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Using Text and Visual Cues for Fine-Grained Classification

Zaryab Shaker, Xiao Feng, Muhammad Adeel Ahmed Tahir
2021 International Journal of Advanced Network, Monitoring, and Controls  
The main focus is combining textual cues and visual cues in deep neural network. First the text is recognized and classified from the image.  ...  Then we combine the attended word embedding and visual feature vector which are then optimized by CNN for Fine-grained image classification.  ...  In 2016 [32] uses multilayer and multimodal fusion of deep neural networks for video classification. In 2017 [33] uses weakly paired multimodal fusion for object recognition.  ... 
doi:10.21307/ijanmc-2021-026 fatcat:o7ostmko7bbljjfdmek4f5s5nm

Deep Features Analysis with Attention Networks [article]

Shipeng Xie, Da Chen, Rong Zhang, Hui Xue
2019 arXiv   pre-print
Deep neural network models have recently draw lots of attention, as it consistently produce impressive results in many computer vision tasks such as image classification, object detection, etc.  ...  In this paper, we propose a novel method to interpret the neural network models with attention mechanism.  ...  It combines feature representation visualization of neural networks and attention mechanism.  ... 
arXiv:1901.10042v1 fatcat:v7vzfn7ldfgd7jief5xzvmfy7m

Understanding More about Human and Machine Attention in Deep Neural Networks [article]

Qiuxia Lai, Salman Khan, Yongwei Nie, Jianbing Shen, Hanqiu Sun, Ling Shao
2020 arXiv   pre-print
In view of these conflicting evidence, here we make a systematic study on using artificial attention and human attention in neural network design.  ...  Understanding the relation between human and machine attention is important for interpreting and designing neural networks.  ...  CONCLUSION We provide an in-depth analysis for human and artificial attention mechanisms in deep neural networks.  ... 
arXiv:1906.08764v3 fatcat:en74dsgmlze6pfnh46wnojavce

Attentional Network for Visual Object Detection [article]

Kota Hara, Ming-Yu Liu, Oncel Tuzel, Amir-massoud Farahmand
2017 arXiv   pre-print
We propose augmenting deep neural networks with an attention mechanism for the visual object detection task.  ...  As detecting objects in an image, the network adaptively places a sequence of glimpses of different shapes at different locations in the image.  ...  The consistent performance improvement over the baseline method verified the benefit of incorporating the attention mechanism to the deep neural networks for the visual object detection task.  ... 
arXiv:1702.01478v1 fatcat:t3ibcr76gve6fel7goew2on7eq

An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI [article]

Xin Zhang, Liangxiu Han, Wenyong Zhu, Liang Sun, Daoqiang Zhang
2020 arXiv   pre-print
In this work, we have proposed a novel computer-aided approach for early diagnosis of AD by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from  ...  Different from the existing approaches, the novelty of our approach is three-fold: 1) A Residual Self-Attention Deep Neural Network has been proposed to capture local, global and spatial information of  ...  Different from the existing approaches, our contributions lie in: 1) A Residual Attention Deep Neural Network has been designed and implemented, allowing for capturing local, global and spatial information  ... 
arXiv:2008.04024v1 fatcat:3g3j3qgkqfdchabdpymv6yale4

Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation

Heyi Li, Yunke Tian, Klaus Mueller, Xin Chen
2019 Image and Vision Computing  
As such, our proposed SR map constitutes a convenient visual interface which unveils the visual attention of the network and reveals which type of objects the model has learned to recognize after training  ...  Despite the tremendous achievements of deep convolutional neural networks (CNNs) in many computer vision tasks, understanding how they actually work remains a significant challenge.  ...  Tengyu Ma for his comments on an earlier version of the manuscript.  ... 
doi:10.1016/j.imavis.2019.02.005 fatcat:4gumh6ftkjgkxfr7q3ktg63epq

Modeling Latent Attention Within Neural Networks [article]

Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L.S. Wong, Michael L. Littman
2017 arXiv   pre-print
In this work, we present a general method for visualizing an arbitrary neural network's inner mechanisms and their power and limitations.  ...  Deep neural networks are able to solve tasks across a variety of domains and modalities of data.  ...  The problem objective is to train an image classifier for detecting the presence of tanks in forests.  ... 
arXiv:1706.00536v2 fatcat:ys6sqdvoizgjpowwhl3bfl3wsi

Traffic Sign Detection and Recognition Based on Convolutional Neural Network

Premamayudu Bulla
2022 International Journal on Recent and Innovation Trends in Computing and Communication  
A deep learning-based convolutional neural network (CNN) model is one of the suitable approach for traffic sign detection and recognition.  ...  The digital image processing techniques for object recognition and extraction of features from visual objects is a huge process and include many conversions and pre-processing steps.  ...  attention paid to deep neural networks (DNNs), which have been developed for pattern recognition research and computer vision. [20] An innovative approach to object categorization that included two deep  ... 
doi:10.17762/ijritcc.v10i4.5533 fatcat:n4kmqhjcizeevodvdamsmhokby

