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Semantic pooling for image categorization using multiple kernel learning
2014
2014 IEEE International Conference on Image Processing (ICIP)
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We propose to select the detectors using Multiple Kernel Learning techniques. We carry out experiments on the well known VOC 2007 dataset, and show our semantic pooling obtains promising results. ...
In this paper, we propose a new method for taking into account the spatial information in image categorization. ...
In order to improve the results, we propose to learn the weights associated with each kernel (i.e., each pooling region) using Multiple Kernel Learning (MKL) [11] . ...
doi:10.1109/icip.2014.7025033
dblp:conf/icip/DurandPTC14
fatcat:fwmsdsw43vbjbksx3edtxbm3k4
Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction
[article]
2019
arXiv
pre-print
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In this paper, we propose an Structured Semantic Model (SSM) to tackles this challenge by designing a orthogonal base convolution and pooling model which adaptively learn the multi-scale base semantic ...
representation between features supervised by the click label.The output of SSM are then used in the Wide&Deep for CTR prediction.Experiments on two public datasets as well as real Weibo production dataset ...
First of all, we produce series of base semantic representation using base functions (the matrices8 mentioned above), and then use hidden layer to choose convolution kernel types and pooling scale at the ...
arXiv:1812.01353v5
fatcat:qacujwogpzct7h3ifipvspynpu
A Multi-Scale Learning Framework for Visual Categorization
[chapter]
2011
Lecture Notes in Computer Science
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We propose a Multiple Scale Learning (MSL) framework to learn the best weights for each scale in the pyramid. ...
We approach the MSL problem as solving a multiple kernel learning (MKL) task, which defines the optimal combination of base kernels constructed at different pyramid levels. ...
We are grateful for the anonymous reviewers for their helpful comments. This work is supported in part by the National Science Council of Taiwan under NSC98-2218-E-001-004 and NSC99-2631-H-001-018. ...
doi:10.1007/978-3-642-19315-6_24
fatcat:kbwjp3jc7bfvnkjpw3dwwm2uwu
Cross-X Learning for Fine-Grained Visual Categorization
[article]
2019
arXiv
pre-print
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In this paper, we propose Cross-X learning, a simple yet effective approach that exploits the relationships between different images and between different network layers for robust multi-scale feature ...
learning. ...
Preliminaries We begin by briefly reviewing the one-squeeze multiexcitation (OSME) block [28] that learns multiple attention region features for each input image. ...
arXiv:1909.04412v1
fatcat:i6rwtt352ng5xb2dlocec7gw74
Squeeze-and-Attention Networks for Semantic Segmentation
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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However, these attention mechanisms ignore an implicit sub-task of semantic segmentation and are constrained by the grid structure of convolution kernels. ...
The final segmentation results are produced by merging outputs from four hierarchical stages of a SANet to integrate multi-scale contexts for obtaining an enhanced pixel-wise prediction. ...
larger context is useful for semantic segmentation. ...
doi:10.1109/cvpr42600.2020.01308
dblp:conf/cvpr/ZhongLBHDLZLW20
fatcat:dpcj5weqafd4zoak7mpm7ap4l4
Home Photo Categorization Based on Photographic Region Templates
[chapter]
2005
Lecture Notes in Computer Science
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To enhance the categorization, both local and global concepts of the photos are modeled and their combined concept learning method for the photo categorization is proposed. ...
Experiment results show that the proposed method is useful to detect multi-category concepts for the home photo album. ...
In general, an image may contain multiple semantic concepts, and thus it is natural to obtain multiple concepts (i.e., categories) from the categorization process. ...
doi:10.1007/11562382_25
fatcat:ur5yki4tzjdspamr3p3auycebq
Deep Context Modeling for Semantic Segmentation
2017
2017 IEEE Winter Conference on Applications of Computer Vision (WACV)
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One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. ...
However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene parsing, face recognition). ...
These models are effective in modeling the spatial information of the image, which is useful for the semantic segmentation task [24] . ...
doi:10.1109/wacv.2017.14
dblp:conf/wacv/NguyenFS17
fatcat:dsa5ijjp35hhje2okth3yrfiha
Efficient Hybrid DCT-Wiener Algorithm Based Deep Learning Approach For Semantic Shape Segmentation
2022
Iraqi Journal of Science
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With the advent in the deep learning domain, lots of efforts are seen in applying deep learning algorithms for semantic segmentation. ...
Extensive experiments validate the advantages of adaptive DCT modeling of CNN in semantic segmentation and image classification. ...
