Benchmarks
Datasets
Greatest papers with code
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.
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.
ACTION DETECTION ACTION RECOGNITION MULTI-LABEL LEARNING VIDEO UNDERSTANDING
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.
CLASSIFICATION EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION 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.
CLASSIFICATION EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING
Learning to Separate Object Sounds by Watching Unlabeled Video
ECCV 2018
• rhgao/separating-object-sounds
• 
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
Multi-Label Sampling based on Local Label Imbalance
Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed measure and sampling approaches for a variety of evaluation metrics, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.
Synthetic Oversampling of Multi-Label Data based on Local Label Distribution
Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods.
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces
Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III.
CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING MULTI-LABEL TEXT CLASSIFICATION
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning
Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing.
ACTIVE LEARNING MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING
CheXclusion: Fairness gaps in deep chest X-ray classifiers
We demonstrate that TPR disparities exist in the state-of-the-art classifiers in all datasets, for all clinical tasks, and all subgroups.
Ranked #1 on
Multi-Label Classification
on MIMIC-CXR
FAIRNESS MEDICAL DIAGNOSIS MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING