Convolution neural network (CNN) has been shown as one of state-of-the-art approaches for learning face representations. However, previous works only utilized identity information instead of leveraging human attributes (e.g., gender and age) which contain high-level semantic meaning. In this work, we aim to incorporate identity and human attributes in learning discriminative face representations through multi-task learning. In our experiments, we learn face representation by using the largest publicly face dataset CASIA-WebFace with gender and age labels, and then evaluate learned features on widely-used LFW benchmark for face verification and identification. We also compare the effectiveness of different attributes for improving face identification. The results show that the proposed model outperforms the baseline CNN method without using multi-task learning and hand-crafted features such as high-dimensional LBP. We also do experiments on gender and age estimation on Adience benchmark to demonstrate that human attribute prediction can also benefit from the proposed multi-task representation learning.

Nowadays, with the success and fast growth of social media communities and mobile devices, people are encouraged to share their multimedia data online. Analyzing and summarizing data into useful information thus becomes increasingly important. For online photo sharing services like Flickr, when users are uploading a batch of daily photos at a time, the tags users provided tend to be rather vague, containing only a small amount of information. For better photo application and understanding, we attempt to automatically discover semantic-rich (hidden) aspects of photos merely by looking at image contents. In this paper, we propose an effective model, which is a combination of LDA model and deep learning representations, to realize the idea of automatic aspect discovery. We then discuss the properties of this aspect discovery model through experiments on event summarization task. In those experiments, we show the high diversity and high quality of aspects discovered by our proposed method. Meanwhile, we conduct an user study to evaluate the quality of the summarized results. Moreover, the proposed method can be further extended to human attribute discovery for a given event. We automatically discover different aspects on our Olympic Games data (e.g. football, ice skating).

Recently, the fast growth of social media communities and mobile devices encourages more people to share their media data online than ever before. Analyzing data and summarizing data into useful information have become increasingly popular and important for modern society. Given a set of event keywords and a dataset, this paper performs event summarization, aiming to discover and summarize what people may concern for each event from the given dataset. More specifically, this paper extracts latent sub-events with diverse and representative attributes for each given event. This paper proposes effective methods on detecting events with (1) human attribute discovery, such as human pose and clothes, (2) scene analysis, (3) image aspect discovery, and (4) temporal and semantic analysis, to provide people different perspectives for the events they are interested in. For practical implementation, this paper studied and conducted experiments on YFCC100M, which is a dataset with 100 million of photos and videos, provided by Yahoo!. Finally, a comprehensive and complete system is created accordingly to support diverse event summarization.