Multi-label zero-shot learning
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Benchmarks
Most implemented
Label-Embedding for Image Classification
Zero-Shot Learning by Convex Combination of Semantic Embeddings
CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation
Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification
Label Propagation for Zero-shot Classification with Vision-Language Models
Papers
Epsilon: Exploring Comprehensive Visual-Semantic Projection for Multi-Label Zero-Shot Learning
This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein the model is trained to recognize multiple unseen classes within a sample (e.g., an image) based on seen cl…
Multi-label zero-shot learningZero-Shot LearningCLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation
Multi-label classification is an essential task utilized in a wide variety of real-world applications. Multi-label zero-shot learning is a method for classifying images into multiple unseen categories for which no traini…
ClassificationDecoderGeneralized Zero-Shot LearningMulti-Label Classification+5Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification
The task of medical image recognition is notably complicated by the presence of varied and multiple pathological indications, presenting a unique challenge in multi-label classification with unseen labels. This complexit…
Decoderimage-classificationImage ClassificationMedical Image Classification+7Label Propagation for Zero-shot Classification with Vision-Language Models
Vision-Language Models (VLMs) have demonstrated impressive performance on zero-shot classification, i.e. classification when provided merely with a list of class names. In this paper, we tackle the case of zero-shot clas…
ClassificationImage ClassificationMulti-label zero-shot learningTransductive Learning+3Query-Based Knowledge Sharing for Open-Vocabulary Multi-Label Classification
Identifying labels that did not appear during training, known as multi-label zero-shot learning, is a non-trivial task in computer vision. To this end, recent studies have attempted to explore the multi-modal knowledge o…
Knowledge DistillationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label zero-shot learning+1GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning
This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein, the model is trained to recognize multiple unseen classes within a sample (e.g., an image) based on seen c…
Multi-label zero-shot learningZero-Shot Learning