Multi-Label Image Classification

29 papers with code • 2 benchmarks • 3 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.

Most implemented papers

ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

arnoweng/CheXNet CVPR 2017

The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases.

Improving Pairwise Ranking for Multi-label Image Classification

raingo-ur/mll-tf CVPR 2017

Pairwise ranking, in particular, has been successful in multi-label image classification, achieving state-of-the-art results on various benchmarks.

Residual Attention: A Simple but Effective Method for Multi-Label Recognition

Kevinz-code/CSRA ICCV 2021

Multi-label image recognition is a challenging computer vision task of practical use.

Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification

zhufengx/SRN_multilabel CVPR 2017

Analysis of the learned SRN model demonstrates that it can effectively capture both semantic and spatial relations of labels for improving classification performance.

General Multi-label Image Classification with Transformers

QData/C-Tran CVPR 2021

Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image.

Multi-Label Learning from Single Positive Labels

elijahcole/single-positive-multi-label CVPR 2021

When the number of potential labels is large, human annotators find it difficult to mention all applicable labels for each training image.

CNN-RNN: A Unified Framework for Multi-label Image Classification

Lin-Zhipeng/CNN-RNN-A-Unified-Framework-for-Multi-label-Image-Classification 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 18 Feb 2018

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

Yochengliu/MLIC-KD-WSD 16 Sep 2018

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 20 Nov 2018

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.