Few-Shot Image Classification
201 papers with code • 88 benchmarks • 23 datasets
Few-Shot Image Classification is a computer vision task that involves training machine learning models to classify images into predefined categories using only a few labeled examples of each category (typically < 6 examples). The goal is to enable models to recognize and classify new images with minimal supervision and limited data, without having to train on large datasets. (typically < 6 examples)
( Image credit: Learning Embedding Adaptation for Few-Shot Learning )
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Latest papers with no code
Frozen 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.
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models
By enabling a VLM to interact with off-the-shelf vision models as tools, the proposed method is capable of classifying and segmenting target objects using only image-level labels.
Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes
Fine-tuning is ineffective for few-shot learning, since the target dataset contains only a handful of examples.
LDCA: 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.
TALDS-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.
Few-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.
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification
In particular, our method achieves 97. 07% and 90. 88% on 5-way 5-shot and 5-way 1-shot tasks of miniImageNet, which surpasses the state-of-the-art results with accuracy of 7. 27% and 8. 72%, respectively.
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification
In order to tackle these issues, we turn to the recently proposed parameter-efficient tuning methods, such as VPT, which updates only the newly added prompt parameters while keeping the pre-trained backbone frozen.
Knowledge-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.
Few-shot Image Classification based on Gradual Machine Learning
Specifically, our proposed solution extracts indicative feature representations by deep backbones, and then constructs both unary and binary factors based on the extracted features to facilitate gradual learning.