Browse > Methodology > Multi-Label Learning

Multi-Label Learning

12 papers with code ยท Methodology

Leaderboards

No evaluation results yet. Help compare methods by submit evaluation metrics.

Latest papers without code

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.

FACE DETECTION FACIAL ATTRIBUTE CLASSIFICATION FACIAL LANDMARK DETECTION MULTI-LABEL LEARNING MULTI-TASK LEARNING

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.

ACTION LOCALIZATION MULTI-LABEL LEARNING

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

ICLR 2020

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.

LEARNING WORD EMBEDDINGS MULTI-LABEL LEARNING

Adversarial Paritial Multi-label Learning

ICLR 2020

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.

MULTI-LABEL LEARNING

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.

MULTI-LABEL CLASSIFICATION 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.

MULTI-LABEL LEARNING

Copula Multi-label Learning

NeurIPS 2019

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

MULTI-LABEL LEARNING

A Multi-Task Gradient Descent Method for Multi-Label Learning

18 Nov 2019

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

MULTI-LABEL LEARNING

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

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.

MULTI-LABEL LEARNING