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ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity
[article]
2021
arXiv
pre-print
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ALADIN takes a weakly supervised approach to learning a representation for fine-grained style similarity of digital artworks, leveraging BAM-FG, a novel large-scale dataset of user generated content groupings ...
We present ALADIN (All Layer AdaIN); a novel architecture for searching images based on the similarity of their artistic style. ...
Finally, we demonstrate the benefit of contrastive learning for weakly supervised learning, by baselining performance against softmax loss for an n−way classification using project group membership as ...
arXiv:2103.09776v1
fatcat:gddjgr4zcnetlp26aihmy2aerq
Front Matter: Volume 12032
2022
Medical Imaging 2022: Image Processing
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segmentation of anatomical structures in fetal torso ultrasound [12032-114] 3A Calcium scoring in low-dose ungated chest CT scans using convolutional long-short term memory networks [12032-115] 3B Weakly ...
diffusion-weighted data using tagged magnetic resonance imaging [
models for organ contouring in head and neck radiotherapy [12032-13] 0G Automatic classification of MRI contrasts using a deep siamese ...
doi:10.1117/12.2638192
fatcat:ikfgnjefaba2tpiamxoftyi6sa
Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection
[article]
2020
arXiv
pre-print
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In this paper, a special decision surface for the weakly-supervised sound event detection (SED) and a disentangled feature (DF) for the multi-label problem in polyphonic SED are proposed. ...
We present a method of generating instance-level probabilities for the embedding level approaches which tend to perform better than the instance-level approaches in terms of bag-level classification but ...
SED: a fully convolutional network with a GMP module [23] , a joint detection-classification (JDC) model with a ATP module [24] , a convolutional neural network (CNN) based model with a GAP module ...
arXiv:1905.10091v6
fatcat:rpm2m4tskjdirnq6vfw2pzqltm
Group-disentangled Representation Learning with Weakly-Supervised Regularization
[article]
2021
arXiv
pre-print
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We investigate learning group-disentangled representations for groups of factors with weak supervision. ...
Further, we demonstrate that learning group-disentangled representations improve upon downstream tasks, including fair classification and 3D shape-related tasks such as reconstruction, classification, ...
ACKNOWLEDGMENTS We thank Hooman Shayani and Tonya Custis for useful discussions and comments on the paper. ...
arXiv:2110.12185v1
fatcat:ywcwv65qbrbk7b57mrzj27x2ui
2021 Index IEEE Transactions on Multimedia Vol. 23
2021
IEEE transactions on multimedia
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TMM 2021 3306-3317 Anisotropic Graph Convolutional Network for Semi-Supervised Learning. ...
., +, TMM 2021 4014-4026 Anisotropic magnetoresistance Anisotropic Graph Convolutional Network for Semi-Supervised Learning. ...
doi:10.1109/tmm.2022.3141947
fatcat:lil2nf3vd5ehbfgtslulu7y3lq
Weakly Supervised Visual Semantic Parsing
[article]
2020
arXiv
pre-print
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Additionally, we propose the first graph-based weakly supervised learning framework, based on a novel graph alignment algorithm, which enables training without bounding box annotations. ...
Through extensive experiments, we show that VSPNet outperforms weakly supervised baselines significantly and approaches fully supervised performance, while being several times faster. ...
Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. ...
arXiv:2001.02359v2
fatcat:mdxk3kd77bgploitjm3j7obtqa
Explanatory Graphs for CNNs
[article]
2018
arXiv
pre-print
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Each filter in a conv-layer of a CNN for object classification usually represents a mixture of object parts. ...
We develop a simple yet effective method to disentangle object-part pattern components from each filter. ...
INTRODUCTION Convolutional neural networks (CNNs) [9] , [12] , [15] have exhibited superior performance in various visual tasks, for example, object classification and detection. ...
arXiv:1812.07997v1
fatcat:vq7pzmcqgbhzvlulnt4ypgt5mq
Weakly Supervised Visual Semantic Parsing
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Additionally, we propose the first graphbased weakly supervised learning framework, based on a novel graph alignment algorithm, which enables training without bounding box annotations. ...
