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CNN-aware Binary Map for General Semantic Segmentation
In this paper we introduce a novel method for general semantic segmentat...
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Using PSPNet and UNet to analyze the internal parameter relationship and visualization of the convolutional neural network
Convolutional neural network(CNN) has achieved great success in many fie...
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Superpixel Segmentation via Convolutional Neural Networks with Regularized Information Maximization
We propose an unsupervised superpixel segmentation method by optimizing ...
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Deep Feature Factorization For Concept Discovery
We propose Deep Feature Factorization (DFF), a method capable of localiz...
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Multimodal Semantic Transfer from Text to Image. Fine-Grained Image Classification by Distributional Semantics
In the last years, image classification processes like neural networks i...
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Convolutional Feature Masking for Joint Object and Stuff Segmentation
The topic of semantic segmentation has witnessed considerable progress d...
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Segmentation of histological images and fibrosis identification with a convolutional neural network
Segmentation of histological images is one of the most crucial tasks for...
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Efficient Convolutional Neural Network with Binary Quantization Layer
In this paper we introduce a novel method for segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN. All the previous papers were limited to few number of category of the images (e.g. PASCAL VOC). Experiments show that our segmentation algorithm outperform the state-of-the-art non-semantic segmentation methods by a large margin.
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