Multi-Label Learning
Leaderboards
Greatest papers with code
Pedestrian Attribute Recognition: A Survey
• wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List
• 
We also review some popular network architectures which have widely applied in the deep learning community.
MULTI-LABEL LEARNING MULTI-TASK LEARNING PEDESTRIAN ATTRIBUTE RECOGNITION
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
Crowd sourcing has become a widely adopted scheme to collect ground truth labels.
#3 best model for
Facial Expression Recognition
on FERPlus
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding
An event happening in the world is often made of different activities and actions that can unfold simultaneously or sequentially within a few seconds.
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
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.
EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING
Learning to Separate Object Sounds by Watching Unlabeled Video
ECCV 2018
• rhgao/Deep-MIML-Network
•
Our work is the first to learn audio source separation from large-scale "in the wild" videos containing multiple audio sources per video.
AUDIO DENOISING AUDIO SOURCE SEPARATION DENOISING MULTI-LABEL LEARNING
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
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.
EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING
Food Ingredients Recognition through Multi-label Learning
Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet.
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity.
Variational Autoencoders for Sparse and Overdispersed Discrete Data
Many applications, such as text modelling, high-throughput sequencing, and recommender systems, require analysing sparse, high-dimensional, and overdispersed discrete (count-valued or binary) data.
Multi-label learning for dynamic model type recommendation
Our proposed framework builds a multi-label meta-classifier responsible for recommending a set of relevant model types based on the local data complexity of the region surrounding each test sample.