Open Logo Detection Challenge
release_dmg7grv3bzavbgqubdj7dyd424
by
Hang Su, Xiatian Zhu, Shaogang Gong
2018
Abstract
Existing logo detection benchmarks consider artificial deployment scenarios
by assuming that large training data with fine-grained bounding box annotations
for each class are available for model training. Such assumptions are often
invalid in realistic logo detection scenarios where new logo classes come
progressively and require to be detected with little or none budget for
exhaustively labelling fine-grained training data for every new class. Existing
benchmarks are thus unable to evaluate the true performance of a logo detection
method in realistic and open deployments. In this work, we introduce a more
realistic and challenging logo detection setting, called Open Logo Detection.
Specifically, this new setting assumes fine-grained labelling only on a small
proportion of logo classes whilst the remaining classes have no labelled
training data to simulate the open deployment. We further create an open logo
detection benchmark, called OpenLogo,to promote the investigation of this new
challenge. OpenLogo contains 27,083 images from 352 logo classes, built by
aggregating/refining 7 existing datasets and establishing an open logo
detection evaluation protocol. To address this challenge, we propose a Context
Adversarial Learning (CAL) approach to synthesising training data with coherent
logo instance appearance against diverse background context for enabling more
effective optimisation of contemporary deep learning detection models.
Experiments show the performance advantage of CAL over existing
state-of-the-art alternative methods on the more realistic and challenging
OpenLogo benchmark.
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