Papers Few-Shot Image Classification
“Few-Shot Image Classification” 태그가 달린 논문 368편 · 필터 해제
Large Language Models are Good Prompt Learners for Low-Shot Image Classification
Low-shot image classification, where training images are limited or inaccessible, has benefited from recent progress on pre-trained vision-language (VL) models with strong generalizability, e.g. CLIP. Prompt learning met…
ClassificationFew-Shot Image Classificationimage-classificationImage Classification+2Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled sample…
DiversityFew-Shot Image ClassificationFew-Shot LearningImage Augmentation+7Simple Semantic-Aided Few-Shot Learning
Learning from a limited amount of data, namely Few-Shot Learning, stands out as a challenging computer vision task. Several works exploit semantics and design complicated semantic fusion mechanisms to compensate for rare…
Few-Shot Image ClassificationFew-Shot LearningFew-Shot LearningFew-Shot Image ClassificationSimple Semantic-Aided Few-Shot LearningFew-Shot Classification & Segmentation Using Large Language Models Agent
The task of few-shot image classification and segmentation (FS-CS) requires the classification and segmentation of target objects in a query image, given only a few examples of the target classes. We introduce a method t…
ClassificationFew-Shot Image Classificationimage-classificationImage Classification+2Are LSTMs Good Few-Shot Learners?
Deep learning requires large amounts of data to learn new tasks well, limiting its applicability to domains where such data is available. Meta-learning overcomes this limitation by learning how to learn. In 2001, Hochrei…
Few-Shot Image ClassificationFew-Shot Learningimage-classificationImage Classification+1Context-Aware Meta-Learning
Large Language Models like ChatGPT demonstrate a remarkable capacity to learn new concepts during inference without any fine-tuning. However, visual models trained to detect new objects during inference have been unable …
Few-Shot Image ClassificationIn-Context LearningMeta-Learninguniversal meta-learningSubspace Adaptation Prior for Few-Shot Learning
Gradient-based meta-learning techniques aim to distill useful prior knowledge from a set of training tasks such that new tasks can be learned more efficiently with gradient descent. While these methods have achieved succ…
Few-Shot Image ClassificationFew-Shot Learningimage-classificationImage Classification+1PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification
Few-shot image classification has received considerable attention for addressing the challenge of poor classification performance with limited samples in novel classes. However, numerous studies have employed sophisticat…
ClassificationContrastive LearningFew-Shot Image ClassificationFew-Shot Learning+2SemiReward: A General Reward Model for Semi-supervised Learning
Semi-supervised learning (SSL) has witnessed great progress with various improvements in the self-training framework with pseudo labeling. The main challenge is how to distinguish high-quality pseudo labels against the c…
Few-Shot Image ClassificationImage ClassificationPseudo LabelSemi-supervised Audio Classification+3Logarithm-transform aided Gaussian Sampling for Few-Shot Learning
Few-shot image classification has recently witnessed the rise of representation learning being utilised for models to adapt to new classes using only a few training examples. Therefore, the properties of the representati…
ClassificationFew-Shot Image ClassificationFew-Shot Learningimage-classification+2MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification
Vision Transformer (ViT) models have recently emerged as powerful and versatile models for various visual tasks. Recently, a work called PMF has achieved promising results in few-shot image classification by utilizing pr…
Data AugmentationDiversityDomain AdaptationFew-Shot Image Classification+5PRE: Vision-Language Prompt Learning with Reparameterization Encoder
Large pre-trained vision-language models such as CLIP have demonstrated great potential in zero-shot transferability to downstream tasks. However, to attain optimal performance, the manual selection of prompts is necessa…
Few-Shot Image ClassificationPrompt EngineeringPrompt LearningLanguage Models as Black-Box Optimizers for Vision-Language Models
Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However, many VLMs rely on proprietary data and are not ope…
Few-Shot Image Classificationimage-classificationImage ClassificationImage Generation+2Support-Set Context Matters for Bongard Problems
Current machine learning methods struggle to solve Bongard problems, which are a type of IQ test that requires deriving an abstract "concept" from a set of positive and negative "support" images, and then classifying whe…
Few-Shot Image ClassificationFew-Shot LearningKnowledge-Aware Prompt Tuning for Generalizable Vision-Language Models
Pre-trained vision-language models, e.g., CLIP, working with manually designed prompts have demonstrated great capacity of transfer learning. Recently, learnable prompts achieve state-of-the-art performance, which howeve…
Few-Shot Image Classificationimage-classificationImage ClassificationTransfer LearningFew-shot Image Classification based on Gradual Machine Learning
Few-shot image classification aims to accurately classify unlabeled images using only a few labeled samples. The state-of-the-art solutions are built by deep learning, which focuses on designing increasingly complex deep…
ClassificationFew-Shot Image Classificationimage-classificationImage ClassificationDiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation
Few-shot learning aims to adapt models trained on the base dataset to novel tasks where the categories were not seen by the model before. This often leads to a relatively uniform distribution of feature values across cha…
Few-Shot Image ClassificationFew-Shot LearningFeature Activation Map: Visual Explanation of Deep Learning Models for Image Classification
Decisions made by convolutional neural networks(CNN) can be understood and explained by visualizing discriminative regions on images. To this end, Class Activation Map (CAM) based methods were proposed as powerful interp…
ClassificationContrastive LearningDeep LearningFew-Shot Image Classification+5FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?
Few-shot learning aims to train models that can be generalized to novel classes with only a few samples. Recently, a line of works are proposed to enhance few-shot learning with accessible semantic information from class…
Contrastive LearningFew-Shot Image ClassificationFew-Shot Learningimage-classification+3Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation
We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a Vision Transformer (ViT) pretrained with self-supervision. Our proposed method takes token representations from the…
Few-Shot Image Classificationimage-classificationImage ClassificationPseudo Label+1