About

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Greatest papers with code

Zero-shot Learning for Audio-based Music Classification and Tagging

5 Jul 2019kunimi00/ZSL_music_tagging

Audio-based music classification and tagging is typically based on categorical supervised learning with a fixed set of labels.

CLASSIFICATION MULTI-LABEL ZERO-SHOT LEARNING MUSIC CLASSIFICATION

Generative Multi-Label Zero-Shot Learning

27 Jan 2021akshitac8/Generative_MLZSL

Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge.

IMAGE CLASSIFICATION MULTI-LABEL ZERO-SHOT LEARNING

A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning

CVPR 2020 hbdat/cvpr20_LESA

Therefore, instead of generating attentions for unseen labels which have unknown behaviors and could focus on irrelevant regions due to the lack of any training sample, we let the unseen labels select among a set of shared attentions which are trained to be label-agnostic and to focus on only relevant/foreground regions through our novel loss.

MULTI-LABEL ZERO-SHOT LEARNING

Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

CVPR 2018 Phoenix1327/ML-ZSL

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.

CLASSIFICATION KNOWLEDGE GRAPHS MULTI-LABEL CLASSIFICATION MULTI-LABEL ZERO-SHOT LEARNING VISUAL REASONING