Object Recognition with and without Objects

Zhuotun Zhu, Lingxi Xie, Alan Yuille
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
In this paper, we train deep neural networks on the foreground (object) and background (context) regions of images respectively.  ...  While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image.  ...  Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  ... 
doi:10.24963/ijcai.2017/505 dblp:conf/ijcai/ZhuXY17 fatcat:iu6apd2m55fazpmaaeesimnb7q

Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization [article]

Ruyi Ji, Longyin Wen, Libo Zhang, Dawei Du, Yanjun Wu, Chen Zhao, Xianglong Liu, Feiyue Huang
2020 arXiv   pre-print
An attention convolutional binary neural tree architecture is presented to address those problems for weakly supervised FGVC.  ...  In addition, we use the attention transformer module to enforce the network to capture discriminative features.  ...  Attention mechanism has played an important role in deep learning to mimic human visual mechanism.  ... 
arXiv:1909.11378v2 fatcat:fxvepmlnwvejfdf5m4coiksfsu

Application of Deep Learning in Art Therapy

T. Kim, Graduate School of Business IT of Kookmin University, Seoul, Korea, Y. Yoon, K. Lee, K.-Y. Kwahk, N. Kim
2021 International Journal of Machine Learning and Computing  
For this purpose, in this paper, we propose a CNN(Convolutional Neural Network)-based deep learning method for art therapy.  ...  Specifically, we apply the image captioning and attention techniques of deep learning to identify psychological features in each drawing.  ...  Object Classification for Sketch Image (House vs. Tree) International Journal of MachineLearning and Computing, Vol. 11, No. 6, November 2021  ... 
doi:10.18178/ijmlc.2021.11.6.1069 fatcat:yt7t3dc7xjgh3k5ah4abfsxqvu

Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization

Ruyi Ji, Longyin Wen, Libo Zhang, Dawei Du, Yanjun Wu, Chen Zhao, Xianglong Liu, Feiyue Huang
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
An attention convolutional binary neural tree is presented to address those problems for weakly supervised FGVC.  ...  In addition, we use the attention transformer module to enforce the network to capture discriminative features.  ...  Attention mechanism has played an important role in deep learning to mimic human visual mechanism.  ... 
doi:10.1109/cvpr42600.2020.01048 dblp:conf/cvpr/JiWZDWZLH20 fatcat:3ckjizkbhbaufk37rieyqomesa

A Comprehensive Survey of Deep Learning for Image Captioning [article]

Md. Zakir Hossain, Ferdous Sohel, Mohd Fairuz Shiratuddin, Hamid Laga
2018 arXiv   pre-print
Generating a description of an image is called image captioning. Image captioning requires to recognize the important objects, their attributes and their relationships in an image.  ...  We also discuss the datasets and the evaluation metrics popularly used in deep learning based automatic image captioning.  ...  This method introduced human gaze with the attention mechanism of deep neural networks in generating image captions.  ... 
arXiv:1810.04020v2 fatcat:javmi4oqffbvxn6m4d2hjmzbhi

Attentive Recurrent Comparators [article]

Pranav Shyam and Shubham Gupta and Ambedkar Dukkipati
2017 arXiv   pre-print
We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations.  ...  In the task of one-shot classification on the Omniglot dataset, we achieve the state of the art performance with an error rate of 1.5%.  ...  Acknowledgements We would like to thank Akshay Mehrotra, Gaurav Pandey and Siddharth Agrawal for their extensive support and feedback while developing the ideas in this work.  ... 
arXiv:1703.00767v3 fatcat:jhnkbgpdtveu5ocl75vm7zmrbq

Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features [article]

Simone Palazzo, Concetto Spampinato, Isaak Kavasidis, Daniela Giordano, Joseph Schmidt, Mubarak Shah
2020 arXiv   pre-print
We then carry out image classification and saliency detection on the learned manifold. Performance analyses show that our approach satisfactorily decodes visual information from neural signals.  ...  This, in turn, can be used to effectively supervise the training of deep learning models, as demonstrated by the high performance of image classification and saliency detection on out-of-training classes  ...  Martina Platania for supporting the data acquisition phase, Dr. Demian Faraci for the experimental results, and NVIDIA for the generous donation of two Titan X GPUs.  ... 
arXiv:1810.10974v2 fatcat:pe5fwfsjrzbbdeyakcpid6ryqy
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