  This technical report details several improvements to the visual concept
detector banks built on images from the Multilingual Visual Sentiment Ontology
(MVSO). The detector banks are trained to detect a total of 9,918
sentiment-biased visual concepts from six major languages: English, Spanish,
Italian, French, German and Chinese. In the original MVSO release,
adjective-noun pair (ANP) detectors were trained for the six languages using an
AlexNet-styled architecture by fine-tuning from DeepSentiBank. Here, through a
more extensive set of experiments, parameter tuning, and training runs, we
detail and release higher accuracy models for detecting ANPs across six
languages from the same image pool and setting as in the original release using
a more modern architecture, GoogLeNet, providing comparable or better
performance with reduced network parameter cost.
  In addition, since the image pool in MVSO can be corrupted by user noise from
social interactions, we partitioned out a sub-corpus of MVSO images based on
tag-restricted queries for higher fidelity labels. We show that as a result of
these higher fidelity labels, higher performing AlexNet-styled ANP detectors
can be trained using the tag-restricted image subset as compared to the models
in full corpus. We release all these newly trained models for public research
use along with the list of tag-restricted images from the MVSO dataset.
