37 citations · 38 across the 9 of their papers we have counts for
13 papers
Multi-Scale Semantic Mapping in Urban Environments via Observation Calibration and Policy Dependence Regularization
Runling Long, Junhao Feng, Jia Wan
Semantic mapping is fundamental to embodied navigation, yet existing methods are developed for indoor environments, where objects exhibit relatively limited scale variation and are…
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…
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, Wei Lin +2
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…
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 valuable yet challenging, while existing methods and datasets are largely limited to fixed cameras with small scene cove…
Temporal Unlearnable Examples: Preventing Personal Video Data from Unauthorized Exploitation by Object Tracking
Qiangqiang Wu, Yi Yu, Chenqi Kong +5
With the rise of social media, vast amounts of user-uploaded videos (e.g., YouTube) are utilized as training data for Visual Object Tracking (VOT). However, the VOT community has l…
Density-based Object Detection in Crowded Scenes
Chenyang Zhao, Jia Wan, Antoni B. Chan
Compared with the generic scenes, crowded scenes contain highly-overlapped instances, which result in: 1) more ambiguous anchors during training of object detectors, and 2) more pr…
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…
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…
A New Method to Capturing Compositional Knowledge in Linguistic Space
Jiahe Wan
Compositional understanding allows visual language models to interpret complex relationships between objects, attributes, and relations in images and text. However, most existing m…
Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAM
Jia Wan, Qiangqiang Wu, Wei Lin +1
The existing crowd counting models require extensive training data, which is time-consuming to annotate. To tackle this issue, we propose a simple yet effective crowd counting meth…
Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization
Ziquan Liu, Yufei Cui, Jia Wan +2
Weight decay is often used to ensure good generalization in the training practice of deep neural networks with batch normalization (BN-DNNs), where some convolution layers are inva…
Fine-Grained Crowd Counting
Jia Wan, Nikil Senthil Kumar, Antoni B. Chan
Current crowd counting algorithms are only concerned about the number of people in an image, which lacks low-level fine-grained information of the crowd. For many practical applica…