most citedLearning Color Compatibility in Fashion Outfits

8 citations · 12 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV20208 cited

Learning Color Compatibility in Fashion Outfits

Heming Zhang, Xuewen Yang, Jianchao Tan +3

Color compatibility is important for evaluating the compatibility of a fashion outfit, yet it was neglected in previous studies. We bring this important problem to researchers' att…

cs.CV20191 cited

Deep Kinship Verification via Appearance-shape Joint Prediction and Adaptation-based Approach

Heming Zhang, Xiaolong Wang, C. -C. Jay Kuo

Kinship verification aims to identify the kin relation between two given face images. It is a very challenging problem due to the lack of training data and facial similarity variat…

cs.CV2019

Accelerating Proposal Generation Network for \\Fast Face Detection on Mobile Devices

Heming Zhang, Xiaolong Wang, Jingwen Zhu +1

Face detection is a widely studied problem over the past few decades. Recently, significant improvements have been achieved via the deep neural network, however, it is still challe…

cs.CV20193 cited

Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML

Jie Zhang, Junting Zhang, Shalini Ghosh +4

Lifelong learning, the problem of continual learning where tasks arrive in sequence, has been lately attracting more attention in the computer vision community. The aim of lifelong…

cs.CV2019

Class-incremental Learning via Deep Model Consolidation

Junting Zhang, Jie Zhang, Shalini Ghosh +5

Deep neural networks (DNNs) often suffer from "catastrophic forgetting" during incremental learning (IL) --- an abrupt degradation of performance on the original set of classes whe…

cs.CV2019

Generative Visual Dialogue System via Adaptive Reasoning and Weighted Likelihood Estimation

Heming Zhang, Shalini Ghosh, Larry Heck +4

The key challenge of generative Visual Dialogue (VD) systems is to respond to human queries with informative answers in natural and contiguous conversation flow. Traditional Maximu…