Papers Multi-Label Image Classification
“Multi-Label Image Classification” 태그가 달린 논문 131편 · 필터 해제
Learn to Predict Sets Using Feed-Forward Neural Networks
This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a set is not fixed in advance. Many real-wor…
image-classificationImage ClassificationMulti-Label Image Classificationobject-detection+1Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification
Multi-label image and video classification are fundamental yet challenging tasks in computer vision. The main challenges lie in capturing spatial or temporal dependencies between labels and discovering the locations of d…
ClassificationGeneral ClassificationGraph Embeddingimage-classification+5In-domain representation learning for remote sensing
Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community. To address it and to establish baselines and a common evaluation protocol in this domain…
Image ClassificationMulti-Label Image ClassificationRepresentation LearningScene ClassificationDistance-based Composable Representations with Neural Networks
We introduce a new deep learning technique that builds individual and class representations based on distance estimates to randomly generated contextual dimensions for different modalities. Recent works have demonstrated…
image-classificationImage ClassificationMulti-Label Image ClassificationRetrievalVisual Attention Consistency Under Image Transforms for Multi-Label Image Classification
Human visual perception shows good consistency for many multi-label image classification tasks under certain spatial transforms, such as scaling, rotation, flipping and translation. This has motivated the data augmentati…
ClassificationData AugmentationGeneral Classificationimage-classification+3Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification
In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g. pedestrian, bicycle and tree)…
General Classificationimage-classificationImage ClassificationMulti-Label Image ClassificationMulti-View Matrix Completion for Multi-Label Image Classification
There is growing interest in multi-label image classification due to its critical role in web-based image analytics-based applications, such as large-scale image retrieval and browsing. Matrix completion has recently bee…
ClassificationGeneral Classificationimage-classificationImage Classification+6SoDeep: a Sorting Deep net to learn ranking loss surrogates
Several tasks in machine learning are evaluated using non-differentiable metrics such as mean average precision or Spearman correlation. However, their non-differentiability prevents from using them as objective function…
image-classificationImage ClassificationImage RetrievalMulti-Label Image Classification+1BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding
This paper presents the BigEarthNet that is a new large-scale multi-label Sentinel-2 benchmark archive. The BigEarthNet consists of 590,326 Sentinel-2 image patches, each of which is a section of i) 120x120 pixels for 10…
Multi-Label Image ClassificationScene ClassificationMulti-Label Adversarial Perturbations
Adversarial examples are delicately perturbed inputs, which aim to mislead machine learning models towards incorrect outputs. While most of the existing work focuses on generating adversarial perturbations in multi-class…
General Classificationimage-classificationImage ClassificationMulti-class Classification+4A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks
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. Although performance g…
ClassificationData AugmentationGeneral Classificationimage-classification+3Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classificati…
ClassificationGeneral Classificationimage-classificationImage Classification+5Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection
Zero-shot learning transfers knowledge from seen classes to novel unseen classes to reduce human labor of labelling data for building new classifiers. Much effort on zero-shot learning however has focused on the standard…
image-classificationImage ClassificationMulti-Label Image ClassificationMulti-label zero-shot learning+2Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning
Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervised algorithms to achieve accurate predi…
ClusteringGeneral Classificationimage-classificationImage Classification+15Structured Label Inference for Visual Understanding
Visual data such as images and videos contain a rich source of structured semantic labels as well as a wide range of interacting components. Visual content could be assigned with fine-grained labels describing major comp…
Action DetectionGeneral Classificationimage-classificationImage Classification+3Saliency-based Sequential Image Attention with Multiset Prediction
Humans process visual scenes selectively and sequentially using attention. Central to models of human visual attention is the saliency map. We propose a hierarchical visual architecture that operates on a saliency map an…
ClassificationGeneral Classificationimage-classificationImage Classification+5Multi-label Image Recognition by Recurrently Discovering Attentional Regions
This paper proposes a novel deep architecture to address multi-label image recognition, a fundamental and practical task towards general visual understanding. Current solutions for this task usually rely on an extra step…
General Classificationimage-classificationImage ClassificationMulti-Label Image Classification+2Attribute Recognition by Joint Recurrent Learning of Context and Correlation
Recognising semantic pedestrian attributes in surveillance images is a challenging task for computer vision, particularly when the imaging quality is poor with complex background clutter and uncontrolled viewing conditio…
AttributeDecoderimage-classificationImage Classification+2Joint Learning of Set Cardinality and State Distribution
We present a novel approach for learning to predict sets using deep learning. In recent years, deep neural networks have shown remarkable results in computer vision, natural language processing and other related problems…
image-classificationImage ClassificationMulti-Label Image ClassificationvalidDeep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model
Pedestrian attribute inference is a demanding problem in visual surveillance that can facilitate person retrieval, search and indexing. To exploit semantic relations between attributes, recent research treats it as a mul…
Attributeimage-classificationImage ClassificationMulti-Label Image Classification+2