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Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
TLDR
We present a deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF. Expand
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Deep convolutional neural fields for depth estimation from a single image
TLDR
We consider the problem of depth estimation from a single monocular image in this work. Expand
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CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning
TLDR
We present CANet, a class-agnostic segmentation network that performs few-shot segmentation on new classes with only a few annotated images available. Expand
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Pyramid Graph Networks With Connection Attentions for Region-Based One-Shot Semantic Segmentation
TLDR
In this paper, we propose to model structured segmentation data with graphs and apply attentive graph reasoning to propagate label information from support data to query data. Expand
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Multiple Kernel Learning in the Primal for Multimodal Alzheimer’s Disease Classification
TLDR
We propose a novel multiple kernel-learning framework to combine multimodal features for AD classification, which is scalable and easy to implement. Expand
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CRF Learning with CNN Features for Image Segmentation
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A deep CNN is trained on the ImageNet dataset and transferred to image segmentations here for constructing potentials of superpixels. Expand
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Sequence searching with deep-learnt depth for condition- and viewpoint-invariant route-based place recognition
TLDR
In this paper we significantly improve the viewpoint invariance of the SeqSLAM algorithm by using state-of-the-art deep learning techniques to generate synthetic viewpoints. Expand
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RefineNet: Multi-Path Refinement Networks for Dense Prediction
TLDR
We present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. Expand
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CRNet: Cross-Reference Networks for Few-Shot Segmentation
TLDR
In this paper, we propose a cross-reference network (CRNet) for few-shot segmentation. Expand
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Structured Learning of Binary Codes with Column Generation for Optimizing Ranking Measures
TLDR
Hashing methods aim to learn a set of hash functions which map the original features to compact binary codes with similarity preserving in the Hamming space. Expand
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