5,730 research outputs found
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
Interpreting CNN Knowledge via an Explanatory Graph
This paper learns a graphical model, namely an explanatory graph, which
reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering
that each filter in a conv-layer of a pre-trained CNN usually represents a
mixture of object parts, we propose a simple yet efficient method to
automatically disentangles different part patterns from each filter, and
construct an explanatory graph. In the explanatory graph, each node represents
a part pattern, and each edge encodes co-activation relationships and spatial
relationships between patterns. More importantly, we learn the explanatory
graph for a pre-trained CNN in an unsupervised manner, i.e., without a need of
annotating object parts. Experiments show that each graph node consistently
represents the same object part through different images. We transfer part
patterns in the explanatory graph to the task of part localization, and our
method significantly outperforms other approaches.Comment: in AAAI 201
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
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