66 citations · 70 across the 3 of their papers we have counts for
5 papers · 1 filter
Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion
Yikai Wang, Fuchun Sun, Ming Lu +1
We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network. The framework consists of two innovative fusion schemes. Firstly, u…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…
A Closed-form Solution to Universal Style Transfer
Ming Lu, Hao Zhao, Anbang Yao +3
Universal style transfer tries to explicitly minimize the losses in feature space, thus it does not require training on any pre-defined styles. It usually uses different layers of…
RON: Reverse Connection with Objectness Prior Networks for Object Detection
Tao Kong, Fuchun Sun, Anbang Yao +3
We present RON, an efficient and effective framework for generic object detection. Our motivation is to smartly associate the best of the region-based (e.g., Faster R-CNN) and regi…
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
Hao Zhao, Ming Lu, Anbang Yao +3
In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the benefits of two novel techniques. The first one is seman…