We propose bilinear models, a recognition architecture that consists of two feature extractors
whose outputs are multiplied using outer product at each location of the image and pooled …

Evidence is mounting that Convolutional Networks (ConvNets) are the most effective
representation learning method for visual recognition tasks. In the common scenario, a …

Fine-grained categorization, which aims to distinguish subordinate-level categories such as
bird species or dog breeds, is an extremely challenging task. This is due to two main issues …

Pose variation and subtle differences in appearance are key challenges to fine-grained
classification. While deep networks have markedly improved general recognition, many …

Fine-grained visual categorization is an emerging research area and has been attracting
growing attention recently. Due to the large inter-class similarity and intra-class variance, it is …

We present a simple and effective architecture for fine-grained visual recognition called
Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a …

Procedural modelling has been successfully applied to the automatic building generation
problem. Among several techniques proposed to tackle the problem of procedural building …

In contrast to basic-level object recognition, fine-grained categorization aims to distinguish
between subordinate categories, such as different animal breeds or species, plant species …

あらまし cvpaper. challenge は産総研・東京電機大学・筑波大学のメンバーで構成される合同
グループであり, コンピュータビジョンやパターン認識分野の論文を体系的にまとめることを目的として …