Eliminating Intra-class Differences for Metal Surface Defect Few-shot Semantic Segmentation

Toward Robust Canine Cardiac Diagnosis: Deep Prototype Alignment Network-Based Few-Shot Segmentation in Veterinary Medicine

DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation

Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation

Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention

Task-Disruptive Background Suppression for Few-Shot Segmentation

Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement

DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation

Background Clustering Pre-training for Few-shot Segmentation

Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation

LLaFS: When Large Language Models Meet Few-Shot Segmentation

Few-shot Multispectral Segmentation with Representations Generated by Reinforcement Learning