4 citations · 7 across the 6 of their papers we have counts for
5 papers · 1 filter
Transferring Knowledge with Attention Distillation for Multi-Domain Image-to-Image Translation
Runze Li, Tomaso Fontanini, Luca Donati +2
Gradient-based attention modeling has been used widely as a way to visualize and understand convolutional neural networks. However, exploiting these visual explanations during the…
MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments
Pan Ji, Runze Li, Bir Bhanu +1
Self-supervised depth estimation for indoor environments is more challenging than its outdoor counterpart in at least the following two aspects: (i) the depth range of indoor seque…
Learning Local Recurrent Models for Human Mesh Recovery
Runze Li, Srikrishna Karanam, Ren Li +3
We consider the problem of estimating frame-level full human body meshes given a video of a person with natural motion dynamics. While much progress in this field has been in singl…
Fully Convolutional Scene Graph Generation
Hengyue Liu, Ning Yan, Masood S. Mortazavi +1
This paper presents a fully convolutional scene graph generation (FCSGG) model that detects objects and relations simultaneously. Most of the scene graph generation frameworks use…
Towards Visually Explaining Variational Autoencoders
Wenqian Liu, Runze Li, Meng Zheng +5
Recent advances in Convolutional Neural Network (CNN) model interpretability have led to impressive progress in visualizing and understanding model predictions. In particular, grad…