1,526 research outputs found
Large Scale Visual Recommendations From Street Fashion Images
We describe a completely automated large scale visual recommendation system
for fashion. Our focus is to efficiently harness the availability of large
quantities of online fashion images and their rich meta-data. Specifically, we
propose four data driven models in the form of Complementary Nearest Neighbor
Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain
LDA for solving this problem. We analyze relative merits and pitfalls of these
algorithms through extensive experimentation on a large-scale data set and
baseline them against existing ideas from color science. We also illustrate key
fashion insights learned through these experiments and show how they can be
employed to design better recommendation systems. Finally, we also outline a
large-scale annotated data set of fashion images (Fashion-136K) that can be
exploited for future vision research
Representation Learning by Learning to Count
We introduce a novel method for representation learning that uses an
artificial supervision signal based on counting visual primitives. This
supervision signal is obtained from an equivariance relation, which does not
require any manual annotation. We relate transformations of images to
transformations of the representations. More specifically, we look for the
representation that satisfies such relation rather than the transformations
that match a given representation. In this paper, we use two image
transformations in the context of counting: scaling and tiling. The first
transformation exploits the fact that the number of visual primitives should be
invariant to scale. The second transformation allows us to equate the total
number of visual primitives in each tile to that in the whole image. These two
transformations are combined in one constraint and used to train a neural
network with a contrastive loss. The proposed task produces representations
that perform on par or exceed the state of the art in transfer learning
benchmarks.Comment: ICCV 2017(oral
A Benchmark for Image Retrieval using Distributed Systems over the Internet: BIRDS-I
The performance of CBIR algorithms is usually measured on an isolated
workstation. In a real-world environment the algorithms would only constitute a
minor component among the many interacting components. The Internet
dramati-cally changes many of the usual assumptions about measuring CBIR
performance. Any CBIR benchmark should be designed from a networked systems
standpoint. These benchmarks typically introduce communication overhead because
the real systems they model are distributed applications. We present our
implementation of a client/server benchmark called BIRDS-I to measure image
retrieval performance over the Internet. It has been designed with the trend
toward the use of small personalized wireless systems in mind. Web-based CBIR
implies the use of heteroge-neous image sets, imposing certain constraints on
how the images are organized and the type of performance metrics applicable.
BIRDS-I only requires controlled human intervention for the compilation of the
image collection and none for the generation of ground truth in the measurement
of retrieval accuracy. Benchmark image collections need to be evolved
incrementally toward the storage of millions of images and that scaleup can
only be achieved through the use of computer-aided compilation. Finally, our
scoring metric introduces a tightly optimized image-ranking window.Comment: 24 pages, To appear in the Proc. SPIE Internet Imaging Conference
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