Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: D3Net: A Speaker-Listener Architecture for Semi-supervised Dense
  Captioning and Visual Grounding in RGB-D Scans   Recent studies on dense captioning and visual grounding in 3D have achieved
impressive results. Despite developments in both areas, the limited amount of
available 3D vision-language data causes overfitting issues for 3D visual
grounding and 3D dense captioning methods. Also, how to discriminatively
describe objects in complex 3D environments is not fully studied yet. To
address these challenges, we present D3Net, an end-to-end neural
speaker-listener architecture that can detect, describe and discriminate. Our
D3Net unifies dense captioning and visual grounding in 3D in a self-critical
manner. This self-critical property of D3Net also introduces discriminability
during object caption generation and enables semi-supervised training on
ScanNet data with partially annotated descriptions. Our method outperforms SOTA
methods in both tasks on the ScanRefer dataset, surpassing the SOTA 3D dense
captioning method by a significant margin (23.56% CiDEr@0.5IoU improvement).

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The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: math
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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th
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The categories of this paper are: cmp-lg