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Deep Imbalanced Attribute Classification using Visual Attention Aggregation
[article]
2018
arXiv
pre-print
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With that in mind, we propose an effective method that extracts and aggregates visual attention masks at different scales. ...
For many computer vision applications, such as image description and human identification, recognizing the visual attributes of humans is an essential yet challenging problem. ...
We argue that a solution to the deep imbalanced attribute classification problem should: (i) extract discriminative information, (ii) leverage visual information that is specific for each attribute, and ...
arXiv:1807.03903v2
fatcat:76gup5jrazfjlofuh2jjtv3dam
Deep Imbalanced Attribute Classification Using Visual Attention Aggregation
[chapter]
2018
Lecture Notes in Computer Science
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With that in mind, we propose an effective method that extracts and aggregates visual attention masks at different scales. ...
For many computer vision applications, such as image description and human identification, recognizing the visual attributes of humans is an essential yet challenging problem. ...
We argue that a solution to the deep imbalanced attribute classification problem should: (i) extract discriminative information, (ii) leverage visual information that is specific for each attribute, and ...
doi:10.1007/978-3-030-01252-6_42
fatcat:jft7ls2jxvhuflyczq7c6dhzgu
Data Augmentation Imbalance For Imbalanced Attribute Classification
[article]
2020
arXiv
pre-print
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Pedestrian attribute recognition is an important multi-label classification problem. ...
Extensive empirical evidence shows that our DAI algorithm achieves state-of-the-art results, based on pedestrian attribute datasets, i.e. standard PA-100K and PETA datasets. ...
Commonly-used practice such as attention model [9] extract and aggregate visual attention masks at different scales. ...
arXiv:2004.13628v3
fatcat:y74gv3odlfcbhd4s4epvs2kud4
SANet:Superpixel Attention Network for Skin Lesion Attributes Detection
[article]
2019
arXiv
pre-print
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To solve these problems, we propose a deep learning framework named superpixel attention network (SANet). ...
However, unlike lesion segmentation and melenoma classification, there are few deep learning methods and literatures focusing on this task. ...
The proposed model can also reformulate super-pixel classification problems as the superpixel segmentations and superpixel attention mechanism is leveraged to enhance the discriminative of visual features ...
arXiv:1910.08995v1
fatcat:g7wsfakianai3k6ji5kbpnpive
SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking
[article]
2021
arXiv
pre-print
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By virtue of the attention mechanism, we conduct a special attentional aggregation network (AAN) consisting of self-AAN and cross-AAN for raising the representation ability of features eventually. ...
To this concern, in this paper, a novel attentional Siamese tracker (SiamAPN++) is proposed for real-time UAV tracking. ...
In visual tracking, RASNet [16] adopts three attention branches to adapt the model without updating the model online in visual tracking. ...
arXiv:2106.08816v2
fatcat:tdkwspfkm5fibpa3urirt2oegy
Graphsite: Ligand-binding site classification using Deep Graph Neural Network
[article]
2021
bioRxiv
pre-print
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Deep learning is a modern artificial intelligence technology. It utilizes deep neural networks to handle complex tasks such as image classification and language translation. ...
Graph neural networks (GNNs) are deep learning models that operate on graphs. ...
Classification performance Two GNN-based methods are evaluated: GraphSite and GIN. GIN uses a sum aggregator, so the edge attributes are ignored. ...
doi:10.1101/2021.12.06.471420
fatcat:ctadiun7bfewnipdlm7oe55evq
Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning
2020
IEEE Access
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INDEX TERMS IDS, imbalanced network traffic, machine learning, deep learning, CSE-CIC-IDS2018. ...
This paper researches machine learning and deep learning for intrusion detection in imbalanced network traffic. ...
We use t-SNE to visualize the NSL-KDD and CSE-CIC-IDS2018 by dimensionality reduction [40] . ...
doi:10.1109/access.2020.3048198
fatcat:tln3mv5u4nadzkz4alexcw65uu
Reinforced Pedestrian Attribute Recognition with Group Optimization Reward
[article]
2022
arXiv
pre-print
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Then we employ an agent to recognize each group of attributes, which is trained with Deep Q-learning algorithm. ...
Two key challenges in PAR include complex alignment relations between images and attributes, and imbalanced data distribution. Existing approaches usually formulate PAR as a recognition task. ...
