Welcome to the Video, Image, and Sound Analysis Lab (VISAL) at the City University of Hong Kong! The lab is directed by Prof. Antoni Chan in the Department of Computer Science.

Our main research activities include:

  • Computer Vision, Surveillance
  • Machine Learning, Pattern Recognition
  • Computer Audition, Music Information Retrieval
  • Eye Gaze Analysis

For more information about our current research, please visit the projects and publication pages.

Opportunities for graduate students and research assistants – if you are interested in joining the lab, please check this information.

Latest News [more]

  • [Mar 30, 2022]

    Our project “Automatic Wide-area Crowd Surveillance Using Multiple Cameras” received the Silver Medal at Inventions Geneva Evaluation Days (IGED) 2022!

  • [Aug 16, 2021]

    Congratulations Qingzhong for defending his thesis!

  • [Aug 12, 2021]

    Congratulations to Jia for defending his thesis!

  • [Jul 1, 2021]

    Dr. Chan was promoted to Professor!

Recent Publications [more]

  • Improved Fine-Tuning by Better Leveraging Pre-Training Data.
    Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Xiangyang Ji, Antoni B. Chan, and Rong Jin,
    In: Neural Information Processing Systems (NeurIPS), To appear 2022.
  • Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization.
    Ziquan Liu and Antoni B. Chan,
    In: British Machine Vision Conference, to appear 2022.
  • Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting.
    Wei Lin, Kunlin Yang, Xinzhu Ma, Junyu Gao, Lingbo Liu, Shinan Liu, Jun Hou, Shuai Yi, and Antoni B. Chan,
    In: British Machine Vision Conference, to appear 2022.
  • Calibration-free Multi-view Crowd Counting.
    Qi Zhang and Antoni B. Chan,
    In: European Conference on Computer Vision (ECCV), Tel Aviv, to appear Oct 2022.
  • 3D Crowd Counting via Geometric Attention-guided Multi-View Fusion.
    Qi Zhang and Antoni Bert Chan,
    International Journal of Computer Vision (IJCV), to appear 2022.
  • RegGeoNet: Learning Regular Representations for Large-Scale 3D Point Clouds.
    Qijian Zhang, Junhui Hou, Yue Qian, Antoni B. Chan, Juyong Zhang, and Ying He,
    International Journal of Computer Vision (IJCV), to appear 2022.
  • On Becoming Socially Anxious: Toddlers’ Attention Bias to Fearful Faces.
    Lamei Wang, Janet H. Hsiao, Antoni B. Chan, Jasmine Cheung, San Hung, and Terry Kit-fong Au,
    Developmental Psychology, to appear 2022.
  • Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models.
    Janet H. Hsiao, Jeehye An, Veronica Kit Sum Hui, Yueyuan Zheng, and Antoni B. Chan,
    npj Science of Learning, to appear 2022.
  • Asymptotic Optimality for Active Learning Processes.
    Xueying Zhan, Yaowei Wang, and Antoni B. Chan,
    In: Uncertainty in Artificial Intelligence (UAI), to appear Aug 2022.
  • Bits-Ensemble: Towards Light-Weight Robust Deep Ensemble by Bits-Sharing.
    Yufei Cui, Shangyu Wu, Qiao Li, Antoni B. Chan, Tei-Wei Kuo, and Jason Xue Chun,
    IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) (accepted to CASES 2022), to appear 2022.

Recent Project Pages [more]

Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting

We propose a novel Crowd Counting framework built upon an external Momentum Template, termed C2MoT, which enables the encoding of domain specific information via an external template representation.

Group-based Distinctive Image Captioning with Memory Attention

We improve the distinctiveness of image captions using a Group-based Distinctive Captioning Model (GdisCap), which compares each image with other images in one similar group and highlights the uniqueness of each image.

Hierarchical Learning of Hidden Markov Models with Clustering Regularization

We propose a novel tree structure variational Bayesian method to learn the individual model and group model simultaneously by treating the group models as the parents of individual models, so that the individual model is learned from observations and regularized by its parents, and conversely, the parent model will be optimized to best represent its children.

Chinese White Dolphin Detection in the Wild

To reduce the human experts’ workload and improve the observation
accuracy, in this paper, we develop a practical system to detect Chinese White Dolphins in the wild automatically.

Eye Movement analysis with Hidden Markov Models (EMHMM) with co-clustering

We analyze eye movement data on stimuli with different feature layouts. Through co-clustering HMMs, we discover common strategies on each stimuli and cluster subjects with similar strategies.

Recent Datasets and Code [more]

Dolphin-14k: Chinese White Dolphin detection dataset

A dataset consisting of  Chinese White Dolphin (CWD) and distractors for detection tasks.

Crowd counting: Zero-shot cross-domain counting

Generalized loss function for crowd counting.

CVCS: Cross-View Cross-Scene Multi-View Crowd Counting Dataset

Synthetic dataset for cross-view cross-scene multi-view counting. The dataset contains 31 scenes, each with about ~100 camera views. For each scene, we capture 100 multi-view images of crowds.

Crowd counting: Generalized loss function

Generalized loss function for crowd counting.

Fine-Grained Crowd Counting Dataset

Dataset for fine-grained crowd counting, which differentiates a crowd into categories based on the low-level behavior attributes of the individuals (e.g. standing/sitting or violent behavior) and then counts the number of people in each category.