About
Welcome to the Video, Image, and Sound Analysis Lab (VISAL) at the City University of Hong Kong! The lab is directed by Dr. 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]
- [Nov 1, 2018]
Congratulations to Lei for defending her thesis!
- [Jun 28, 2016]
Congratulations to Sijin for defending his thesis!
- [Jun 25, 2016]
Congratulations to Adeel for winning a “Best Research Paper Award 2013/14” from the Higher Education Commission (HEC) of Pakistan for his TPAMI 2013 paper!
- [Apr 26, 2016]
Congratulations to Huy for defending his Thesis!
Recent Publications [more]
- Eye Movement Patterns in Face Recognition are Associated with Cognitive Decline in Older Adults.
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Psychonomic Bulletin & Review, to appear 2018. - Density-Preserving Hierarchical EM Algorithm: Simplifying Gaussian Mixture Models for Approximate Inference.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), to appear 2018. - Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking.
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IEEE Trans. on Circuits and Systems for Video Technology (TCSVT), to appear 2018. - Individuals with Insomnia Misrecognize Angry Faces as Fearful Faces While Missing the Eyes: An Eye-Tracking Study.
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SLEEP, to appear. - Hand detection using deformable part models on an egocentric perspective.
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In: Digital Image Computing: Techniques and Applications (DICTA), Canberra, Dec 2018. - Gated Hierarchical Attention for Image Captioning.
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In: Asian Conference on Computer Vision (ACCV), Perth, Dec 2018. - Learning Dynamic Memory Networks for Object Tracking.
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In: European Conference on Computer Vision (ECCV), Munich, Sept 2018. - Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid.
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In: British Machine Vision Conference (BMVC), Newcastle, Sept 2018. [supplemental] - Music Reading Expertise Facilitates English but not Chinese sentence reading: Evidence from Eye Movement Behavior.
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In: 15th International Conference on Music Perception and Cognition (ICMPC15), Sydney, July 2018. - Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in Crowd Scenes.
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In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Jun 2018.
Recent Project Pages [more]
An algorithm is proposed to simplify the Gaussian Mixture Models into a reduced mixture model with fewer mixture components, by maximizing a variational lower bound of the expected log-likelihood of a set of virtual samples.
- , "Approximate Inference for Generic Likelihoods via Density-Preserving GMM Simplification." In: NIPS 2016 Workshop on Advances in Approximate Bayesian Inference, Barcelona, Dec 2016.
- , "Density-Preserving Hierarchical EM Algorithm: Simplifying Gaussian Mixture Models for Approximate Inference." IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), to appear 2018.
RNN-based models dominate the field of image captioning, however, (1) RNNs have to be calculated step-by-step, which is not easily parallelized. (2) There is a long path between the start and end of the sentence using RNNs. Tree structures can make a shorter path, but trees require special processing. (3) RNNs only learn single-level representations at each time step, while convolutional decoders are able to learn multi-level representations of concepts, and each of them should corresponds to an image area, which should benefit word prediction.
- , "CNN+CNN: Convolutional Decoders for Image Captioning." In: IEEE Computer Vision and Pattern Recognition: Language and Vision Workshop, Salt Lake City, Jun 2018.
- , "Gated Hierarchical Attention for Image Captioning." In: Asian Conference on Computer Vision (ACCV), Perth, Dec 2018.
We propose CNN-pixel and FCNN-skip to produce an original-resolution density map. In our experiments, we found that the lower-resolution density maps sometimes have better counting performance. In contrast, the original-resolution density maps improved localization tasks, such as detection and tracking, compared to bilinear upsampling the lower-resolution density maps.
- , "Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking." IEEE Trans. on Circuits and Systems for Video Technology (TCSVT), to appear 2018.
