Papers Few-Shot Semantic Segmentation
“Few-Shot Semantic Segmentation” 태그가 달린 논문 180편 · 필터 해제
Few-Shot Semantic Segmentation Meets SAM3
Few-Shot Semantic Segmentation (FSS) focuses on segmenting novel object categories from only a handful of annotated examples. Most existing approaches rely on extensive episodic training to learn transferable representat…
Few-Shot Semantic SegmentationRevealing the Semantic Selection Gap in DINOv3 through Training-Free Few-Shot Segmentation
Recent self-supervised Vision Transformers (ViTs), such as DINOv3, provide rich feature representations for dense vision tasks. This study investigates the intrinsic few-shot semantic segmentation (FSS) capabilities of f…
Few-Shot Semantic SegmentationTest-time AdaptationAI-Based Culvert-Sewer Inspection
Culverts and sewer pipes are critical components of drainage systems, and their failure can lead to serious risks to public safety and the environment. In this thesis, we explore methods to improve automated defect segme…
Few-Shot Semantic SegmentationFew-Shot LearningData AugmentationSAM-Aug: Leveraging SAM Priors for Few-Shot Parcel Segmentation in Satellite Time Series
Few-shot semantic segmentation of time-series remote sensing images remains a critical challenge, particularly in regions where labeled data is scarce or costly to obtain. While state-of-the-art models perform well under…
Few-Shot Semantic SegmentationTemporal SequencesTake a Peek: Efficient Encoder Adaptation for Few-Shot Semantic Segmentation via LoRA
Few-shot semantic segmentation (FSS) aims to segment novel classes in query images using only a small annotated support set. While prior research has mainly focused on improving decoders, the encoder's limited ability to…
Few-Shot Semantic SegmentationComputational EfficiencyMatching-Based Few-Shot Semantic Segmentation Models Are Interpretable by Design
Few-Shot Semantic Segmentation (FSS) models achieve strong performance in segmenting novel classes with minimal labeled examples, yet their decision-making processes remain largely opaque. While explainable AI has advanc…
Few-Shot Semantic SegmentationMulti-Text Guided Few-Shot Semantic Segmentation
Recent CLIP-based few-shot semantic segmentation methods introduce class-level textual priors to assist segmentation by typically using a single prompt (e.g., a photo of class). However, these approaches often result in …
Few-Shot Semantic SegmentationAttention-Enhanced Prototypical Learning for Few-Shot Infrastructure Defect Segmentation
Few-shot semantic segmentation is vital for deep learning-based infrastructure inspection applications, where labeled training examples are scarce and expensive. Although existing deep learning frameworks perform well, t…
Few-Shot Semantic SegmentationFew to Big: Prototype Expansion Network via Diffusion Learner for Point Cloud Few-shot Semantic Segmentation
Few-shot 3D point cloud semantic segmentation aims to segment novel categories using a minimal number of annotated support samples. However, prototypes derived from the limited non-structural point cloud support set are …
Few-Shot Semantic SegmentationFS-SAM2: Adapting Segment Anything Model 2 for Few-Shot Semantic Segmentation via Low-Rank Adaptation
Few-shot semantic segmentation has recently attracted great attention. The goal is to develop a model capable of segmenting unseen classes using only a few annotated samples. Most existing approaches adapt a pre-trained …
Few-Shot Semantic SegmentationComputational EfficiencyVideo SegmentationObject-level Correlation for Few-Shot Segmentation
Few-shot semantic segmentation (FSS) aims to segment objects of novel categories in the query images given only a few annotated support samples. Existing methods primarily build the image-level correlation between the su…
Few-Shot Semantic SegmentationThrough the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation
Meta-learning aims to uniformly sample homogeneous support-query pairs, characterized by the same categories and similar attributes, and extract useful inductive biases through identical network architectures. However, t…
Few-Shot Semantic SegmentationAdapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation
Cross-domain few-shot segmentation (CD-FSS) is proposed to pre-train the model on a source-domain dataset with sufficient samples, and then transfer the model to target-domain datasets where only a few samples are availa…
Cross-Domain Few-ShotFew-Shot Semantic SegmentationSemantic SegmentationDINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining
Few-shot semantic segmentation has gained increasing interest due to its generalization capability, i.e., segmenting pixels of novel classes requiring only a few annotated images. Prior work has focused on meta-learning …
Few-Shot Semantic SegmentationMeta-LearningSemantic SegmentationFSSUWNet: Mitigating the Fragility of Pre-trained Models with Feature Enhancement for Few-Shot Semantic Segmentation in Underwater Images
Few-Shot Semantic Segmentation (FSS), which focuses on segmenting new classes in images using only a limited number of annotated examples, has recently progressed in data-scarce domains. However, in this work, we show th…
Few-Shot Semantic SegmentationSemantic SegmentationExploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models
Industrial defect segmentation is critical for manufacturing quality control. Due to the scarcity of training defect samples, few-shot semantic segmentation (FSS) holds significant value in this field. However, existing …
Few-Shot Semantic SegmentationManufacturing Quality ControlMeta-LearningMetric Learning+2AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies
Automated interpretation of seismic images using deep learning methods is challenging because of the limited availability of training data. Few-shot learning is a suitable learning paradigm in such scenarios due to its a…
Few-Shot LearningFew-Shot Semantic SegmentationSemantic SegmentationOvercoming Support Dilution for Robust Few-shot Semantic Segmentation
Few-shot Semantic Segmentation (FSS) is a challenging task that utilizes limited support images to segment associated unseen objects in query images. However, recent FSS methods are observed to perform worse, when enlarg…
Few-Shot Semantic SegmentationSegmentationSemantic SegmentationFew-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks
This paper proposes a novel approach to few-shot semantic segmentation for machinery with multiple parts that exhibit spatial and hierarchical relationships. Our method integrates the foundation models CLIPSeg and Segmen…
Few-Shot Semantic SegmentationSegmentationSemantic SegmentationVideo Segmentation+1DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation
Few-shot semantic segmentation (FSS) aims to enable models to segment novel/unseen object classes using only a limited number of labeled examples. However, current FSS methods frequently struggle with generalization …
Few-Shot Semantic SegmentationSegmentationSemantic Segmentation