4,138 research outputs found
Non-parametric spatially constrained local prior for scene parsing on real-world data
Scene parsing aims to recognize the object category of every pixel in scene
images, and it plays a central role in image content understanding and computer
vision applications. However, accurate scene parsing from unconstrained
real-world data is still a challenging task. In this paper, we present the
non-parametric Spatially Constrained Local Prior (SCLP) for scene parsing on
realistic data. For a given query image, the non-parametric SCLP is learnt by
first retrieving a subset of most similar training images to the query image
and then collecting prior information about object co-occurrence statistics
between spatial image blocks and between adjacent superpixels from the
retrieved subset. The SCLP is powerful in capturing both long- and short-range
context about inter-object correlations in the query image and can be
effectively integrated with traditional visual features to refine the
classification results. Our experiments on the SIFT Flow and PASCAL-Context
benchmark datasets show that the non-parametric SCLP used in conjunction with
superpixel-level visual features achieves one of the top performance compared
with state-of-the-art approaches.Comment: 10 pages, journa
A Diagram Is Worth A Dozen Images
Diagrams are common tools for representing complex concepts, relationships
and events, often when it would be difficult to portray the same information
with natural images. Understanding natural images has been extensively studied
in computer vision, while diagram understanding has received little attention.
In this paper, we study the problem of diagram interpretation and reasoning,
the challenging task of identifying the structure of a diagram and the
semantics of its constituents and their relationships. We introduce Diagram
Parse Graphs (DPG) as our representation to model the structure of diagrams. We
define syntactic parsing of diagrams as learning to infer DPGs for diagrams and
study semantic interpretation and reasoning of diagrams in the context of
diagram question answering. We devise an LSTM-based method for syntactic
parsing of diagrams and introduce a DPG-based attention model for diagram
question answering. We compile a new dataset of diagrams with exhaustive
annotations of constituents and relationships for over 5,000 diagrams and
15,000 questions and answers. Our results show the significance of our models
for syntactic parsing and question answering in diagrams using DPGs
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