Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Object Detection from Captions via Textual Scene Attributes   Object detection is a fundamental task in computer vision, requiring large
annotated datasets that are difficult to collect, as annotators need to label
objects and their bounding boxes. Thus, it is a significant challenge to use
cheaper forms of supervision effectively. Recent work has begun to explore
image captions as a source for weak supervision, but to date, in the context of
object detection, captions have only been used to infer the categories of the
objects in the image. In this work, we argue that captions contain much richer
information about the image, including attributes of objects and their
relations. Namely, the text represents a scene of the image, as described
recently in the literature. We present a method that uses the attributes in
this "textual scene graph" to train object detectors. We empirically
demonstrate that the resulting model achieves state-of-the-art results on
several challenging object detection datasets, outperforming recent approaches.

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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
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The categories of this paper are: alg-geom
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The categories of this paper are: stat
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The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: physics
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: chem-ph