Multi-Label Learning

69 papers with code • 1 benchmarks • 6 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution

Microsoft/FERPlus 3 Aug 2016

Crowd sourcing has become a widely adopted scheme to collect ground truth labels.

A Survey on Extreme Multi-label Learning

siddsax/XML-CNN 8 Oct 2022

Multi-label learning has attracted significant attention from both academic and industry field in recent decades.

Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification

xmc-aalto/bonsai 17 Apr 2019

In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.

MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning

zeroqiaoba/mer2023-baseline 18 Apr 2023

The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia.

Deep Region and Multi-Label Learning for Facial Action Unit Detection

zkl20061823/DRML CVPR 2016

Region learning (RL) and multi-label learning (ML) have recently attracted increasing attentions in the field of facial Action Unit (AU) detection.

DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification

Refefer/fastxml 8 Sep 2016

In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size.

Food Ingredients Recognition through Multi-label Learning

MarcBS/food_ingredients_recognition 27 Jul 2017

Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet.

Learning to Separate Object Sounds by Watching Unlabeled Video

rhgao/Deep-MIML-Network ECCV 2018

Our work is the first to learn audio source separation from large-scale "in the wild" videos containing multiple audio sources per video.

Synthetic Oversampling of Multi-Label Data based on Local Label Distribution

tsoumakas/mulan 2 May 2019

Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods.

Self-Paced Multi-Label Learning with Diversity

amjadseyedi/SPMLD 8 Oct 2019

The major challenge of learning from multi-label data has arisen from the overwhelming size of label space which makes this problem NP-hard.