Zero-Shot Learning
576 papers with code • 19 benchmarks • 29 datasets
Zero-shot learning (ZSL) is a model's ability to detect classes never seen during training. The condition is that the classes are not known during supervised learning.
Earlier work in zero-shot learning use attributes in a two-step approach to infer unknown classes. In the computer vision context, more recent advances learn mappings from image feature space to semantic space. Other approaches learn non-linear multimodal embeddings. In the modern NLP context, language models can be evaluated on downstream tasks without fine tuning.
Benchmark datasets for zero-shot learning include aPY, AwA, and CUB, among others.
( Image credit: Prototypical Networks for Few shot Learning in PyTorch )
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Latest papers with no code
Small Language Models are Good Too: An Empirical Study of Zero-Shot Classification
This study is part of the debate on the efficiency of large versus small language models for text classification by prompting. We assess the performance of small language models in zero-shot text classification, challenging the prevailing dominance of large models. Across 15 datasets, our investigation benchmarks language models from 77M to 40B parameters using different architectures and scoring functions.
Evolving Interpretable Visual Classifiers with Large Language Models
To address these limitations, we present a novel method that discovers interpretable yet discriminative sets of attributes for visual recognition.
OTTER: Improving Zero-Shot Classification via Optimal Transport
Popular zero-shot models suffer due to artifacts inherited from pretraining.
`Eyes of a Hawk and Ears of a Fox': Part Prototype Network for Generalized Zero-Shot Learning
Current approaches in Generalized Zero-Shot Learning (GZSL) are built upon base models which consider only a single class attribute vector representation over the entire image.
Connecting NeRFs, Images, and Text
Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage.
Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning
ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information.
Anchor-based Robust Finetuning of Vision-Language Models
Specifically, two types of anchors are elaborated in our method, including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner, ii) image-text-pair anchor which is retrieved from the dataset similar to pretraining data of CLIP according to the downstream task, associating with the original CLIP text with rich semantics.
Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework
Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples.
High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning
However, current attention-based models may overlook the transferability of visual features and the distinctiveness of attribute localization when learning regional features in images.
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models
In this paper, we explore the feasibility of leveraging language as a naturally high-quality supervision for chest CT imaging.