7,887 research outputs found
Visually grounded learning of keyword prediction from untranscribed speech
During language acquisition, infants have the benefit of visual cues to
ground spoken language. Robots similarly have access to audio and visual
sensors. Recent work has shown that images and spoken captions can be mapped
into a meaningful common space, allowing images to be retrieved using speech
and vice versa. In this setting of images paired with untranscribed spoken
captions, we consider whether computer vision systems can be used to obtain
textual labels for the speech. Concretely, we use an image-to-words multi-label
visual classifier to tag images with soft textual labels, and then train a
neural network to map from the speech to these soft targets. We show that the
resulting speech system is able to predict which words occur in an
utterance---acting as a spoken bag-of-words classifier---without seeing any
parallel speech and text. We find that the model often confuses semantically
related words, e.g. "man" and "person", making it even more effective as a
semantic keyword spotter.Comment: 5 pages, 3 figures, 5 tables; small updates, added link to code;
accepted to Interspeech 201
Visual Semantic Re-ranker for Text Spotting
Many current state-of-the-art methods for text recognition are based on
purely local information and ignore the semantic correlation between text and
its surrounding visual context. In this paper, we propose a post-processing
approach to improve the accuracy of text spotting by using the semantic
relation between the text and the scene. We initially rely on an off-the-shelf
deep neural network that provides a series of text hypotheses for each input
image. These text hypotheses are then re-ranked using the semantic relatedness
with the object in the image. As a result of this combination, the performance
of the original network is boosted with a very low computational cost. The
proposed framework can be used as a drop-in complement for any text-spotting
algorithm that outputs a ranking of word hypotheses. We validate our approach
on ICDAR'17 shared task dataset
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