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20172019
most citedRobust Spatial Filtering with Graph Convolutional Neural Networks

150 citations · 211 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CV2019★ 53 cited

Interlaced Sparse Self-Attention for Semantic Segmentation

Lang Huang, Yuhui Yuan, Jianyuan Guo +3

In this paper, we present a so-called interlaced sparse self-attention approach to improve the efficiency of the \emph{self-attention} mechanism for semantic segmentation. The main…

cs.CV2019★ 1 cited

Multi-scale Template Matching with Scalable Diversity Similarity in an Unconstrained Environment

Yi Zhang, Chao Zhang, Takuya Akashi

We propose a novel multi-scale template matching method which is robust against both scaling and rotation in unconstrained environments. The key component behind is a similarity me…

cs.CV2019

A Probabilistic Bitwise Genetic Algorithm for B-Spline based Image Deformation Estimation

Takumi Nakane, Takuya Akashi, Xuequan Lu +1

We propose a novel genetic algorithm to solve the image deformation estimation problem by preserving the genetic diversity. As a classical problem, there is always a trade-off betw…

cs.CV2019★ 2 cited

Attribute-Aware Attention Model for Fine-grained Representation Learning

Kai Han, Jianyuan Guo, Chao Zhang +1

How to learn a discriminative fine-grained representation is a key point in many computer vision applications, such as person re-identification, fine-grained classification, fine-g…

cs.CV2018★ 2 cited

Cross-domain Deep Feature Combination for Bird Species Classification with Audio-visual Data

Bold Naranchimeg, Chao Zhang, Takuya Akashi

In recent decade, many state-of-the-art algorithms on image classification as well as audio classification have achieved noticeable successes with the development of deep convoluti…

cs.CV2017★ 150 cited

Robust Spatial Filtering with Graph Convolutional Neural Networks

Felipe Petroski Such, Shagan Sah, Miguel Dominguez +5

Convolutional Neural Networks (CNNs) have recently led to incredible breakthroughs on a variety of pattern recognition problems. Banks of finite impulse response filters are learne…