Papers Multi-Label Image Classification
“Multi-Label Image Classification” 태그가 달린 논문 131편 · 필터 해제
Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation
Semantic segmentation is a challenging task in the absence of densely labelled data. Only relying on class activation maps (CAM) with image-level labels provides deficient segmentation supervision. Prior works thus consi…
Auxiliary Learningimage-classificationImage ClassificationMulti-Label Image Classification+7Multi-Label Image Classification with Contrastive Learning
Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains. The success of constrastive learning in single-label classifications…
ClassificationContrastive Learningimage-classificationImage Classification+3Multi-layered Semantic Representation Network for Multi-label Image Classification
Multi-label image classification (MLIC) is a fundamental and practical task, which aims to assign multiple possible labels to an image. In recent years, many deep convolutional neural network (CNN) based approaches have …
Classificationimage-classificationImage ClassificationMulti-Label Classification+1Multi-Label Learning from Single Positive Labels
Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is considerably more challenging to annotate …
image-classificationImage ClassificationMissing LabelsMulti-Label Classification+3MlTr: Multi-label Classification with Transformer
The task of multi-label image classification is to recognize all the object labels presented in an image. Though advancing for years, small objects, similar objects and objects with high conditional probability are still…
Classificationimage-classificationImage ClassificationMulti-Label Classification+2GM-MLIC: Graph Matching based Multi-Label Image Classification
Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the co…
ClassificationGeneral ClassificationGraph Matchingimage-classification+2Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network
Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance…
Data AugmentationData Visualizationimage-classificationImage Classification+3Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark
Perhaps surprisingly sewerage infrastructure is one of the most costly infrastructures in modern society. Sewer pipes are manually inspected to determine whether the pipes are defective. However, this process is limited …
ClassificationGeneral ClassificationImage ClassificationMulti-Label Classification+2Efficient Online ML API Selection for Multi-Label Classification Tasks
Multi-label classification tasks such as OCR and multi-object recognition are a major focus of the growing machine learning as a service industry. While many multi-label prediction APIs are available, it is challenging f…
General Classificationimage-classificationImage ClassificationMulti-Label Classification+8Coarse to Fine: Multi-label Image Classification with Global/Local Attention
In our daily life, the scenes around us are always with multiple labels especially in a smart city, i.e., recognizing the information of city operation to response and control. Great efforts have been made by using Deep …
General Classificationimage-classificationImage ClassificationMulti-Label Image ClassificationDeep Semantic Dictionary Learning for Multi-label Image Classification
Compared with single-label image classification, multi-label image classification is more practical and challenging. Some recent studies attempted to leverage the semantic information of categories for improving multi-la…
ClassificationDictionary LearningGeneral Classificationimage-classification+2Tensor Composition Net for Visual Relationship Prediction
We present a novel Tensor Composition Net (TCN) to predict visual relationships in images. Visual Relationship Prediction (VRP) provides a more challenging test of image understanding than conventional image tagging and …
Extreme Multi-Label Classificationimage-classificationImage ClassificationImage Retrieval+6General Multi-label Image Classification with Transformers
Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the Classification Transformer (C-Tran), a ge…
ClassificationGeneral Classificationimage-classificationImage Classification+1Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task
In this paper, we run two methods of explanation, namely LIME and Grad-CAM, on a convolutional neural network trained to label images with the LEGO bricks that are visible in them. We evaluate them on two criteria, the i…
image-classificationImage ClassificationMulti-Label Image ClassificationReconstruction Regularized Deep Metric Learning for Multi-label Image Classification
In this paper, we present a novel deep metric learning method to tackle the multi-label image classification problem. In order to better learn the correlations among images features, as well as labels, we attempt to expl…
General Classificationimage-classificationImage ClassificationMetric Learning+1Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition
Multi-label image recognition is a practical and challenging task compared to single-label image classification. However, previous works may be suboptimal because of a great number of object proposals or complex attentio…
Diversityimage-classificationMulti-Label ClassificationMulti-Label Image Classification+1Unsupervised Image Classification for Deep Representation Learning
Deep clustering against self-supervised learning is a very important and promising direction for unsupervised visual representation learning since it requires little domain knowledge to design pretext tasks. However, the…
ClassificationClusteringContrastive LearningDeep Clustering+13Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
Training deep neural networks usually requires a large amount of labeled data to obtain good performance. However, in medical image analysis, obtaining high-quality labels for the data is laborious and expensive, as accu…
ClassificationGeneral Classificationimage-classificationImage Classification+5Learning from Noisy Labels with Noise Modeling Network
Multi-label image classification has generated significant interest in recent years and the performance of such systems often suffers from the not so infrequent occurrence of incorrect or missing labels in the training d…
General Classificationimage-classificationImage ClassificationMissing Labels+2Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision
One of the primary challenges limiting the applicability of deep learning is its susceptibility to learning spurious correlations rather than the underlying mechanisms of the task of interest. The resulting failure to ge…
counterfactualimage-classificationImage ClassificationMulti-Label Image Classification+5