Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-Modal Progressive Comprehension for Referring Segmentation   Given a natural language expression and an image/video, the goal of referring
segmentation is to produce the pixel-level masks of the entities described by
the subject of the expression. Previous approaches tackle this problem by
implicit feature interaction and fusion between visual and linguistic
modalities in a one-stage manner. However, human tends to solve the referring
problem in a progressive manner based on informative words in the expression,
i.e., first roughly locating candidate entities and then distinguishing the
target one. In this paper, we propose a Cross-Modal Progressive Comprehension
(CMPC) scheme to effectively mimic human behaviors and implement it as a CMPC-I
(Image) module and a CMPC-V (Video) module to improve referring image and video
segmentation models. For image data, our CMPC-I module first employs entity and
attribute words to perceive all the related entities that might be considered
by the expression. Then, the relational words are adopted to highlight the
target entity as well as suppress other irrelevant ones by spatial graph
reasoning. For video data, our CMPC-V module further exploits action words
based on CMPC-I to highlight the correct entity matched with the action cues by
temporal graph reasoning. In addition to the CMPC, we also introduce a simple
yet effective Text-Guided Feature Exchange (TGFE) module to integrate the
reasoned multimodal features corresponding to different levels in the visual
backbone under the guidance of textual information. In this way, multi-level
features can communicate with each other and be mutually refined based on the
textual context. Combining CMPC-I or CMPC-V with TGFE can form our image or
video version referring segmentation frameworks and our frameworks achieve new
state-of-the-art performances on four referring image segmentation benchmarks
and three referring video segmentation benchmarks respectively.

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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph
3
The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: stat
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The categories of this paper are: hep-lat
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ph