Through extensive experiments, we show that VSPNET outperforms weakly supervised baselines significantly and approaches fully supervised performance, while being several times faster. ...
Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. ...
doi:10.1109/cvpr42600.2020.00379
dblp:conf/cvpr/ZareianKC20
fatcat:h3v5phfq3ze4refvwhfntufbri
Interpreting CNN Knowledge via an Explanatory Graph
[article]
2017
arXiv
pre-print
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More importantly, we learn the explanatory graph for a pre-trained CNN in an unsupervised manner, i.e., without a need of annotating object parts. ...
Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns ...
These methods did not use part annotations, and only used object boxes for learning/selection. ...
arXiv:1708.01785v3
fatcat:vct2ttai75bplhqeghapwwwyli
WildGait: Learning Gait Representations from Raw Surveillance Streams
[article]
2021
arXiv
pre-print
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As such, we propose a novel weakly supervised learning framework, WildGait, which consists of training a Spatio-Temporal Graph Convolutional Network on a large number of automatically annotated skeleton ...
Existing methods for gait recognition require cooperative gait scenarios, in which a single person is walking multiple times in a straight line in front of a camera. ...
Similar to us, applies a graph convolutional network to process skeleton sequences, but use a final pyramid pooling layer for recognition. ...
arXiv:2105.05528v5
fatcat:vab4fmeh2rdcri5pttq723njwu
2021 Index IEEE Transactions on Image Processing Vol. 30
2021
IEEE Transactions on Image Processing
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TIP 2021 6648-6658 AP-CNN: Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification. ...
., +, TIP 2021 2810-2825 AP-CNN: Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification. ...
doi:10.1109/tip.2022.3142569
fatcat:z26yhwuecbgrnb2czhwjlf73qu
Aligned to the Object, not to the Image: A Unified Pose-aligned Representation for Fine-grained Recognition
[article]
2018
arXiv
pre-print
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network. ...
The effectiveness of this paradigm relative to competing methods suggests the critical importance of disentangling pose and appearance for continued progress in fine-grained recognition. ...
We contrast the pose-aligned regions with weakly-supervised regions that are generated in a purely data-driven fashion and with "axis-aligned" rectangular bounding boxes centered around a keypoint or landmark ...
arXiv:1801.09057v4
fatcat:nwuvizkmebfbrhonnkfwkuhxdy
DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization
[article]
2022
arXiv
pre-print
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In this paper, we survey related deep learning (DL) works in SciVis, specifically in the direction of DL4SciVis: designing DL solutions for solving SciVis problems. ...
We classify and discuss these works along six dimensions: domain setting, research task, learning type, network architecture, loss function, and evaluation metric. ...
The authors would like to thank the anonymous reviewers for their insightful comments. ...
arXiv:2204.06504v1
fatcat:33fc2smtuffwll6pghdffbebi4
VAE-CE: Visual Contrastive Explanation using Disentangled VAEs
[article]
2021
arXiv
pre-print
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We build the model using a disentangled VAE, extended with a new supervised method for disentangling individual dimensions. ...
Such concepts can also be highly useful for interpreting the model's classifications. ...
we can expect well-disentangled representations,
of explanation in the context of image classification using showing that strong inductive biases or supervision are a
neural networks, and disentanglement ...
arXiv:2108.09159v1
fatcat:onejltviqzavfm6phethxxux3q
MakeupBag: Disentangling Makeup Extraction and Application
[article]
2020
arXiv
pre-print
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We solve makeup disentanglement and facial makeup application as separable objectives, in contrast to other current deep methods that entangle the two tasks. ...
This paper introduces MakeupBag, a novel method for automatic makeup style transfer. ...
Weakly supervised segmentation MakeupBag addresses makeup extraction as a weaklysupervised segmentation task. ...
arXiv:2012.02157v1
fatcat:icgnqmikgnaw5lrluqnmd6cygy
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