42 citations · 244 across the 31 of their papers we have counts for
7 papers · 1 filter
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization
Siyuan Qiao, Zhe Lin, Jianming Zhang +1
In this paper, we study the problem of improving computational resource utilization of neural networks. Deep neural networks are usually over-parameterized for their tasks in order…
Guided Zoom: Questioning Network Evidence for Fine-grained Classification
Sarah Adel Bargal, Andrea Zunino, Vitali Petsiuk +4
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions. It does so by making sure the model has "the right reas…
DeepLens: Shallow Depth Of Field From A Single Image
Lijun Wang, Xiaohui Shen, Jianming Zhang +5
We aim to generate high resolution shallow depth-of-field (DoF) images from a single all-in-focus image with controllable focal distance and aperture size. To achieve this, we prop…
GAPLE: Generalizable Approaching Policy LEarning for Robotic Object Searching in Indoor Environment
Xin Ye, Zhe Lin, Joon-Young Lee +3
We study the problem of learning a generalizable action policy for an intelligent agent to actively approach an object of interest in an indoor environment solely from its visual i…
Concept Mask: Large-Scale Segmentation from Semantic Concepts
Yufei Wang, Zhe Lin, Xiaohui Shen +2
Existing works on semantic segmentation typically consider a small number of labels, ranging from tens to a few hundreds. With a large number of labels, training and evaluation of…
Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias
Rameswar Panda, Jianming Zhang, Haoxiang Li +3
While machine learning approaches to visual emotion recognition offer great promise, current methods consider training and testing models on small scale datasets covering limited v…
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
Andrea Zunino, Sarah Adel Bargal, Pietro Morerio +3
We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction defined as the firing of neurons in specific paths. In this work, we utilize…