Papers Few-Shot Image Classification
“Few-Shot Image Classification” 태그가 달린 논문 368편 · 필터 해제
Assessing Sample Quality via the Latent Space of Generative Models
Advances in generative models increase the need for sample quality assessment. To do so, previous methods rely on a pre-trained feature extractor to embed the generated samples and real samples into a common space for co…
Few-Shot Image Classificationimage-classificationImage ClassificationSiamese Transformer Networks for Few-shot Image Classification
Humans exhibit remarkable proficiency in visual classification tasks, accurately recognizing and classifying new images with minimal examples. This ability is attributed to their capacity to focus on details and identify…
ClassificationFew-Shot Image Classificationimage-classificationImage Classification+2Improving Hyperbolic Representations via Gromov-Wasserstein Regularization
Hyperbolic representations have shown remarkable efficacy in modeling inherent hierarchies and complexities within data structures. Hyperbolic neural networks have been commonly applied for learning such representations …
Few-Shot Image Classificationimage-classificationImage ClassificationLink Prediction+1AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation
Pre-trained vision-language models (VLMs) have shown impressive results in various visual classification tasks. However, we often fail to fully unleash their potential when adapting them for new concept understanding due…
Action RecognitionFew-Shot Image ClassificationFew-Shot Learningimage-classification+3GalLoP: Learning Global and Local Prompts for Vision-Language Models
Prompt learning has been widely adopted to efficiently adapt vision-language models (VLMs), e.g. CLIP, for few-shot image classification. Despite their success, most prompt learning methods trade-off between classificati…
DiversityDomain GeneralizationFew-Shot Image Classificationimage-classification+3The Balanced-Pairwise-Affinities Feature Transform
The Balanced-Pairwise-Affinities (BPA) feature transform is designed to upgrade the features of a set of input items to facilitate downstream matching or grouping related tasks. The transformed set encodes a rich represe…
Few-Shot Image ClassificationImage ClusteringPerson Re-IdentificationCooperative Meta-Learning with Gradient Augmentation
Model agnostic meta-learning (MAML) is one of the most widely used gradient-based meta-learning, consisting of two optimization loops: an inner loop and outer loop. MAML learns the new task from meta-initialization param…
Few-Shot Image Classificationimage-classificationImage ClassificationMeta-Learning+1Tiny models from tiny data: Textual and null-text inversion for few-shot distillation
Few-shot image classification involves classifying images using very few training examples. Recent vision foundation models show excellent few-shot transfer abilities, but are large and slow at inference. Using knowledge…
Few-Shot Image Classificationimage-classificationImage ClassificationKnowledge Distillation+2Class-relevant Patch Embedding Selection for Few-Shot Image Classification
Effective image classification hinges on discerning relevant features from both foreground and background ele- ments, with the foreground typically holding the critical informa- tion. While humans adeptly classify images…
Computational Efficiencyfeature selectionFew-Shot Image Classificationimage-classification+1Variational Neuron Shifting for Few-Shot Image Classification Across Domains
Few-shot image classification aims to recognize unseen classes with few labeled samples. Existing meta-learning models learn the ability of learning good representation or model parameters, in order to adapt to new ta…
Bayesian InferenceFew-Shot Image ClassificationFew-Shot Learningimage-classification+2Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification
CLIP showcases exceptional cross-modal matching capabilities due to its training on image-text contrastive learning tasks. However, without specific optimization for unimodal scenarios, its performance in single-modality…
Contrastive LearningFew-Shot Image Classificationimage-classificationImage ClassificationFrozen Feature Augmentation for Few-Shot Image Classification
Training a linear classifier or lightweight model on top of pretrained vision model outputs, so-called 'frozen features', leads to impressive performance on a number of downstream few-shot tasks. Currently, frozen featur…
ClassificationData AugmentationFew-Shot Image Classificationimage-classification+1Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models
The task of few-shot image classification and segmentation (FS-CS) involves classifying and segmenting target objects in a query image, given only a few examples of the target classes. We introduce the Vision-Instructed …
Few-Shot Image Classificationimage-classificationImage ClassificationIn-Context Learning+4Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
Recent successes suggest that parameter-efficient fine-tuning of foundation models as the state-of-the-art method for transfer learning in vision, replacing the rich literature of alternatives such as meta-learning. In t…
Domain GeneralizationFew-Shot Image ClassificationMeta-LearningMixture-of-Experts+3BECLR: Batch Enhanced Contrastive Few-Shot Learning
Learning quickly from very few labeled samples is a fundamental attribute that separates machines and humans in the era of deep representation learning. Unsupervised few-shot learning (U-FSL) aspires to bridge this gap b…
Contrastive LearningFew-Shot Image ClassificationFew-Shot LearningRepresentation Learning+2Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes
When training data is scarce, it is common to make use of a feature extractor that has been pre-trained on a large base dataset, either by fine-tuning its parameters on the ``target'' dataset or by directly adopting its …
Cross-Domain Few-ShotFew-Shot Image ClassificationFew-Shot Learningimage-classification+1LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning
Few-shot image classification has emerged as a key challenge in the field of computer vision, highlighting the capability to rapidly adapt to new tasks with minimal labeled data. Existing methods predominantly rely on im…
ClassificationFew-Shot Image ClassificationFew-Shot Learningimage-classification+1Transductive Zero-Shot and Few-Shot CLIP
Transductive inference has been widely investigated in few-shot image classification but completely overlooked in the recent fast growing literature on adapting vision-langage models like CLIP. This paper addresses t…
ClassificationFew-Shot Image Classificationimage-classificationImage ClassificationRAFIC: Retrieval-Augmented Few-shot Image Classification
Few-shot image classification is the task of classifying unseen images to one of N mutually exclusive classes, using only a small number of training examples for each class. The limited availability of these examples (de…
ClassificationFew-Shot Image Classificationimage-classificationImage Classification+2TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot Image Classification
Few-shot image classification aims to classify images from unseen novel classes with few samples. Recent works demonstrate that deep local descriptors exhibit enhanced representational capabilities compared to image-leve…
Few-Shot Image Classificationimage-classificationImage Classification