Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fine-grained Domain Adaptive Crowd Counting via Point-derived
  Segmentation   Existing domain adaptation methods for crowd counting view each crowd image
as a whole and reduce domain discrepancies on crowds and backgrounds
simultaneously. However, we argue that these methods are suboptimal, as crowds
and backgrounds have quite different characteristics and backgrounds may vary
dramatically in different crowd scenes (see Fig.~\ref{teaser}). This makes
crowds not well aligned across domains together with backgrounds in a holistic
manner. To this end, we propose to untangle crowds and backgrounds from crowd
images and design fine-grained domain adaption methods for crowd counting.
Different from other tasks which possess region-based fine-grained annotations
(e.g., segments or bounding boxes), crowd counting only annotates one point on
each human head, which impedes the implementation of fine-grained adaptation
methods. To tackle this issue, we propose a novel and effective schema to learn
crowd segmentation from point-level crowd counting annotations in the context
of Multiple Instance Learning. We further leverage the derived segments to
propose a crowd-aware fine-grained domain adaptation framework for crowd
counting, which consists of two novel adaptation modules, i.e., Crowd Region
Transfer (CRT) and Crowd Density Alignment (CDA). Specifically, the CRT module
is designed to guide crowd features transfer across domains beyond background
distractions, and the CDA module dedicates to constraining the target-domain
crowd density distributions. Extensive experiments on multiple cross-domain
settings (i.e., Synthetic $\rightarrow$ Real, Fixed $\rightarrow$ Fickle,
Normal $\rightarrow$ BadWeather) demonstrate the superiority of the proposed
method compared with state-of-the-art methods.

27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat