Rongrong Ji

TL;DR: A novel kernel-based supervised hashing model which requires a limited amount of supervised information, i.e., similar and dissimilar data pairs, and a feasible training cost in achieving high quality hashing, and significantly outperforms the state-of-the-arts in searching both metric distance neighbors and semantically similar neighbors is proposed.

TL;DR: This work presents a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP) and proposes SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image.

TL;DR: A hypergraph analysis approach to address the problem of view-based 3-D object retrieval and recognition by avoiding the estimation of the distance between objects by constructing multiple hypergraphs based on their 2-D views.

TL;DR: A hypergraph neural networks framework for data representation learning, which can encode high-order data correlation in a hypergraph structure using a hyperedge convolution operation, which outperforms recent state-of-theart methods.

TL;DR: This paper proposes a novel filter pruning method by exploring the High Rank of feature maps (HRank), inspired by the discovery that the average rank of multiple feature maps generated by a single filter is always the same, regardless of the number of image batches CNNs receive.