arXiv subjects

Xiaoying Chen

Publications and source records attributed to Xiaoying Chen.

2 recordsLinked to original sources

arXiv2026

On the 2-Linkage Problem for Split Digraphs

A digraph is {\bf $ k $-linked} if for arbitary two disjoint vertex sets $\{s_1, \ldots, s_k\}$ and $\{t_1, \ldots, t_k\}$, there exist vertex-disjoint directed paths $P_1, \ldots, P_k$ {such that $P_i$ is a directed path from $s_i$ to $t_i$ for each $i\in [k]$}. A {\bf split digraph} is a digraph $ D = (V_1, V_2; A) $ whose vertex set is a disjoint union of two nonempty sets $ V_1 $ and $ V_2 $ such that $ V_1 $ is an independent set and the subdigraph induced by $ V_2 $ is semicomplete (no pair of non-adjacent vertices). A {\bf semicomplete split digraph} is a split digraph $ D = (V_1, V_2; A) $ in which every vertex in the independent set $ V_1 $ is adjacent to every vertex in $ V_2 $. {Semicomplete split digraphs form an important subclass of the class of semicomplete multipartite digraphs.} In this paper, we prove that every 6-strong split digraph is 2-linked. This solves a problem posed by Bang-Jensen and Wang [J. Graph Theory, 2025]. We also show that every 5-strong semicomplete split digraph is 2-linked. This bound is tight already for semicomplete digraphs.

math.CO↗

arXiv2024

TFCounter:Polishing Gems for Training-Free Object Counting

Object counting is a challenging task with broad application prospects in security surveillance, traffic management, and disease diagnosis. Existing object counting methods face a tri-fold challenge: achieving superior performance, maintaining high generalizability, and minimizing annotation costs. We develop a novel training-free class-agnostic object counter, TFCounter, which is prompt-context-aware via the cascade of the essential elements in large-scale foundation models. This approach employs an iterative counting framework with a dual prompt system to recognize a broader spectrum of objects varying in shape, appearance, and size. Besides, it introduces an innovative context-aware similarity module incorporating background context to enhance accuracy within messy scenes. To demonstrate cross-domain generalizability, we collect a novel counting dataset named BIKE-1000, including exclusive 1000 images of shared bicycles from Meituan. Extensive experiments on FSC-147, CARPK, and BIKE-1000 datasets demonstrate that TFCounter outperforms existing leading training-free methods and exhibits competitive results compared to trained counterparts.

cs.CV↗