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Modelling local deep convolutional neural network features to improve fine-grained image classification. arXiv:1502.07802
, 2015
"... We propose a local modelling approach using deep convolu-tional neural networks (CNNs) for fine-grained image clas-sification. Recently, deep CNNs trained from large datasets have considerably improved the performance of object recog-nition. However, to date there has been limited work using these d ..."
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Cited by 1 (1 self)
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-provements can be achieved on the challenging Fish and UEC FOOD-100 datasets. Index Terms — fine-grained classification, deep convolu-tional neural networks, session variation modelling, Gaussian mixture models. 1.
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification
"... Fine-grained classification is challenging because cate-gories can only be discriminated by subtle and local dif-ferences. Variances in the pose, scale or rotation usually make the problem more difficult. Most fine-grained clas-sification systems follow the pipeline of finding foreground object or o ..."
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or object parts (where) to extract discriminative fea-tures (what). In this paper, we propose to apply visual attention to fine-grained classification task using deep neural network. Our pipeline integrates three types of attention: the bottom-up attention that propose candidate patches, the object
Relation Classification via Convolutional Deep Neural Network
"... The state-of-the-art methods used for relation classification are primarily based on statistical ma-chine learning, and their performance strongly depends on the quality of the extracted features. The extracted features are often derived from the output of pre-existing natural language process-ing ( ..."
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Cited by 11 (1 self)
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(NLP) systems, which leads to the propagation of the errors in the existing tools and hinders the performance of these systems. In this paper, we exploit a convolutional deep neural network (DNN) to extract lexical and sentence level features. Our method takes all of the word tokens as input without
Subset Feature Learning for Fine-Grained Category Classification
"... Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We pro-pose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specifi ..."
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Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We pro-pose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features
Hyper-class Augmented and Regularized Deep Learning for Fine-grained Image Classification
"... Deep convolutional neural networks (CNN) have seen tremendous success in large-scale generic object recogni-tion. In comparison with generic object recognition, fine-grained image classification (FGIC) is much more chal-lenging because (i) fine-grained labeled data is much more expensive to acquire ..."
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Cited by 1 (0 self)
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Deep convolutional neural networks (CNN) have seen tremendous success in large-scale generic object recogni-tion. In comparison with generic object recognition, fine-grained image classification (FGIC) is much more chal-lenging because (i) fine-grained labeled data is much more expensive to acquire
Part Detector Discovery in Deep Convolutional Neural Networks
"... Abstract. Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature represen-tations suitable for discrimination. However, part localization is a chal-lenging task due to the large variation of appearance and pose. In this paper, ..."
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Cited by 1 (0 self)
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Abstract. Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature represen-tations suitable for discrimination. However, part localization is a chal-lenging task due to the large variation of appearance and pose. In this paper
Holistic Classification of CT Attenuation Patterns for Interstitial Lung Diseases via Deep Convolutional Neural Networks
"... Abstract. Interstitial lung diseases (ILD) involve several abnormal imaging pat-terns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of the extent and nature of the diseases. Therefore it is importan ..."
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Abstract. Interstitial lung diseases (ILD) involve several abnormal imaging pat-terns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of the extent and nature of the diseases. Therefore
Deep Convolutional Neural Networks On Multichannel Time Series For Human Activity Recognition
"... This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined hu-man activities. In this problem, extracting effec-tive features for identifying activities is a critica ..."
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problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
"... We present a novel method for classifying emotions from static facial images. Our approach leverages on the recent success of Convolutional Neural Networks (CNN) on face recognition problems. Unlike the settings often assumed there, far less labeled data is typically available for train-ing emotion ..."
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We present a novel method for classifying emotions from static facial images. Our approach leverages on the recent success of Convolutional Neural Networks (CNN) on face recognition problems. Unlike the settings often assumed there, far less labeled data is typically available for train-ing emotion
Median Filtering Forensics Based on Convolutional Neural Networks
"... Abstract—Median filtering detection has recently drawn much attention in image editing and image anti-forensic techniques. Current image median filtering forensics algorithms mainly extract features manually. To deal with the challenge of detecting median filtering from small-size and compressed ima ..."
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image blocks, by taking into account of the properties of median filtering, we propose a median filtering detection method based on convolutional neural networks (CNNs), which can automatically learn and obtain features directly from the image. To our best knowledge, this is the first work of applying
Results 1 - 10
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