32 citations · 40 across the 5 of their papers we have counts for
6 papers
CLIP-Guided Source-Free Object Detection in Aerial Images
Nanqing Liu, Xun Xu, Yongyi Su +3
Domain adaptation is crucial in aerial imagery, as the visual representation of these images can significantly vary based on factors such as geographic location, time, and weather…
Point-Query Quadtree for Crowd Counting, Localization, and More
Chengxin Liu, Hao Lu, Zhiguo Cao +1
We show that crowd counting can be viewed as a decomposable point querying process. This formulation enables arbitrary points as input and jointly reasons whether the points are cr…
Robust Object Detection With Inaccurate Bounding Boxes
Chengxin Liu, Kewei Wang, Hao Lu +2
Learning accurate object detectors often requires large-scale training data with precise object bounding boxes. However, labeling such data is expensive and time-consuming. As the…
Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting
Min Shi, Hao Lu, Chen Feng +2
Class-agnostic counting (CAC) aims to count all instances in a query image given few exemplars. A standard pipeline is to extract visual features from exemplars and match them with…
From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting
Haipeng Xiong, Hao Lu, Chengxin Liu +3
Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in [0, inf) in th…
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer
Haipeng Xiong, Hao Lu, Chengxin Liu +3
Visual counting, a task that predicts the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in in theor…