2 research outputs found
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization
We develop hierarchically quantized efficient embedding representations for
similarity-based search and show that this representation provides not only the
state of the art performance on the search accuracy but also provides several
orders of speed up during inference. The idea is to hierarchically quantize the
representation so that the quantization granularity is greatly increased while
maintaining the accuracy and keeping the computational complexity low. We also
show that the problem of finding the optimal sparse compound hash code
respecting the hierarchical structure can be optimized in polynomial time via
minimum cost flow in an equivalent flow network. This allows us to train the
method end-to-end in a mini-batch stochastic gradient descent setting. Our
experiments on Cifar100 and ImageNet datasets show the state of the art search
accuracy while providing several orders of magnitude search speedup
respectively over exhaustive linear search over the dataset.Comment: Accepted and to appear at CVPR 201
flexgrid2vec: Learning Efficient Visual Representations Vectors
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 multi-channel GCN that learns features of the most
representative image patches. We have investigated both spectral and
non-spectral implementations of the GCN node-embedding. Specifically, we have
implemented flexgrid2vec based on different node-aggregation 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.Comment: 13 page