Showing 1-25 of 420 resultsfor "Index Terms":Few-shot Semantic Segmentation
"Index Terms":Few-shot Semantic Segmentation
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FBINet: Few-shot Semantic Segmentation with Foreground and Background Iteration
IEEE Transactions on Instrumentation and Measurement
Year: 2025 | Early Access Article |
Defect detection methods based on few-shot segmentation are becoming more and more popular in industrial applications, and few-shot segmentation methods need to use only a limited number of densely labeled samples to segment objects of unseen classes. However, most existing few-shot semantic segmentation (FSS) approaches primarily focus on either foreground knowledge or background knowledge of an ...Show More
EPFNet: Edge-Prototype Fusion Network Toward Few-Shot Semantic Segmentation for Aerial Remote-Sensing Images
Few-shot semantic segmentation is a technique that is receiving increasing attention. The aim of this approach is to enable models to segment objects with a few support images (usually 1, 5, 10, etc.). At present, few-shot semantic segmentation has made great progress in the field of natural scene images (NSIs), but these methods cannot be applied directly to the field of remote-sensing images (RS...Show More
Adaptive Self-Supporting Prototype Learning for Remote Sensing Few-Shot Semantic Segmentation
The semantic segmentation of remote sensing images with few shots has important theoretical and application value. Most of the existing few-shot semantic segmentation frameworks are based on prototype learning methods, in which a single support prototype is designed to guide the query set for prediction. However, the visual differences between the support set and the query set make it difficult fo...Show More
Self-Supporting Adaptive Prototype Learning for Remote Sensing Few-Shot Semantic Segmentation
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
The segmentation of remote sensing images with few shots is valuable both in theory and application. Many existing few-shot segmentation methods rely on prototype learning, where a single support prototype guides predictions for the query set. However, due to visual differences between the support and query sets, a single support prototype struggles to capture all semantic information effectively....Show More
Prototypical Metric Segment Anything Model for Data-Free Few-Shot Semantic Segmentation
Few-shot semantic segmentation (FSS) is crucial for image interpretation, yet it is constrained by requirements for extensive base data and a narrow focus on foreground-background differentiation. This work introduces Data-free Few-shot Semantic Segmentation (DFSS), a task that requires limited labeled images and forgoes the need for extensive base data, allowing for comprehensive image segmentati...Show More
Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark
Clifford Broni-Bediako;Junshi Xia;Jian Song;Hongruixuan Chen;Mennatullah Siam;Naoto Yokoya
Learning with limited labeled data is a challenging problem in various applications, including remote sensing. Few-shot semantic segmentation is one approach that can encourage deep learning models to learn from few labeled examples for novel classes not seen during the training. The generalized few-shot segmentation setting has an additional challenge which encourages models not only to adapt to ...Show More
Spatial Correlation Fusion Network for Few-Shot Segmentation
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Few-shot semantic segmentation aims to learn new knowledge rapidly with very few annotated data to segment novel classes. Recent methods follow a metric learning framework with prototypes for foreground representation [1]. However, representing support images by one or more prototypes may face problems caused by inadequate representation for segmentation, noise in complex scenes, and close semanti...Show More
Few-Shot Segmentation based on Global-cross Attention
2023 35th Chinese Control and Decision Conference (CCDC)
Year: 2023 | Conference Paper |
As one of the major problems in the field of computer vision, few-shot segmentation has been always in the spotlight. The goal of few-shot segmentation is to employ a few samples to train a model so that it can segment the same kind of query images. However, features of images are invariably the crux to achieve semantic representations. For the sake of extracting more features and further improvin...Show More
Few-shot semantic segmentation aims to extract information from few annotated support images to segment unknown class objects in the query image. Traditional algorithms may produce errors and insufficient feature extraction using multi-layer cosine similarity to extract correlation information, due to the large differences in appearance and posture between novel class objects, as well as the simil...Show More
Boosting Few-Shot Semantic Segmentation With Prior-Driven Edge Feature Enhancement Network
Few-shot semantic segmentation (FSS) focuses on segmenting objects of novel classes with only a small number of annotated samples and has achieved great development. However, compared with general semantic segmentation, inaccurate boundary predictions remain a serious problem in FSS. This is because, in scenarios with few samples, the extracted query features by the model struggle to contain suffi...Show More
The convolutional neural network-based semantic segmentation is emerging few-shot learning, which is quite popular in the field of image processing. Semantic segmentation can make good use of a small number of datasets to achieve image segmentation effectively. This paper takes image segmentation as an entry point to study few-shot learning algorithms, focusing on the deep convolutional network-ba...Show More
Text-Guided Few-Shot Semantic Segmentation with Training-Free Multimodal Feature Matching
Guillaume Buthmann;Tomoya Sakai;Haoxiang Qiu;Takayuki Katsuki;Daiki Kimura
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
This paper addresses few-shot semantic segmentation (FSS) guided by text, where we classify unseen novel classes using image and text references as in-context examples, without the need for training. We enhance the quality and stability of the segmentation masks generated by FSS by combining the capability of open-vocabulary zero-shot semantic segmentation (ZSS) based on foundation models for imag...Show More
Memory-Based Contrastive Learning with Optimized Sampling for Incremental Few-Shot Semantic Segmentation
2024 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2024 | Conference Paper |
Incremental few-shot semantic segmentation (IFSS) aims to incrementally expand a semantic segmentation model’s ability to identify new classes based on few samples. However, it grapples with the dual challenges of catastrophic forgetting (due to feature drift in old classes) and overfitting (triggered by inadequate samples in new classes). To address these issues, a novel approach is proposed to i...Show More
