Papers Multi-label zero-shot learning
“Multi-label zero-shot learning” 태그가 달린 논문 27편 · 필터 해제
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(ML)$^2$P-Encoder: On Exploration of Channel-Class Correlation for Multi-Label Zero-Shot Learning
Recent studies usually approach multi-label zero-shot learning (MLZSL) with visual-semantic mapping on spatial-class correlation, which can be computationally costly, and worse still, fails to capture fine-grained cl…
Multi-label zero-shot learningZero-Shot LearningOpen Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features
In computer vision, multi-label recognition are important tasks with many real-world applications, but classifying previously unseen labels remains a significant challenge. In this paper, we propose a novel algorithm, Al…
ClassificationDecoderMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer
Real-world recognition system often encounters the challenge of unseen labels. To identify such unseen labels, multi-label zero-shot learning (ML-ZSL) focuses on transferring knowledge by a pre-trained textual label embe…
Image-text matchingKnowledge DistillationLanguage ModellingMulti-Label Classification+7Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention
Multi-label zero-shot learning extends conventional single-label zero-shot learning to a more realistic scenario that aims at recognizing multiple unseen labels of classes for each input sample. Existing works usually ex…
Multi-label zero-shot learningZero-Shot LearningML-Decoder: Scalable and Versatile Classification Head
In this paper, we introduce ML-Decoder, a new attention-based classification head. ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data compared to global average …
ClassificationDecoderFine-Grained Image ClassificationImage Classification+3Discriminative Region-based Multi-Label Zero-Shot Learning
Multi-label zero-shot learning (ZSL) is a more realistic counter-part of standard single-label ZSL since several objects can co-exist in a natural image. However, the occurrence of multiple objects complicates the reason…
Image RetrievalMulti-label zero-shot learningzero-shot-classificationZero-Shot LearningContrastive Language-Image Pre-training for the Italian Language
CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts. The model is trained on a massive amount of English data and shows impressive per…
Image RetrievalMulti-label zero-shot learningMultimodal Deep LearningRetrieval+2Semantic Diversity Learning for Zero-Shot Multi-label Classification
Training a neural network model for recognizing multiple labels associated with an image, including identifying unseen labels, is challenging, especially for images that portray numerous semantically diverse labels. As c…
ClassificationDiversityImage RetrievalMulti-Label Classification+5Generative Multi-Label Zero-Shot Learning
Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized varia…
AttributeGenerative Adversarial Networkimage-classificationImage Classification+3Interaction Compass: Multi-Label Zero-Shot Learning of Human-Object Interactions via Spatial Relations
We study the problem of multi-label zero-shot recognition in which labels are in the form of human-object interactions (combinations of actions on objects), each image may contain multiple interactions and some inter…
Human-Object Interaction DetectionMulti-label zero-shot learningObjectZero-Shot LearningA Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning
In this work, we develop a shared multi-attention model for multi-label zero-shot learning. We argue that designing attention mechanism for recognizing multiple seen and unseen labels in an image is a non-trivial task as…
Multi-label zero-shot learningZero-Shot LearningZero-shot Learning for Audio-based Music Classification and Tagging
Audio-based music classification and tagging is typically based on categorical supervised learning with a fixed set of labels. This intrinsically cannot handle unseen labels such as newly added music genres or semantic w…
AttributeClassificationGeneral ClassificationMulti-label zero-shot learning+4Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection
Zero-shot learning transfers knowledge from seen classes to novel unseen classes to reduce human labor of labelling data for building new classifiers. Much effort on zero-shot learning however has focused on the standard…
image-classificationImage ClassificationMulti-Label Image ClassificationMulti-label zero-shot learning+2Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize …
General ClassificationKnowledge GraphsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3