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# Multi-Label Learning Edit

12 papers with code · Methodology

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# Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification

10 Feb 2020

Two different network architectures are respectively designed to extract features for two groups of attributes, and a novel dynamic weighting scheme is proposed to automatically assign the loss weight to each facial attribute during training.

# Weakly-Supervised Multi-Person Action Recognition in 360$^{\circ}$ Videos

9 Feb 2020

To enable research in this direction, we introduce 360Action, the first omnidirectional video dataset for multi-person action recognition.

# DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases

The objective in deep extreme multi-label learning is to jointly learn feature representations and classifiers to automatically tag data points with the most relevant subset of labels from an extremely large label set.

Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community.

# Classifier Chains: A Review and Perspectives

26 Dec 2019

This performance led to further studies of how exactly it works, and how it could be improved, and in the recent decade numerous studies have explored classifier chains mechanisms on a theoretical level, and many improvements have been made to the training and inference procedures, such that this method remains among the state-of-the-art options for multi-label learning.

# An Embarrassingly Simple Baseline for eXtreme Multi-label Prediction

17 Dec 2019

The goal of eXtreme Multi-label Learning (XML) is to design and learn a model that can automatically annotate a given data point with the most relevant subset of labels from an extremely large label set.

# Copula Multi-label Learning

This inspires us to develop a novel copula multi-label learning paradigm for modeling label and feature dependencies.

18 Nov 2019

Multi-label learning studies the problem where an instance is associated with a set of labels.

# Prototypical Networks for Multi-Label Learning

17 Nov 2019

By measuring the density function values, new instances mapped to the new space can easily identify their membership to possible multiple categories.

# Multi-Label Learning with Deep Forest

15 Nov 2019

In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models.