336 citations · 523 across the 20 of their papers we have counts for
5 papers · 1 filter
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…
CBAM: Convolutional Block Attention Module
Sanghyun Woo, Jongchan Park, Joon-Young Lee +1
We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks. Given an intermediate feature map, o…
BAM: Bottleneck Attention Module
Jongchan Park, Sanghyun Woo, Joon-Young Lee +1
Recent advances in deep neural networks have been developed via architecture search for stronger representational power. In this work, we focus on the effect of attention in genera…
Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning
Jongchan Park, Joon-Young Lee, Donggeun Yoo +1
Learning-based color enhancement approaches typically learn to map from input images to retouched images. Most of existing methods require expensive pairs of input-retouched images…
The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation
Kaichun Mo, Haoxiang Li, Zhe Lin +1
Deep reinforcement learning (DRL) demonstrates its potential in learning a model-free navigation policy for robot visual navigation. However, the data-demanding algorithm relies on…