1,299 research outputs found
Adaptive Representations for Tracking Breaking News on Twitter
Twitter is often the most up-to-date source for finding and tracking breaking
news stories. Therefore, there is considerable interest in developing filters
for tweet streams in order to track and summarize stories. This is a
non-trivial text analytics task as tweets are short, and standard retrieval
methods often fail as stories evolve over time. In this paper we examine the
effectiveness of adaptive mechanisms for tracking and summarizing breaking news
stories. We evaluate the effectiveness of these mechanisms on a number of
recent news events for which manually curated timelines are available.
Assessments based on ROUGE metrics indicate that an adaptive approaches are
best suited for tracking evolving stories on Twitter.Comment: 8 Pag
Video Stream Retrieval of Unseen Queries using Semantic Memory
Retrieval of live, user-broadcast video streams is an under-addressed and
increasingly relevant challenge. The on-line nature of the problem requires
temporal evaluation and the unforeseeable scope of potential queries motivates
an approach which can accommodate arbitrary search queries. To account for the
breadth of possible queries, we adopt a no-example approach to query retrieval,
which uses a query's semantic relatedness to pre-trained concept classifiers.
To adapt to shifting video content, we propose memory pooling and memory
welling methods that favor recent information over long past content. We
identify two stream retrieval tasks, instantaneous retrieval at any particular
time and continuous retrieval over a prolonged duration, and propose means for
evaluating them. Three large scale video datasets are adapted to the challenge
of stream retrieval. We report results for our search methods on the new stream
retrieval tasks, as well as demonstrate their efficacy in a traditional,
non-streaming video task.Comment: Presented at BMVC 2016, British Machine Vision Conference, 201
Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks
Financial news contains useful information on public companies and the
market. In this paper we apply the popular word embedding methods and deep
neural networks to leverage financial news to predict stock price movements in
the market. Experimental results have shown that our proposed methods are
simple but very effective, which can significantly improve the stock prediction
accuracy on a standard financial database over the baseline system using only
the historical price information.Comment: 5 pages, 2 figures, technical repor
Learning Graph Embeddings for Compositional Zero-shot Learning
In compositional zero-shot learning, the goal is to recognize unseen
compositions (e.g. old dog) of observed visual primitives states (e.g. old,
cute) and objects (e.g. car, dog) in the training set. This is challenging
because the same state can for example alter the visual appearance of a dog
drastically differently from a car. As a solution, we propose a novel graph
formulation called Compositional Graph Embedding (CGE) that learns image
features, compositional classifiers, and latent representations of visual
primitives in an end-to-end manner. The key to our approach is exploiting the
dependency between states, objects, and their compositions within a graph
structure to enforce the relevant knowledge transfer from seen to unseen
compositions. By learning a joint compatibility that encodes semantics between
concepts, our model allows for generalization to unseen compositions without
relying on an external knowledge base like WordNet. We show that in the
challenging generalized compositional zero-shot setting our CGE significantly
outperforms the state of the art on MIT-States and UT-Zappos. We also propose a
new benchmark for this task based on the recent GQA dataset.Comment: Accepted in IEEE CVPR 202
Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions
We aim for zero-shot localization and classification of human actions in
video. Where traditional approaches rely on global attribute or object
classification scores for their zero-shot knowledge transfer, our main
contribution is a spatial-aware object embedding. To arrive at spatial
awareness, we build our embedding on top of freely available actor and object
detectors. Relevance of objects is determined in a word embedding space and
further enforced with estimated spatial preferences. Besides local object
awareness, we also embed global object awareness into our embedding to maximize
actor and object interaction. Finally, we exploit the object positions and
sizes in the spatial-aware embedding to demonstrate a new spatio-temporal
action retrieval scenario with composite queries. Action localization and
classification experiments on four contemporary action video datasets support
our proposal. Apart from state-of-the-art results in the zero-shot localization
and classification settings, our spatial-aware embedding is even competitive
with recent supervised action localization alternatives.Comment: ICC
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