9 papers
Train the Agent, Not the Expert: Learning to Harness Heterogeneous Experts for Multi-Turn Visual Reasoning
Yaowu Fan, Tao Han, Dazhao Du +2
Recent progress in computer vision has produced a wide range of powerful specialized models for detection, segmentation, counting, and other visual tasks. However, these models are…
Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods
Yaowu Fan, Jia Wan, Tao Han +3
Counting and tracking dense crowds in large-scale scenes is a highly practical yet challenging problem. Existing methods mostly rely on fixed-camera datasets with limited scene cov…
Dense Point-to-Mask Optimization with Reinforced Point Selection for Crowd Instance Segmentation
Hongru Chen, Jiyang Huang, Jia Wan +1
Crowd instance segmentation is a crucial task with a wide range of applications, including surveillance and transportation. Currently, point labels are common in crowd datasets, wh…
Exclusivity-Guided Mask Learning for Semi-Supervised Crowd Instance Segmentation and Counting
Jiyang Huang, Hongru Chen, Hongru Cheng +3
Semi-supervised crowd analysis is a prominent area of research, as unlabeled data are typically abundant and inexpensive to obtain. However, traditional point-based annotations con…
Embodied Crowd Counting
Runling Long, Yunlong Wang, Jia Wan +5
Occlusion is one of the fundamental challenges in crowd counting. In the community, various data-driven approaches have been developed to address this issue, yet their effectivenes…
Video Individual Counting for Moving Drones
Yaowu Fan, Jia Wan, Tao Han +2
Video Individual Counting (VIC) has received increasing attention for its importance in intelligent video surveillance. Existing works are limited in two aspects, i.e., dataset and…