Although the visual attention mechanism has been successfully applied to PAR, the complexity of pedestrian images makes attention masks fail to obtain the position of a specific attribute. ...
arXiv:2205.14042v1
fatcat:4pzdeqexe5b77fb5fhka2f7oi4
Network Embedding with Completely-imbalanced Labels
2020
IEEE Transactions on Knowledge and Data Engineering
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2020 pre-print arXiv:2007.03545v1 fatcat:r5fzfhkbtjdtvbkmxveyriwcv4
However, existing semi-supervised methods would get unappealing results in the completely-imbalanced label setting where some classes have no labeled nodes at all. ...
Specifically, to benefit from the completely-imbalanced labels, RSDNE guarantees both intra-class similarity and inter-class dissimilarity in an approximate way. ...
For example, we can define some attributes like "wing", "climb" or "tail" for animals. Then we can train attribute recognizers using images and attribute information from seen classes. ...
doi:10.1109/tkde.2020.2971490
fatcat:fbgwesqkw5cq7gand5z6wva2nq
Deep Multi-Scale Resemblance Network for the Sub-class Differentiation of Adrenal Masses on Computed Tomography Images
[article]
2020
arXiv
pre-print
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We also augmented the training data with randomly sampled paired adrenal masses to reduce the influence of imbalanced training data. We used 229 CT scans of patients with adrenal masses. ...
Methods: We developed a deep multi-scale resemblance network (DMRN) to overcome these limitations and leveraged paired CNNs to evaluate the intra-class similarities. ...
AMs
Related Work Current automated medical imaging classification methods are: (i) traditional, using handcrafted features, with conventional classifiers; and (ii) deep learning using deep convolutional ...
arXiv:2007.14625v1
fatcat:3zce7zccnrfm7ize756imydjq4
Cross-Modality Interaction Network for Equine Activity Recognition Using Imbalanced Multi-Modal Data
2021
Sensors
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In conclusion, CMI-Net and CB focal loss effectively enhanced the equine activity classification performance using imbalanced multi-modal sensor data. ...
With the recent advances in deep learning, wearable sensors have increasingly been used in automated animal activity recognition. ...
recall confusion matrix aggregating the classification results under 6-fold cross-validation when using CMI-Net with CB focal loss (γ = 0.5). ...
doi:10.3390/s21175818
pmid:34502709
fatcat:cru27grwzzdcpiqv4ul74zeym4
Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network
[article]
2019
arXiv
pre-print
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One significant problem with adopting DNNs for skin cancer classification is that the class frequencies in the existing datasets are imbalanced. ...
Self-attention mechanism is used to directly model the long-range dependencies in the feature maps. ...
Despite the using of self-attention mechanism, the generated samples suffer from some artifacts due to unstable training behavior. The imbalanced learning rate (TTUR) is used to tackle this issue. ...
arXiv:1910.11960v1
fatcat:3ozy75wygbgy5k3qiejavlt2g4
A survey on generative adversarial networks for imbalance problems in computer vision tasks
2021
Journal of Big Data
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It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets ...
Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. ...
Deep Recurrent Attentive Writer (DRAW) [107] networks combine spatial attention mechanism with a sequential variational autoencoder. ...
doi:10.1186/s40537-021-00414-0
pmid:33552840
pmcid:PMC7845583
fatcat:g3p6hbjuj5c5vbe23ms4g6ed6q
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification
[article]
2022
arXiv
pre-print
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Traditional solutions for imbalanced classification (e.g. resampling) are ineffective in node classification without considering the graph structure. ...
The proposed framework is evaluated via several widely used graph datasets, showing that our proposed model consistently outperforms the existing state-of-the-art methods. ...
From this visualization, we can see that proposed GNN-CL model achieves the best classification results as well as a stable training process. ...
arXiv:2202.02529v1
fatcat:dsfblf6l4ve4dprmebiurr2xmy
Exploring to establish an appropriate model for image aesthetic assessment via CNN-based RSRL: An empirical study
[article]
2021
arXiv
pre-print
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This method employs the recurrent attention network to focus on some key regions to extract visual features. ...
In [7] , the authors propose a novel multimodal recurrent attention CNN, which incorporates the visual information with the text information. ...
arXiv:2106.03316v2
fatcat:dbxm4u4c6bh6lhpcld3m75fcq4
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