50 citations · 126 across the 12 of their papers we have counts for
12 papers
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
Rajeev Yasarla, Hong Cai, Jisoo Jeong +3
We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation. MAMo can augment and improve any single-image depth estimation networks into video de…
DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow
Risheek Garrepalli, Jisoo Jeong, Rajeswaran C Ravindran +2
Recent advancements in neural network-based optical flow estimation often come with prohibitively high computational and memory requirements, presenting challenges in their model a…
DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling
Jisoo Jeong, Hong Cai, Risheek Garrepalli +1
We propose a novel data augmentation approach, DistractFlow, for training optical flow estimation models by introducing realistic distractions to the input frames. Based on a mixin…
Imposing Consistency for Optical Flow Estimation
Jisoo Jeong, Jamie Menjay Lin, Fatih Porikli +1
Imposing consistency through proxy tasks has been shown to enhance data-driven learning and enable self-supervision in various tasks. This paper introduces novel and effective cons…
MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
JongMok Kim, Jooyoung Jang, Seunghyeon Seo +3
Many recent semi-supervised learning (SSL) studies build teacher-student architecture and train the student network by the generated supervisory signal from the teacher. Data augme…
Deep Learning-based High-precision Depth Map Estimation from Missing Viewpoints for 360 Degree Digital Holography
Hakdong Kim, Heonyeong Lim, Minkyu Jee +5
In this paper, we propose a novel, convolutional neural network model to extract highly precise depth maps from missing viewpoints, especially well applicable to generate holograph…
Interpolation-based semi-supervised learning for object detection
Jisoo Jeong, Vikas Verma, Minsung Hyun +2
Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection…
Class-Imbalanced Semi-Supervised Learning
Minsung Hyun, Jisoo Jeong, Nojun Kwak
Semi-Supervised Learning (SSL) has achieved great success in overcoming the difficulties of labeling and making full use of unlabeled data. However, SSL has a limited assumption th…
Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction
Daesik Kim, Gyujeong Lee, Jisoo Jeong +1
In this work, we introduce a novel weakly supervised object detection (WSOD) paradigm to detect objects belonging to rare classes that have not many examples using transferable kno…
Residual Features and Unified Prediction Network for Single Stage Detection
Kyoungmin Lee, Jaeseok Choi, Jisoo Jeong +1
Recently, a lot of single stage detectors using multi-scale features have been actively proposed. They are much faster than two stage detectors that use region proposal networks (R…
Superpixel-based Semantic Segmentation Trained by Statistical Process Control
Hyojin Park, Jisoo Jeong, Youngjoon Yoo +1
Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks. However, considering that nei…
Enhancement of SSD by concatenating feature maps for object detection
Jisoo Jeong, Hyojin Park, Nojun Kwak
We propose an object detection method that improves the accuracy of the conventional SSD (Single Shot Multibox Detector), which is one of the top object detection algorithms in bot…