grid2vec: Learning Efficient Visual Representations via Flexible Grid-Graphs

Publication Year: 2020 Publication Type : JournalArticle


We propose flexgrid2vec, a novel approach for image representation learning. Existing visual representation methods suffer from several issues, including the need for highly intensive computation, the risk of losing in-depth structural information and the specificity of the method to certain shapes or objects. flexgrid2vec converts an image to a low-dimensional feature vector. We represent each image with a graph of flexible, unique node locations and edge distances. flexgrid2vec is a multichannel GCN that learns features of the most representative image patches. We have investigated both spectral and nonspectral implementations of the GCN node-embedding. Specifically, we have implemented flexgrid2vec based on different nodeaggregation methods, such as vector summation, concatenation and normalisation with eigenvector centrality. We compare the performance of flexgrid2vec with a set of state-of-the-art visual representation learning models on binary and multi-class image classification tasks. Although we utilise imbalanced, low-size and low-resolution datasets, flexgrid2vec shows stable and outstanding results against well-known base classifiers. flexgrid2vec achieves 96:23% on CIFAR-10, 83:05% on CIFAR-100, 94:50% on STL-10, 98:8% on ASIRRA and 89:69% on the COCO dataset.


@article{DBLP:journals/corr/abs-2007-15444, archiveprefix = {arXiv},
    author = {Ali Hamdi and Du Yong Kim and Flora D. Salim},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    eprint = {2007.15444},
    journal = {CoRR},
    timestamp = {Mon, 03 Aug 2020 01:00:00 +0200},
    title = {grid2vec: Learning Efficient Visual Representations via Flexible Grid-Graphs},
    url = {},
    volume = {abs/2007.15444},
    year = {2020}


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