2015
CVPR
CVPR 2015
Deep LAC: Deep Localization, Alignment and Classification for Fine-Grained Recognition
Abstract
We propose a fine-grained recognition system that incorporates part localization, alignment, and classification in one deep neural network. This is a nontrivial process, as the input to the classification module should be functions that enable back-propagation in constructing the solver. Our major contribution is to propose a valve linkage function(VLF) for back-propagation chaining and form our deep localization, alignment and classification (LAC) system. The VLF can adaptively compromise the errors of classification and alignment when training the LAC model. It in turn helps update localization. The performance on fine-grained object data bears out the effectiveness of our LAC system.
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Interdisciplinary Bridge
— Computer Vision and Deep Learning and Machine Learning
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Keyword Pioneer
— valve linkage function
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Hot Topic Early Bird
— feature alignment
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Neural Network Optimization
Deep Learning > Architectures > Neural Networks
Machine Learning > Learning Types > Supervised Learning
Computer Vision > Analysis > Object Segmentation
Deep Learning > Learning Types > Representation Learning
Computer Vision > Analysis > Object Classification