Section 4 illustrates the proposed Hybrid DCT-AWWF based deep learning approach for image analysis using semantic labels. ...
doi:10.24996/ijs.2022.63.2.31
fatcat:zkeb47jftbdxxc6d5igh3iwlha
Squeeze-and-Attention Networks for Semantic Segmentation
[article]
2020
arXiv
pre-print
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However, these attention mechanisms ignore an implicit sub-task of semantic segmentation and are constrained by the grid structure of convolution kernels. ...
The final segmentation results are produced by merging outputs from four hierarchical stages of a SANet to integrate multi-scale contexts for obtaining an enhanced pixel-wise prediction. ...
larger context is useful for semantic segmentation. ...
arXiv:1909.03402v4
fatcat:bgavf77hs5h6zjeeto62kny24i
Fine-grained Classification via Categorical Memory Networks
[article]
2020
arXiv
pre-print
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Motivated by the desire to exploit patterns shared across classes, we present a simple yet effective class-specific memory module for fine-grained feature learning. ...
The attention scores with respect to each class prototype are used as weights to combine prototypes via weighted sum, producing a uniquely tailored response feature representation for a given input. ...
[9] introduce a bi-linear pooling in a kernelized framework, improving the computational efficiency of bi-linear pooling. Kong et al. ...
arXiv:2012.06793v1
fatcat:csxxhxynuzdnbmnxhznczbiiqy
Prototype Mixture Models for Few-shot Semantic Segmentation
[article]
2020
arXiv
pre-print
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Using a single prototype acquired directly from the support image to segment the query image causes semantic ambiguity. ...
In this paper, we propose prototype mixture models (PMMs), which correlate diverse image regions with multiple prototypes to enforce the prototype-based semantic representation. ...
Such a vector squeezing discriminative information across feature channels is used to guide the feature comparison between support image(s) and query images for semantic segmentation. ...
arXiv:2008.03898v2
fatcat:gzejkdc7xbenjpxrhd6oqio6v4
Ask the Image: Supervised Pooling to Preserve Feature Locality
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
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The two representations are then combined adaptively with Multiple Kernel Learning. ...
In this paper we propose a weighted supervised pooling method for visual recognition systems. ...
In our case, the combination of a semantic pooling (B+C) allows us to obtain a correct classification: part of the objects are weighted properly and the related features will have higher impact on the ...
doi:10.1109/cvpr.2014.114
dblp:conf/cvpr/FanelloNCMO14
fatcat:gjaawqnwkbg5jb4p6wxujnc45u
Semantic Segmentation with Second-Order Pooling
[chapter]
2012
Lecture Notes in Computer Science
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Instead of coding, we found that enriching local descriptors with additional image information leads to large performance gains, especially in conjunction with the proposed pooling methodology. ...
We show that second-order pooling over free-form regions produces results superior to those of the winning systems in the Pascal VOC 2011 semantic segmentation challenge, with models that are 20,000 times ...
We would like to thank the anonymous referees for helpful suggestions. ...
doi:10.1007/978-3-642-33786-4_32
fatcat:jtxdt5dg3zcexfyqqnimzyzesa
Temporal Unet: Sample Level Human Action Recognition using WiFi
[article]
2019
arXiv
pre-print
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To achieve WiFi-based sample-level action recognition, we fully analyze approaches in image-based semantic segmentation as well as in video-based frame-level action recognition, then propose a simple yet ...
Human doing actions will result in WiFi distortion, which is widely explored for action recognition, such as the elderly fallen detection, hand sign language recognition, and keystroke estimation. ...
kernels in WiFi distortion for multi-scale TOV, which is proven useful for action recognition. ...
arXiv:1904.11953v1
fatcat:hnhfuokzincoxjmjvk77wh26fq
High-Resolution Aerial Image Labeling With Convolutional Neural Networks
2017
IEEE Transactions on Geoscience and Remote Sensing
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2016 pre-print arXiv:1611.01962v1 fatcat:roqm7hgchvgsrbj4u7ablvuj2y
They were initially conceived for image categorization, i.e., the problem of assigning a semantic label to an entire input image. ...
In this paper we address the problem of dense semantic labeling, which consists in assigning a semantic label to every pixel in an image. ...
We can view the pixelwise semantic labeling problem as taking an image patch and categorizing its central pixel. ...
doi:10.1109/tgrs.2017.2740362
fatcat:mka6svy3afdklfv54l5rsxicva
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