Rongrong Ji

TL;DR: This paper presents a unified statistical framework for modeling both saccadic eye movements and visual saliency based on super-Gaussian component (SGC) analysis, and shows promising potentials of statistical approaches for human behavior research.

TL;DR: This paper mine a discriminative topology and a low-redundant topology from the local descriptors under a probabilistic perspective, which are further integrated into a boosting framework for scene categorization.

TL;DR: This paper presents a novel multi-grained contrastive model, namely X-CLIP, and proposes the Attention Over Similarity Matrix (AOSM) module to make the model focus on the contrast between essential frames and words, thus lowering the impact of unnecessary frames and Words on retrieval results.

TL;DR: In this article, a semi-supervised adversarial learning framework was proposed to solve the problem of monocular depth estimation when only a limited number of training image-depth pairs are available.

TL;DR: This paper designs a group of biological experiments that adopt eye tracker to identify spatial regions of interest during the facial aesthetic judgments of subjects, which forms a Bio-inspired Facial Aesthetic Ontology (Bio-FAO) and is made public available, and adopts the cutting-edge convolutional neural network to train a set of Bio- inspired Attribute features.