2 research outputs found
Extracting textual overlays from social media videos using neural networks
Textual overlays are often used in social media videos as people who watch
them without the sound would otherwise miss essential information conveyed in
the audio stream. This is why extraction of those overlays can serve as an
important meta-data source, e.g. for content classification or retrieval tasks.
In this work, we present a robust method for extracting textual overlays from
videos that builds up on multiple neural network architectures. The proposed
solution relies on several processing steps: keyframe extraction, text
detection and text recognition. The main component of our system, i.e. the text
recognition module, is inspired by a convolutional recurrent neural network
architecture and we improve its performance using synthetically generated
dataset of over 600,000 images with text prepared by authors specifically for
this task. We also develop a filtering method that reduces the amount of
overlapping text phrases using Levenshtein distance and further boosts system's
performance. The final accuracy of our solution reaches over 80A% and is au
pair with state-of-the-art methods.Comment: International Conference on Computer Vision and Graphics (ICCVG) 201
The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements
Truly real-life data presents a strong, but exciting challenge for sentiment
and emotion research. The high variety of possible `in-the-wild' properties
makes large datasets such as these indispensable with respect to building
robust machine learning models. A sufficient quantity of data covering a deep
variety in the challenges of each modality to force the exploratory analysis of
the interplay of all modalities has not yet been made available in this
context. In this contribution, we present MuSe-CaR, a first of its kind
multimodal dataset. The data is publicly available as it recently served as the
testing bed for the 1st Multimodal Sentiment Analysis Challenge, and focused on
the tasks of emotion, emotion-target engagement, and trustworthiness
recognition by means of comprehensively integrating the audio-visual and
language modalities. Furthermore, we give a thorough overview of the dataset in
terms of collection and annotation, including annotation tiers not used in this
year's MuSe 2020. In addition, for one of the sub-challenges - predicting the
level of trustworthiness - no participant outperformed the baseline model, and
so we propose a simple, but highly efficient Multi-Head-Attention network that
exceeds using multimodal fusion the baseline by around 0.2 CCC (almost 50 %
improvement).Comment: accepted versio