7,317 research outputs found
Learning to Hash-tag Videos with Tag2Vec
User-given tags or labels are valuable resources for semantic understanding
of visual media such as images and videos. Recently, a new type of labeling
mechanism known as hash-tags have become increasingly popular on social media
sites. In this paper, we study the problem of generating relevant and useful
hash-tags for short video clips. Traditional data-driven approaches for tag
enrichment and recommendation use direct visual similarity for label transfer
and propagation. We attempt to learn a direct low-cost mapping from video to
hash-tags using a two step training process. We first employ a natural language
processing (NLP) technique, skip-gram models with neural network training to
learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a
corpus of 10 million hash-tags. We then train an embedding function to map
video features to the low-dimensional Tag2vec space. We learn this embedding
for 29 categories of short video clips with hash-tags. A query video without
any tag-information can then be directly mapped to the vector space of tags
using the learned embedding and relevant tags can be found by performing a
simple nearest-neighbor retrieval in the Tag2Vec space. We validate the
relevance of the tags suggested by our system qualitatively and quantitatively
with a user study
Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings
In this paper we present a novel interactive multimodal learning system,
which facilitates search and exploration in large networks of social multimedia
users. It allows the analyst to identify and select users of interest, and to
find similar users in an interactive learning setting. Our approach is based on
novel multimodal representations of users, words and concepts, which we
simultaneously learn by deploying a general-purpose neural embedding model. We
show these representations to be useful not only for categorizing users, but
also for automatically generating user and community profiles. Inspired by
traditional summarization approaches, we create the profiles by selecting
diverse and representative content from all available modalities, i.e. the
text, image and user modality. The usefulness of the approach is evaluated
using artificial actors, which simulate user behavior in a relevance feedback
scenario. Multiple experiments were conducted in order to evaluate the quality
of our multimodal representations, to compare different embedding strategies,
and to determine the importance of different modalities. We demonstrate the
capabilities of the proposed approach on two different multimedia collections
originating from the violent online extremism forum Stormfront and the
microblogging platform Twitter, which are particularly interesting due to the
high semantic level of the discussions they feature
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