About
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]
- [Jun 16, 2023]
Congratulations to Hui for defending her thesis!
- [Jan 19, 2023]
Congratulations to Xueying for defending her thesis!
- [Dec 9, 2022]
Congratulations to Ziquan for defending his thesis!
- [Nov 30, 2022]
Call for Papers: Special Issue on “Applications of artificial intelligence, computer vision, physics and econometrics modelling methods in pedestrian traffic modelling and crowd safety” in Transportation Research Part C: Emerging Technologies. Deadline April 30th, 2023.
Recent Publications [more]
- Gradient-based Instance-Specific Visual Explanations for Object Specification and Object Discrimination.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), to appear 2024. - A Fixed-Point Approach to Unified Prompt-Based Counting.
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In: AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Feb 2024. - Multi-view People Detection in Large Scenes via Supervised View-wise Contribution Weighting.
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In: AAAI Conference on Artificial Intelligence (AAAI), Feb 2024. [project] - Retrieval-Augmented Multiple Instance Learning.
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In: Neural Information Processing Systems (NeurIPS), New Orleans, Dec 2023. - Single-Frame-Based Deep View Synchronization for Unsynchronized Multicamera Surveillance.
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IEEE Trans. on Neural Networks and Learning Systems (TNNLS), 34(12):10653-10667, Dec 2023. [code&dataset] - Towards the next generation explainable AI that promotes AI-human mutual understanding.
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In: NeurIPS workshop on XAI in Action: Past, Present, and Future Applications, New Orleans, Dec 2023. - Generalized Characteristic Function Loss for Crowd Analysis in the Frequency Domain.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), to appear 2023. [supplemental] - Scalable Video Object Segmentation with Simplified Framework.
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In: International Conf. Computer Vision (ICCV), Paris, Oct 2023. [code] - Modeling Noisy Annotations for Point-Wise Supervision.
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IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI), to appear 2023. - Variational Nested Dropout.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 45(8):10519-10534, Aug 2023 (online Feb 2023).
Recent Project Pages [more]
We propose a batch-mode Pareto Optimization Active Learning (POAL) framework for Active Learning under Out-of-Distribution data scenarios.
- , "Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios." Transactions on Machine Learning Research (TMLR), June 2023.
We propose the gradient-weighted Object Detector Activation Maps (ODAM), a visualized explanation technique for interpreting the predictions of object detectors, including class score and bounding box coordinates.
- , "ODAM: Gradient-based Instance-Specific Visual Explanations for Object Detection." In: Intl. Conf. on Learning Representations (ICLR), Rwanda, May 2023. [code]
- , "Gradient-based Instance-Specific Visual Explanations for Object Specification and Object Discrimination." IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), to appear 2024.
We present a comprehensive comparative survey of 19 Deep Active Learning approaches for classification tasks.
We introduce an active learning benchmark comprising 35 public datasets and experiment protocols, and evaluate 17 pool-based AL methods.
- , "A Comparative Survey: Benchmarking for Pool-based Active Learning." In: International Joint Conf. on Artificial Intelligence (IJCAI), Survey Track, Aug 2021.
We propose a calibration-free multi-view crowd counting (CF-MVCC) method, which obtains the scene-level count as a weighted summation over the predicted density maps from the camera-views, without needing camera calibration parameters.
- , "Calibration-free Multi-view Crowd Counting." In: European Conference on Computer Vision (ECCV), Tel Aviv, Oct 2022. [supplemental]
Recent Datasets and Code [more]
Modeling Eye Movements with Deep Neural Networks and Hidden Markov Models (DNN+HMM)
This is the toolbox for modeling eye movements and feature learning with deep neural networks and hidden Markov models (DNN+HMM).
- Files: download here
- Project page
- If you use this toolbox please cite:
Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models.
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npj Science of Learning, 7:28, Oct 2022.
Dolphin-14k: Chinese White Dolphin detection dataset
A dataset consisting of Chinese White Dolphin (CWD) and distractors for detection tasks.
- Files: Google Drive, Readme
- Project page
- If you use this dataset please cite:
Chinese White Dolphin Detection in the Wild.
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In: ACM Multimedia Asia (MMAsia), Gold Coast, Australia, Dec 2021.
Crowd counting: Zero-shot cross-domain counting
Generalized loss function for crowd counting.
- Files: github
- Project page
- If you use this toolbox please cite:
Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting.
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In: ACM Multimedia (MM), Oct 2021.
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.
- Files: Google Drive
- Project page
- If you use this dataset please cite:
Cross-View Cross-Scene Multi-View Crowd Counting.
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In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR):557-567, Jun 2021.
Crowd counting: Generalized loss function
Generalized loss function for crowd counting.
- Files: github
- Project page
- If you use this toolbox please cite:
A Generalized Loss Function for Crowd Counting and Localization.
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In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021.