Language-Guided Few-Shot Semantic Segmentation
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Few-shot learning is a promising way for reducing the label cost in new categories adaptation with the guidance of a small, well labeled support set. But for few-shot semantic segmentation, the pixel-level annotations of support images are still expensive. In this paper, we propose an innovative solution to tackle the challenge of few-shot semantic segmentation using only language information, i.e...Show More
Not Just Learning From Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing
Hanbo Bi;Yingchao Feng;Zhiyuan Yan;Yongqiang Mao;Wenhui Diao;Hongqi Wang;Xian Sun
Few-shot segmentation (FSS) is proposed to segment unknown class targets with just a few annotated samples. Most current FSS methods follow the paradigm of mining the semantics from the support images to guide the query image segmentation. However, such a pattern of “learning from others” struggles to handle the extreme intraclass variation, preventing FSS from being directly generalized to remote...Show More
Employing Offset-Attention for 3D Few-shot Semantic Segmentation
2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
The existing deep 3D semantic segmentation methods mostly are trained with a large number of human annotations. However, due to the expensive labor for annotations label, few-shot 3D semantic segmentation is achieving more attention. In this work, we improve the performance of few-shot learning based on semantic segmentation of 3D point clouds using the offset attention method that has been succes...Show More
Self-Support Prototype-Aware For Few-Shot Semantic Segmentation
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
In recent years, significant progress has been made in prototype-based learning methods for few-shot semantic segmentation. However, prototype features originating from the support images are interfered with by intra-class diversity and thus cannot be aligned with the query foreground, resulting in poor segmentation accuracy. Therefore, we propose a novel self-support prototype-aware (SSPA) networ...Show More
Generalized Few-Shot 3D Point Cloud Segmentation
2024 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2024 | Conference Paper |
Few-Shot 3D Point Cloud Semantic Segmentation (3D-FS) mitigates the issues of insufficient data annotation and emerging new classes in real-world scenarios, but it totally ignores the performance on base classes. In this paper, we address a more practical task named Generalized Few-Shot 3D Point Cloud Semantic Segmentation (3D-GFS), which aims to perform segmentation on both the base classes with ...Show More
Multi-scale Discriminative Location-Aware Network for Few-Shot Semantic Segmentation
2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)
Year: 2019 | Conference Paper |
Cited by: Papers (11)
Few-shot semantic segmentation methods aim for predicting the regions of different object categories with only a few labeled samples. It is difficult to produce segmentation results with high accuracy when a new category appears. In this paper, we propose a Multi-scale Discriminative Location-aware (MDL) network to tackle the few-shot semantic segmentation problem. In order to use information from...Show More
FECANet: Boosting Few-Shot Semantic Segmentation With Feature-Enhanced Context-Aware Network
Huafeng Liu;Pai Peng;Tao Chen;Qiong Wang;Yazhou Yao;Xian-Sheng Hua
Few-shot semantic segmentation is the task of learning to locate each pixel of the novel class in the query image with only a few annotated support images. The current correlation-based methods construct pair-wise feature correlations to establish the many-to-many matching because the typical prototype-based approaches cannot learn fine-grained correspondence relations. However, the existing metho...Show More
Base and Meta: A New Perspective on Few-Shot Segmentation
Despite the progress made by few-shot segmentation (FSS) in low-data regimes, the generalization capability of most previous works could be fragile when countering hard query samples with seen-class objects. This paper proposes a fresh and powerful scheme to tackle such an intractable bias problem, dubbed base and meta (BAM). Concretely, we apply an auxiliary branch (base learner) to the conventio...Show More
Few-Shot Segmentation via Channel Attention and Supervision Augmentation
2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP)
Year: 2023 | Conference Paper |
The effect of the semantic segmentation methods has been significantly improved by introducing deep convolutional neural networks (CNNs) in recent years. But, these approaches need quantities of data with pixel-level labels and have poor generalizability. Few-shot segmentation (FSS) is subsequently proposed, which can complete the segmentation of unseen classes with only a small amount of annotate...Show More
Prototype Adaption and Projection for Few- and Zero-Shot 3D Point Cloud Semantic Segmentation
In this work, we address the challenging task of few-shot and zero-shot 3D point cloud semantic segmentation. The success of few-shot semantic segmentation in 2D computer vision is mainly driven by the pre-training on large-scale datasets like imagenet. The feature extractor pre-trained on large-scale 2D datasets greatly helps the 2D few-shot learning. However, the development of 3D deep learning ...Show More
A Review on Few-shot Learning for Medical Image Segmentation
Yeongjoon Kim;Donggoo Kang;Yeongheon Mok;Sunkyu Kwon;Joonki Paik
2023 International Conference on Electronics, Information, and Communication (ICEIC)
Year: 2023 | Conference Paper |
Cited by: Papers (6)
Deep-learning based approach has solved various medical imaging problems successfully. Since the lack of training data issues caused by patient privacy, the few-shot learning method has been studied widely. However, this issue still afflicts model performance even in few-shot learning methods. To solve this issue, it is important to quickly optimize the initial parameter values using a small amoun...Show More
Fully Automated Scan-to-BIM Via Point Cloud Instance Segmentation
D. Campagnolo;E. Camuffo;U. Michieli;P. Borin;S. Milani;A. Giordano
2023 IEEE International Conference on Image Processing (ICIP)
Year: 2023 | Conference Paper |
Digital reconstruction through Building Information Models (BIM) is a valuable methodology for documenting and analyzing existing buildings. Its pipeline starts with geometric acquisition. (e.g., via photogrammetry or laser scanning) for accurate point cloud collection. However, the acquired data are noisy and unstructured, and the creation of a semantically-meaningful BIM representation requires ...Show More
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