Single Domain Generalization for Crowd Counting

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


MPhil Thesis Defence


Title: "Single Domain Generalization for Crowd Counting"

By

Mr. Zhuoxuan PENG


Abstract:

Due to its promising results, density map regression has been widely employed 
for image-based crowd counting. The approach, however, often suffers from 
severe performance degradation when tested on data from unseen scenarios, the 
so-called "domain shift" problem. To address the problem, we investigate in 
this work single domain generalization (SDG) for crowd counting. The existing 
SDG approaches are mainly for image classification and segmentation, and can 
hardly be extended to our case due to its regression nature and label ambiguity 
(i.e., ambiguous pixel-level ground truths). We propose MPCount, a novel 
effective SDG approach even for narrow source distribution. MPCount stores 
diverse density values for density map regression and reconstructs 
domain-invariant features by means of only one memory bank, a content error 
mask and attention consistency loss. By partitioning the image into grids, it 
employs patch-wise classification as an auxiliary task to mitigate label 
ambiguity. Through extensive experiments on different datasets, MPCount is 
shown to significantly improve counting accuracy compared to the state of the 
art under diverse scenarios unobserved in the training data characterized by 
narrow source distribution.


Date:                   Friday, 28 June 2024

Time:                   10:00am - 12:00noon

Venue:                  Room 3494
                        Lifts 25/26

Chairman:               Dr. Dan XU

Committee Members:      Prof. Gary CHAN (Supervisor)
                        Prof. Raymond WONG