M
Mo Zhou
Researcher at Xi'an Jiaotong University
Publications - 15
Citations - 909
Mo Zhou is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Ranking & Computer science. The author has an hindex of 5, co-authored 11 publications receiving 563 citations. Previous affiliations of Mo Zhou include Xidian University.
Papers
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Proceedings ArticleDOI
Ordinal Regression with Multiple Output CNN for Age Estimation
TL;DR: This paper proposes an End-to-End learning approach to address ordinal regression problems using deep Convolutional Neural Network, which could simultaneously conduct feature learning and regression modeling, and achieves the state-of-the-art performance on both the MORPH and AFAD datasets.
Proceedings ArticleDOI
Hierarchical Multimodal LSTM for Dense Visual-Semantic Embedding
TL;DR: This work presents a hierarchical structured recurrent neural network (RNN), namely Hierarchical Multimodal LSTM (HM-LSTM), which exploits the hierarchical relations between sentences and phrases, and between whole images and image regions, to jointly establish their representations.
Proceedings ArticleDOI
SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction
TL;DR: Wang et al. as mentioned in this paper proposed a sparse graph convolutional network (SGCN) for pedestrian trajectory prediction, which explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians.
Journal ArticleDOI
Ladder Loss for Coherent Visual-Semantic Embedding
TL;DR: A continuous variable is introduced to model the relevance degree between queries and multiple candidates, and a coherent embedding space is proposed, where candidates with higher relevance degrees are mapped closer to the query than those with lower relevance degrees.
Book ChapterDOI
Adversarial Ranking Attack and Defense
TL;DR: In this paper, the expected ranking order is first represented as a set of inequalities, and then a triplet-like objective function is designed to obtain the optimal perturbation.