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Multi-Label Learning

14 papers with code · Methodology

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Pedestrian Attribute Recognition: A Survey

22 Jan 2019wangxiao5791509/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

Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding

1 Nov 2019zhoubolei/moments_models

An event happening in the world is often made of different activities and actions that can unfold simultaneously or sequentially within a few seconds.

ACTION DETECTION MULTI-LABEL LEARNING VIDEO UNDERSTANDING

DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification

8 Sep 2016Refefer/fastxml

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

17 Apr 2019tomtung/omikuji

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

27 Jul 2017MarcBS/food_ingredients_recognition

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

MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING

Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction

EMNLP 2018 WHUNLPLab/Papers-to-read

A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity.

MULTI-LABEL LEARNING RELATION EXTRACTION

Variational Autoencoders for Sparse and Overdispersed Discrete Data

2 May 2019ethanhezhao/NBVAE

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 RECOMMENDATION SYSTEMS

Multi-label learning for dynamic model type recommendation

1 Apr 2020marianaasouza/dynamic-model-recommender

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.

MULTI-LABEL LEARNING RECOMMENDATION SYSTEMS