Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Intention-aware Feature Propagation Network for Interactive Segmentation   We aim to tackle the problem of point-based interactive segmentation, in
which two key challenges are to infer user's intention correctly and to
propagate the user-provided annotations to unlabeled regions efficiently. To
address those challenges, we propose a novel intention-aware feature
propagation strategy that performs explicit user intention estimation and
learns an efficient click-augmented feature representation for high-resolution
foreground segmentation. Specifically, we develop a coarse-to-fine sparse
propagation network for each interactive segmentation step, which consists of a
coarse-level network for more effective tracking of user's interest, and a
fine-level network for zooming to the target object and performing fine-level
segmentation. Moreover, we design a new sparse graph network module for both
levels to enable efficient long-range propagation of click information.
Extensive experiments show that our method surpasses the previous
state-of-the-art methods on all popular benchmarks, demonstrating its efficacy.

18
The categories of this paper are: econ
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc