Tiejun Huang

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Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. This severely limits their scalability in realworld applications. To overcome this limitation, we develop a novel cross-dataset transfer learning approach to learn a discriminative(More)
The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from a large-scale image or video database. However, compared with person re-identification or face recognition, vehicle search problem has long been neglected by researchers in vision community. This paper focuses on an interesting but(More)
Keyphrases play a key role in text indexing, summarization and categorization. However, most of the existing keyphrase extraction approaches require human-labeled training sets. In this paper, we propose an automatic keyphrase extraction algorithm, which can be used in both supervised and unsupervised tasks. This algorithm treats each document as a semantic(More)
Promising visual object search technologies as well as numerous real world applications have been developed. To ensure the application interoperability, the Moving Picture Experts Group (MPEG) has made great efforts in standardizing visual search technologies. Moreover, extraction and transmission of compact descriptors are valuable for next generation(More)
Extraction and transmission of compact descriptors are of great importance for next-generation mobile visual search applications. Existing visual descriptor techniques mainly compress visual features into compact codes of fixed bit rate, which is not adaptive to the bandwidth fluctuation in wireless environment. In this letter, we propose a Rate-adaptive(More)
This letter presents a novel approach for visual saliency estimation through single image optimization. Instead of directly mapping visual features to saliency values with a unified model, we treat regional saliency values as the optimization objective on each single image. By using a quadratic programming framework, our approach can adaptively optimize the(More)
In this paper, we present a probabilistic multi-task learning approach for visual saliency estimation in video. In our approach, the problem of visual saliency estimation is modeled by simultaneously considering the stimulus-driven and task-related factors in a probabilistic framework. In this framework, a stimulus-driven component simulates the low-level(More)