Cheng Deng

TL;DR: A new clustering model, called DEeP Embedded Regularized ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments is proposed, which indicates the superiority and faster running time of DEPICT in real-world clustering tasks, where no labeled data is available for hyper-parameter tuning.

TL;DR: Zhang et al. as mentioned in this paper proposed a new clustering model, called DEeP Embedded Regularized Clustering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments.

TL;DR: A triplet-based deep hashing (TDH) network for cross-modal retrieval using the triplet labels, which describe the relative relationships among three instances as supervision in order to capture more general semantic correlations between cross- modal instances.

TL;DR: Zhang et al. as discussed by the authors proposed a coarse-to-fine pyramid model to relax the need of precise bounding boxes, which not only incorporates local and global information, but also integrates the gradual cues between them.

TL;DR: This paper proposes a novel deep cross-modal hashing method to generate compact hash codes through an end-to-end deep learning architecture, which can effectively capture the intrinsic relationships between various modalities.