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.
Psychonomic Bulletin & Review, to appear 2018.
- Density-Preserving Hierarchical EM Algorithm: Simplifying Gaussian Mixture Models for Approximate Inference.
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.
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.
SLEEP, to appear.
- Hand detection using deformable part models on an egocentric perspective.
In: Digital Image Computing: Techniques and Applications (DICTA), Canberra, Dec 2018.
- Gated Hierarchical Attention for Image Captioning.
In: Asian Conference on Computer Vision (ACCV), Perth, Dec 2018.
- Learning Dynamic Memory Networks for Object Tracking.
In: European Conference on Computer Vision (ECCV), Munich, Sept 2018.
- Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid.
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.
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.
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.,