614 citations · 1.1k across the 11 of their papers we have counts for
6 papers · 1 filter
Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning
Ilchae Jung, Kihyun You, Hyeonwoo Noh +2
We propose a novel meta-learning framework for real-time object tracking with efficient model adaptation and channel pruning. Given an object tracker, our framework learns to fine-…
Large-Scale Image Retrieval with Attentive Deep Local Features
Hyeonwoo Noh, Andre Araujo, Jack Sim +2
We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature). The new feature is based on convolutional neura…
Training Recurrent Answering Units with Joint Loss Minimization for VQA
Hyeonwoo Noh, Bohyung Han
We propose a novel algorithm for visual question answering based on a recurrent deep neural network, where every module in the network corresponds to a complete answering unit with…
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
Hyeonwoo Noh, Paul Hongsuck Seo, Bohyung Han
We tackle image question answering (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose weights are determined adaptively based o…
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
Seunghoon Hong, Hyeonwoo Noh, Bohyung Han
We propose a novel deep neural network architecture for semi-supervised semantic segmentation using heterogeneous annotations. Contrary to existing approaches posing semantic segme…
Learning Deconvolution Network for Semantic Segmentation
Hyeonwoo Noh, Seunghoon Hong, Bohyung Han
We propose a novel semantic segmentation algorithm by learning a deconvolution network. We learn the network on top of the convolutional layers adopted from VGG 16-layer net. The d…