Papers Multi-Label Image Classification
“Multi-Label Image Classification” 태그가 달린 논문 131편 · 필터 해제
PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification
Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent space between the visual patch and linguisti…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Probability Guided Loss for Long-Tailed Multi-Label Image Classification
Long-tailed learning has attracted increasing attention in very recent years. Long-tailed multi-label image classification is one subtask and remains challenging and poorly researched. In this paper, we provide a fresh p…
Classificationimage-classificationImage ClassificationLong-tail Learning+3Pseudo Labels for Single Positive Multi-Label Learning
The cost of data annotation is a substantial impediment for multi-label image classification: in every image, every category must be labeled as present or absent. Single positive multi-label (SPML) learning is a cost-eff…
image-classificationImage ClassificationMissing LabelsMulti-Label Image Classification+1Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance
Fine-grained multi-label classification models have broad applications in e-commerce, such as visual based label predictions ranging from fashion attribute detection to brand recognition. One challenge to achieve satisfa…
AttributeClassificationimage-classificationImage Classification+3Category Query Learning for Human-Object Interaction Classification
Unlike most previous HOI methods that focus on learning better human-object features, we propose a novel and complementary approach called category query learning. Such queries are explicitly associated to interaction ca…
ClassificationDecoderHuman-Object Interaction Detectionimage-classification+3Unsupervised domain adaptation by learning using privileged information
Successful unsupervised domain adaptation is guaranteed only under strong assumptions such as covariate shift and overlap between input domains. The latter is often violated in high-dimensional applications like image cl…
Domain Adaptationimage-classificationImage ClassificationMulti-Label Image Classification+1Extending global-local view alignment for self-supervised learning with remote sensing imagery
Since large number of high-quality remote sensing images are readily accessible, exploiting the corpus of images with less manual annotation draws increasing attention. Self-supervised models acquire general feature repr…
Change DetectionContrastive LearningImage ClassificationKnowledge Distillation+5Causality Compensated Attention for Contextual Biased Visual Recognition
Visual attention does not always capture the essential object representation desired for robust predictions. Attention modules tend to underline not only the target object but also the common co-occurring context that th…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Image ClassificationObjectTowards Reliable Assessments of Demographic Disparities in Multi-Label Image Classifiers
Disaggregated performance metrics across demographic groups are a hallmark of fairness assessments in computer vision. These metrics successfully incentivized performance improvements on person-centric tasks such as face…
Fairnessimage-classificationImage ClassificationMulti-Label Image Classification+1Deep Dependency Networks for Multi-Label Classification
We propose a simple approach which combines the strengths of probabilistic graphical models and deep learning architectures for solving the multi-label classification task, focusing specifically on image and video data. …
Action ClassificationClassificationimage-classificationImage Classification+3Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification
In this paper, a discriminator-free adversarial-based Unsupervised Domain Adaptation (UDA) for Multi-Label Image Classification (MLIC) referred to as DDA-MLIC is proposed. Recently, some attempts have been made for intro…
Domain Adaptationimage-classificationImage ClassificationMulti-Label Image Classification+1Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains
This paper proposes an adaptive graph-based approach for multi-label image classification. Graph-based methods have been largely exploited in the field of multi-label classification, given their ability to model label co…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Deep Learning based Multi-Label Image Classification of Protest Activities
With the rise of internet technology amidst increasing rates of urbanization, sharing information has never been easier thanks to globally-adopted platforms for digital communication. The resulting output of massive amou…
Deep Learningimage-classificationImage ClassificationMulti-Label Image ClassificationScene-Aware Label Graph Learning for Multi-Label Image Classification
Multi-label image classification refers to assigning a set of labels for an image. One of the main challenges of this task is how to effectively capture the correlation among labels. Existing studies on this issue mo…
Graph Learningimage-classificationImage ClassificationMulti-Label Image Classification+2Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision
Multi-label image classification, which can be categorized into label-dependency and region-based methods, is a challenging problem due to the complex underlying object layouts. Although region-based methods are less lik…
Feature Correlationimage-classificationImage ClassificationMulti-Label Image ClassificationSSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
Self-supervised pre-training bears potential to generate expressive representations without human annotation. Most pre-training in Earth observation (EO) are based on ImageNet or medium-size, labeled remote sensing (RS) …
Earth ObservationMulti-Label Image ClassificationSelf-Supervised LearningMuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid
Multi-label image classification is a foundational topic in various domains. Multimodal learning approaches have recently achieved outstanding results in image representation and single-label image classification. For in…
Classificationimage-classificationImage ClassificationMulti-Label Classification+4G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification
Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, since it could be expensive in practice to annotate all the labels in every …
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Two-Stream Transformer for Multi-Label Image Classification
Multi-label image classification is a fundamental yet challenging task in computer vision that aims to identify multiple objects from a given image. Recent studies on this task mainly focus on learning cross-modal intera…
Classificationimage-classificationImage ClassificationMulti-Label Image Classification+1Multi Label Image Classification using Adaptive Graph Convolutional Networks (ML-AGCN)
In this paper, a novel graph-based approach for multi-label image classification called Multi-Label Adaptive Graph Convolutional Network (ML-AGCN) is introduced. Graph-based methods have shown great potential in the fiel…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1