Richang Hong

TL;DR: The benchmark results indicate that it is possible to learn effective models from sufficiently large image dataset to facilitate general image retrieval and four research issues on web image annotation and retrieval are identified.

TL;DR: The background knowledge and the available features related to crowded scenes are provided and existing models, popular algorithms, evaluation protocols, and system performance are provided corresponding to different aspects of the crowded scene analysis.

TL;DR: This paper shows that various crucial factors in video annotation, including multiple modalities, multiple distance functions, and temporal consistency, all correspond to different relationships among video units, and hence they can be represented by different graphs, and proposes optimized multigraph-based semi-supervised learning (OMG-SSL), which aims to simultaneously tackle these difficulties in a unified scheme.

TL;DR: This paper proposes an efficient multi-cue analysis framework for robust visual tracking by combining different types of features, and constructs multiple experts through Discriminative Correlation Filter and each of them tracks the target independently.

TL;DR: This paper presents a novel approach - Multiple Feature Hashing (MFH) to tackle both the accuracy and the scalability issues of NDVR and shows that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.