Papers Few-Shot Image Classification
“Few-Shot Image Classification” 태그가 달린 논문 368편 · 필터 해제
InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models
Prompt tuning has become a popular strategy for adapting Vision-Language Models (VLMs) to zero/few-shot visual recognition tasks. Some prompting techniques introduce prior knowledge due to its richness, but when learnabl…
Few-Shot Image Classificationimage-classificationImage ClassificationAugmented Conditioning Is Enough For Effective Training Image Generation
Image generation abilities of text-to-image diffusion models have significantly advanced, yielding highly photo-realistic images from descriptive text and increasing the viability of leveraging synthetic images to train …
Conditional Image GenerationDescriptiveDiversityFew-Shot Image Classification+3Geometric Mean Improves Loss For Few-Shot Learning
Few-shot learning (FSL) is a challenging task in machine learning, demanding a model to render discriminative classification by using only a few labeled samples. In the literature of FSL, deep models are trained in a man…
Few-Shot Image ClassificationFew-Shot Learningimage-classificationImage Classification+1A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs
A fundamental challenge in artificial intelligence involves understanding the cognitive mechanisms underlying visual reasoning in sophisticated models like Vision-Language Models (VLMs). How do these models integrate vis…
DescriptiveDiagnosticFew-Shot Image ClassificationVisual ReasoningIDEA: Image Description Enhanced CLIP-Adapter
CLIP (Contrastive Language-Image Pre-training) has attained great success in pattern recognition and computer vision. Transferring CLIP to downstream tasks (e.g. zero- or few-shot classification) is a hot topic in multim…
Few-Shot Image Classificationimage-classificationImage ClassificationImage Description+1Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification
Few-shot image classification remains a critical challenge in the field of computer vision, particularly in data-scarce environments. Existing methods typically rely on pre-trained visual-language models, such as CLIP. H…
Few-Shot Image ClassificationFew-Shot Learningimage-classificationImage Classification+1Task-Specific Preconditioner for Cross-Domain Few-Shot Learning
Cross-Domain Few-Shot Learning~(CDFSL) methods typically parameterize models with task-agnostic and task-specific parameters. To adapt task-specific parameters, recent approaches have utilized fixed optimization strategi…
Cross-Domain Few-Shotcross-domain few-shot learningFew-Shot Image ClassificationFew-Shot LearningModelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection
The aim of multi-label few-shot image classification (ML-FSIC) is to assign semantic labels to images, in settings where only a small number of training examples are available for each label. A key feature of the multi-l…
feature selectionFew-Shot Image Classificationimage-classificationImage Classification+1Semi-Supervised Risk Control via Prediction-Powered Inference
The risk-controlling prediction sets (RCPS) framework is a general tool for transforming the output of any machine learning model to design a predictive rule with rigorous error rate control. The key idea behind this fra…
Few-Shot Image Classificationimage-classificationImage ClassificationPrediction+1Multi-Level Correlation Network For Few-Shot Image Classification
Few-shot image classification(FSIC) aims to recognize novel classes given few labeled images from base classes. Recent works have achieved promising classification performance, especially for metric-learning methods, whe…
Few-Shot Image Classificationimage-classificationImage ClassificationMetric Learning+1Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models
In this paper, we present a Neuron Abandoning Attention Flow (NAFlow) method to address the open problem of visually explaining the attention evolution dynamics inside CNNs when making their classification decisions. A n…
ClassificationContrastive LearningDecision MakingFew-Shot Image Classification+3Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification
This study aims to optimize the few-shot image classification task and improve the model's feature extraction and classification performance by combining self-supervised learning with the deep network model ResNet-101. D…
ClassificationFew-Shot Image Classificationimage-classificationImage Classification+1FewVS: A Vision-Semantics Integration Framework for Few-Shot Image Classification
Some recent methods address few-shot image classification by extracting semantic information from class names and devising mechanisms for aligning vision and semantics to integrate information from both modalities. Howev…
Few-Shot Image ClassificationFew-Shot Learningimage-classificationImage ClassificationSemantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP
A text encoder within Vision-Language Models (VLMs) like CLIP plays a crucial role in translating textual input into an embedding space shared with images, thereby facilitating the interpretative analysis of vision tasks…
Few-Shot Image Classificationimage-classificationImage ClassificationImage Retrieval+2Learning to Obstruct Few-Shot Image Classification over Restricted Classes
Advancements in open-source pre-trained backbones make it relatively easy to fine-tune a model for new tasks. However, this lowered entry barrier poses potential risks, e.g., bad actors developing models for harmful appl…
AttributeClassificationFew-Shot Image Classificationimage-classification+1Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms
In the context of few-shot classification, the goal is to train a classifier using a limited number of samples while maintaining satisfactory performance. However, traditional metric-based methods exhibit certain limitat…
Cross-Domain Few-ShotFew-Shot Image Classificationimage-classificationImage ClassificationLocal Descriptors Weighted Adaptive Threshold Filtering For Few-Shot Learning
Few-shot image classification is a challenging task in the field of machine learning, involving the identification of new categories using a limited number of labeled samples. In recent years, methods based on local desc…
Few-Shot Image ClassificationFew-Shot Learningimage-classificationImage Classification+1Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules
Few-shot classification involves identifying new categories using a limited number of labeled samples. Current few-shot classification methods based on local descriptors primarily leverage underlying consistent features …
ClassificationFew-Shot Image ClassificationFew-Shot Learningimage-classification+1A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class
Few-shot image classification aims to classify novel classes with few labeled samples. Recent research indicates that deep local descriptors have better representational capabilities. These studies recognize the impact o…
ClassificationFew-Shot Image ClassificationFew-Shot Learningimage-classification+1Improved Few-Shot Image Classification Through Multiple-Choice Questions
Through a simple multiple choice language prompt a VQA model can operate as a zero-shot image classifier, producing a classification label. Compared to typical image encoders, VQA models offer an advantage: VQA-produced …
ArticlesFew-Shot Image Classificationimage-classificationImage Classification+2