Zero-Shot Learning
565 papers with code • 18 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 )
Further readings:
Benchmarks
These leaderboards are used to track progress in Zero-Shot Learning
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Libraries
Use these libraries to find Zero-Shot Learning models and implementations
CUB-200-2011
Oxford 102 Flower
PASCAL Context
AwA
SNIPS
AwA2
aPY
TVQA
LSMDC
MIT-States
Subtasks
Latest papers
Meta-Prompting for Automating Zero-shot Visual Recognition with LLMs
Prompt ensembling of Large Language Model (LLM) generated category-specific prompts has emerged as an effective method to enhance zero-shot recognition ability of Vision-Language Models (VLMs).
CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning
Large pre-trained VLMs like CLIP have demonstrated superior zero-shot recognition ability, and a number of recent studies leverage this ability to mitigate catastrophic forgetting in CL, but they focus on closed-set CL in a single domain dataset.
OpenGraph: Open-Vocabulary Hierarchical 3D Graph Representation in Large-Scale Outdoor Environments
In this work, we propose OpenGraph, the first open-vocabulary hierarchical graph representation designed for large-scale outdoor environments.
MolBind: Multimodal Alignment of Language, Molecules, and Proteins
Recent advancements in biology and chemistry have leveraged multi-modal learning, integrating molecules and their natural language descriptions to enhance drug discovery.
Split to Merge: Unifying Separated Modalities for Unsupervised Domain Adaptation
tl-uestc/unimos
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In this work, we introduce a Unified Modality Separation (UniMoS) framework for unsupervised domain adaptation.
Personalized LoRA for Human-Centered Text Understanding
yoyo-yun/plora
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Effectively and efficiently adapting a pre-trained language model (PLM) for human-centered text understanding (HCTU) is challenging since user tokens are million-level in most personalized applications and do not have concrete explicit semantics.
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification
xhz0809/x-shot
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In recent years, few-shot and zero-shot learning, which learn to predict labels with limited annotated instances, have garnered significant attention.
On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction
Recent zero-shot classification methods converted the task to other NLP tasks (e. g., textual entailment) and used off-the-shelf models of these NLP tasks to directly perform inference on the test data without using a large amount of IE annotation data.
CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification
The advancement of Zero-Shot Learning in the medical domain has been driven forward by using pre-trained models on large-scale image-text pairs, focusing on image-text alignment.
Can GNN be Good Adapter for LLMs?
In terms of efficiency, the GNN adapter introduces only a few trainable parameters and can be trained with low computation costs.
