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20162023
most citedPyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition

139 citations · 1.4k across the 83 of their papers we have counts for

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Showing 2017Show all

7 papers · 1 filter

cs.CV2017★ 7 cited

Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual Cross Retrieval

Yuming Shen, Li Liu, Ling Shao +1

Cross-modal hashing is usually regarded as an effective technique for large-scale textual-visual cross retrieval, where data from different modalities are mapped into a shared Hamm…

cs.CV2017★ 135 cited

Discriminative Block-Diagonal Representation Learning for Image Recognition

Zheng Zhang, Yong Xu, Ling Shao +1

Existing block-diagonal representation researches mainly focuses on casting block-diagonal regularization on training data, while only little attention is dedicated to concurrently…

cs.CV2017

DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI

Yawen Huang, Ling Shao, Alejandro F. Frangi

Cross-modal image synthesis is a topical problem in medical image computing. Existing methods for image synthesis are either tailored to a specific application, require large scale…

cs.CV2017

Dual-reference Face Retrieval

BingZhang Hu, Feng Zheng, Ling Shao

Face retrieval has received much attention over the past few decades, and many efforts have been made in retrieving face images against pose, illumination, and expression variation…

cs.CV2017

Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding

Yawen Huang, Ling Shao, Alejandro F. Frangi

Magnetic Resonance Imaging (MRI) offers high-resolution \emph{in vivo} imaging and rich functional and anatomical multimodality tissue contrast. In practice, however, there are cha…

cs.CV2017

From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis

Yang Long, Li Liu, Ling Shao +3

Robust object recognition systems usually rely on powerful feature extraction mechanisms from a large number of real images. However, in many realistic applications, collecting suf…