Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scene Graph Generation with Geometric Context   Scene Graph Generation has gained much attention in computer vision research
with the growing demand in image understanding projects like visual question
answering, image captioning, self-driving cars, crowd behavior analysis,
activity recognition, and more. Scene graph, a visually grounded graphical
structure of an image, immensely helps to simplify the image understanding
tasks. In this work, we introduced a post-processing algorithm called Geometric
Context to understand the visual scenes better geometrically. We use this
post-processing algorithm to add and refine the geometric relationships between
object pairs to a prior model. We exploit this context by calculating the
direction and distance between object pairs. We use Knowledge Embedded Routing
Network (KERN) as our baseline model, extend the work with our algorithm, and
show comparable results on the recent state-of-the-art algorithms.

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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: chem-ph
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The categories of this paper are: supr-con
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: q-alg
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The categories of this paper are: ao-sci