84 citations · 107 across the 13 of their papers we have counts for
14 papers
Dynamic Neural Network for Multi-Task Learning Searching across Diverse Network Topologies
Wonhyeok Choi, Sunghoon Im
In this paper, we present a new MTL framework that searches for structures optimized for multiple tasks with diverse graph topologies and shares features among tasks. We design a r…
Offline-to-Online Knowledge Distillation for Video Instance Segmentation
Hojin Kim, Seunghun Lee, Sunghoon Im
In this paper, we present offline-to-online knowledge distillation (OOKD) for video instance segmentation (VIS), which transfers a wealth of video knowledge from an offline model t…
A Study on the Generality of Neural Network Structures for Monocular Depth Estimation
Jinwoo Bae, Kyumin Hwang, Sunghoon Im
Monocular depth estimation has been widely studied, and significant improvements in performance have been recently reported. However, most previous works are evaluated on a few ben…
Deep Digging into the Generalization of Self-Supervised Monocular Depth Estimation
Jinwoo Bae, Sungho Moon, Sunghoon Im
Self-supervised monocular depth estimation has been widely studied recently. Most of the work has focused on improving performance on benchmark datasets, such as KITTI, but has off…
ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation
Seunghun Lee, Wonhyeok Choi, Changjae Kim +2
In this paper, we present a direct adaptation strategy (ADAS), which aims to directly adapt a single model to multiple target domains in a semantic segmentation task without pretra…
ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attack
Dahoon Park, Kon-Woo Kwon, Sunghoon Im +1
In this paper, we present Zero-data Based Repeated bit flip Attack (ZeBRA) that precisely destroys deep neural networks (DNNs) by synthesizing its own attack datasets. Many prior w…