Multi-Label Image Classification
47 papers with code • 7 benchmarks • 9 datasets
The Multi-Label Image Classification focuses on predicting labels for images in a multi-class classification problem where each image may belong to more than one class.
Benchmarks
These leaderboards are used to track progress in Multi-Label Image Classification
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Libraries
Use these libraries to find Multi-Label Image Classification models and implementationsDatasets
MS COCO
BigEarthNet
VizWiz-Classification
Sewer-ML
Bengali.AI Handwritten Graphemes
dacl10k
Most implemented papers
CNN-RNN: A Unified Framework for Multi-label Image Classification
Lin-Zhipeng/CNN-RNN-A-Unified-Framework-for-Multi-label-Image-Classification
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CVPR 2016
While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image.
Structured Label Inference for Visual Understanding
daveboat/structured_label_inference
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In this paper, we exploit this rich structure for performing graph-based inference in label space for a number of tasks: multi-label image and video classification and action detection in untrimmed videos.
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs.
A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks
hellowangqian/multi-label-image-classification
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Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks.
SoDeep: a Sorting Deep net to learn ranking loss surrogates
technicolor-research/sodeep
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CVPR 2019
Our approach is based on a deep architecture that approximates the sorting of arbitrary sets of scores.
Visual Attention Consistency Under Image Transforms for Multi-Label Image Classification
hguosc/visual_attention_consistency
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CVPR 2019
In this paper, we further propose the assumption of perceptual consistency of visual attention regions for classification under such transforms, i. e., the attention region for a classification follows the same transform if the input image is spatially transformed.
In-domain representation learning for remote sensing
google-research/google-research
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Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community.
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
liuquande/SRC-MT
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It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data.
Unsupervised Image Classification for Deep Representation Learning
HIK-LAB/Unsupervised-Image-Classification
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Extensive experiments on ImageNet dataset have been conducted to prove the effectiveness of our method.
Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition
gaobb/MCAR
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To bridge the gap between global and local streams, we propose a multi-class attentional region module which aims to make the number of attentional regions as small as possible and keep the diversity of these regions as high as possible.
