News

MCL member, Qin Huang, was awarded the Capocelli Prize at the 2017 Data Compression Conference for his paper entitled “Measure and Prediction of HEVC Perceptually Lossy/Lossless Boundary QP Values”. We are so glad to have him share about his experience in attending the conference. Here is his sharing.
Earlier in this April, I attended the Data Compression Conference 2017 in Snowbird, Utah to present our paper ‘Measure and Prediction of HEVC Perceptually Lossy/Lossless Boundary QP Values’. The work is designed to provide with a dynamic prediction framework that could help estimate the minimum encoding QPs to offer perceptually similar quality.

The conference is held on the beautiful mountain of Snowbird, and it was a really amazing experience to share our work with all the experts in the field. I met with a lot of researchers both in academy and industry, and we discussed constantly after the conference.

It was my great honor to receive the 2017 Capocelli Award, and I really appreciate all the DCC program committee for considering me for the award.

Thank you all!

Professor Kuo Gave a Talk on Deep Learning at Ming Hsieh Institute

The Ming Hsieh Institute has launched an MHI Emerging Trends Series. MCL Director, Professor C.-C. Jay Kuo, was the first speaker in this series. Professor Kuo gave his talk on deep learning on April 10 (Monday), 2017.

There is a resurging interest in developing a neural-network-based solution to supervised machine learning in the last 5 years. However, little theoretical work was reported in this area. In his talk, Professor Kuo attempted to provide some theoretical foundation to the working principle of the convolutional neural network (CNN) from a signal processing viewpoint. First, he introduced the RECOS transform as a basic building block for CNNs. The term “RECOS” is an acronym for “REctified-COrrelations on a Sphere”. It consists of two main concepts: data clustering on a sphere and rectification. Then, a CNN is interpreted as a network that implements the guided multi-layer RECOS transform. Along this line, he also compared the traditional single-layer and modern multi-layer signal analysis approaches. Furthermore, he discussed how guidance can be provided by data labels through backpropagation in the training with an attempt to offer a smooth transition from weakly to heavily supervised learning. Finally, he pointed out several future research directions at the end.

There were about 80 people attending Professor Kuo’s seminar. Many questions were asked after his talk. Professor Kuo said that he enjoyed the interaction with the audience very much and it demonstrated the strong interest of the audience on this topic.

MCL members, Chi-Hao (Eddy) Wu and Siyang Li presented their papers at Winter Conference on Applications of Computer Vision (WACV) 2017, Santa Rosa, CA, USA

The title of Eddy’s paper is “Boosted Convolutional Neural Networks (BCNN) for Pedestrian Detection”, with Weihao Gan, De Lan and C.-C. Jay Kuo as the co-authors. Here is a brief summary:

“In this work, a boosted convolutional neural network (BCNN) system is proposed to enhance the pedestrian detection performance. Being inspired by the classic boosting idea, we develop a weighted loss function that emphasizes challenging samples in training a convolutional neural network (CNN). Two types of samples are considered challenging: 1) samples with detection scores falling in the decision boundary, and 2) temporally associated samples with inconsistent scores. A weighting scheme is designed for each of them. Finally, we train a boosted fusion layer to benefit from the integration of these two weighting schemes. We use the Fast-RCNN as the baseline, and test the corresponding BCNN on the Caltech pedestrian dataset in the experiment, and show a significant performance gain of the BCNN over its baseline.” 

Siyang’s paper is entitled “Box Refinement: Object Proposal Enhancement and Pruning”, co-authored with Heming Zhang, Junting Zhang, Yuzhuo Ren and C.-C. Jay Kuo. The summary goes as followed:

“Object proposal generation has been an important preprocessing step for object detectors in general and the convolutional neural network (CNN) detectors in particular. Recently, people start to use the CNN to generate object proposals but most of these methods suffer from the localization bias problem, like other objectness-based methods. Since contours offer a powerful cue for accurate localization, we propose a box refinement method by searching for the optimal contour for each initial bounding box that minimizes the contour cost. [...]

Fashion fingerprint is a compact feature vector for fashion items that can be used for tasks such as recognition, clustering, and retrieval of similar items. It is equally useful for both online fashion retailers as well as for physical apparel stores (with or without their online extensions). A related problem is understanding the apparel preferences of an individual from the dresses that they wear while visiting the physical store. One of the challenges of fashion study different from others is lack of enough accurate annotation. Available datasets have either limited number of images or very noisy annotation.

Currently we have successfully trained a fashion item localization model based on SSD[1]. The model is able to localize upper clothes, bottom clothes and one-pieces and has been tested on the Clothing Parsing dataset[2]. It achieves an F-score of 0.887 on upper clothes localization. For other clothing items, errors occur because the model may focus on too local regions and thus gets confused between skirt and dress. Prior location of human body will be incorporated in our model to solve this problem.

In the future we will further refine our localization model and also work on two directions. One is to recognize the garments based on our localization. The other one is to automatically label more images to enlarge the size of datasets.

[1] Liu, Wei, et al. “SSD: Single shot multibox detector.” European Conference on Computer Vision. Springer International Publishing, 2016.
[2] Liang, Xiaodan, et al. “Deep human parsing with active template regression.” IEEE transactions on pattern analysis and machine intelligence 37.12 (2015): 2402-2414.

MCL Director, Professor C.-C. Jay Kuo, received the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award on March 6 at the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) held in New Orleans, Louisiana, USA.

The IEEE Leon K. Kirchmayer Graduate Teaching Award is sponsored by the Leon K. Kirchmayer Memorial Fund and recognizes inspirational teaching of graduate students in the IEEE fields of interest.  Professor Kuo received this award for excellence in inspirational guidance of graduate students and curriculum development in the area of multimedia signal processing.

Professor Kuo gave the following short speech in the Award Ceremony. “It was a great honor to be recognized by the prestigious Lion K. Kirchmayer Graduate Teaching Award. I would like to use this opportunity to appreciate a few people who had great impacts on my teaching career. When I was a PhD student at MIT, I was fortunate to work with a few young faculty members. They were my PhD and Master thesis advisor Bernard Levy, my PhD thesis co-advisor, John Tsitsiklis, my MS thesis co-advisor, Bruce Musicus, my PhD thesis committee member, Nick Trefethen, and my postdoc mentor at UCLA, Tony Chan. They spent an enormous amount of time nurturing and advising me. I am obliged to them deeply. Furthermore, I would like to say thanks to my graduate students. They are not only my students but also my teachers. I learned many new topics together with them. Finally, I would like to give thanks to my wife and daughter. Their unconditional love and patience allow me to do whatever I want to pursue. I do owe them tremendously, and would to like to share my joy and honor with them.”

Congratulations to Professor [...]