2 research outputs found
FathomNet: An underwater image training database for ocean exploration and discovery
Thousands of hours of marine video data are collected annually from remotely
operated vehicles (ROVs) and other underwater assets. However, current manual
methods of analysis impede the full utilization of collected data for real time
algorithms for ROV and large biodiversity analyses. FathomNet is a novel
baseline image training set, optimized to accelerate development of modern,
intelligent, and automated analysis of underwater imagery. Our seed data set
consists of an expertly annotated and continuously maintained database with
more than 26,000 hours of videotape, 6.8 million annotations, and 4,349 terms
in the knowledge base. FathomNet leverages this data set by providing imagery,
localizations, and class labels of underwater concepts in order to enable
machine learning algorithm development. To date, there are more than 80,000
images and 106,000 localizations for 233 different classes, including midwater
and benthic organisms. Our experiments consisted of training various deep
learning algorithms with approaches to address weakly supervised localization,
image labeling, object detection and classification which prove to be
promising. While we find quality results on prediction for this new dataset,
our results indicate that we are ultimately in need of a larger data set for
ocean exploration.Comment: 8 pages, 6 figure
Unsupervised Multi-label Dataset Generation from Web Data
This paper presents a system towards the generation of multi-label datasets
from web data in an unsupervised manner. To achieve this objective, this work
comprises two main contributions, namely: a) the generation of a low-noise
unsupervised single-label dataset from web-data, and b) the augmentation of
labels in such dataset (from single label to multi label). The generation of a
single-label dataset uses an unsupervised noise reduction phase (clustering and
selection of clusters using anchors) obtaining a 85% of correctly labeled
images. An unsupervised label augmentation process is then performed to assign
new labels to the images in the dataset using the class activation maps and the
uncertainty associated with each class. This process is applied to the dataset
generated in this paper and a public dataset (Places365) achieving a 9.5% and
27% of extra labels in each dataset respectively, therefore demonstrating that
the presented system can robustly enrich the initial dataset.Comment: The 3rd Workshop on Visual Understanding by Learning from Web Data
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