39,963 research outputs found
What value do explicit high level concepts have in vision to language problems?
Much of the recent progress in Vision-to-Language (V2L) problems has been
achieved through a combination of Convolutional Neural Networks (CNNs) and
Recurrent Neural Networks (RNNs). This approach does not explicitly represent
high-level semantic concepts, but rather seeks to progress directly from image
features to text. We propose here a method of incorporating high-level concepts
into the very successful CNN-RNN approach, and show that it achieves a
significant improvement on the state-of-the-art performance in both image
captioning and visual question answering. We also show that the same mechanism
can be used to introduce external semantic information and that doing so
further improves performance. In doing so we provide an analysis of the value
of high level semantic information in V2L problems.Comment: Accepted to IEEE Conf. Computer Vision and Pattern Recognition 2016.
Fixed titl
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering
Many vision and language tasks require commonsense reasoning beyond
data-driven image and natural language processing. Here we adopt Visual
Question Answering (VQA) as an example task, where a system is expected to
answer a question in natural language about an image. Current state-of-the-art
systems attempted to solve the task using deep neural architectures and
achieved promising performance. However, the resulting systems are generally
opaque and they struggle in understanding questions for which extra knowledge
is required. In this paper, we present an explicit reasoning layer on top of a
set of penultimate neural network based systems. The reasoning layer enables
reasoning and answering questions where additional knowledge is required, and
at the same time provides an interpretable interface to the end users.
Specifically, the reasoning layer adopts a Probabilistic Soft Logic (PSL) based
engine to reason over a basket of inputs: visual relations, the semantic parse
of the question, and background ontological knowledge from word2vec and
ConceptNet. Experimental analysis of the answers and the key evidential
predicates generated on the VQA dataset validate our approach.Comment: 9 pages, 3 figures, AAAI 201
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