no code implementations • 11 Apr 2024 • Jamie Menjay Lin, Jisoo Jeong, Hong Cai, Risheek Garrepalli, Kai Wang, Fatih Porikli
Optical flow estimation is crucial to a variety of vision tasks.
no code implementations • CVPR 2024 • Jisoo Jeong, Hong Cai, Risheek Garrepalli, Jamie Menjay Lin, Munawar Hayat, Fatih Porikli
We propose OCAI, a method that supports robust frame interpolation by generating intermediate video frames alongside optical flows in between.
no code implementations • 19 Mar 2024 • Rajeev Yasarla, Manish Kumar Singh, Hong Cai, Yunxiao Shi, Jisoo Jeong, Yinhao Zhu, Shizhong Han, Risheek Garrepalli, Fatih Porikli
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training.
Ranked #3 on
Monocular Depth Estimation
on KITTI Eigen split
no code implementations • IEEE/CVF International Conference on Computer Vision (ICCV) 2023 • Rajeev Yasarla, Hong Cai, Jisoo Jeong, Yunxiao Shi, Risheek Garrepalli, Fatih Porikli
We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation.
Ranked #14 on
Monocular Depth Estimation
on KITTI Eigen split
no code implementations • 9 Jun 2023 • Risheek Garrepalli, Jisoo Jeong, Rajeswaran C Ravindran, Jamie Menjay Lin, Fatih Porikli
Also, we present a novel dynamic coarse-to-fine cost volume processing during various stages of refinement to avoid multiple levels of cost volumes.
no code implementations • CVPR 2023 • Jisoo Jeong, Hong Cai, Risheek Garrepalli, Fatih Porikli
We propose a novel data augmentation approach, DistractFlow, for training optical flow estimation models by introducing realistic distractions to the input frames.
no code implementations • CVPR 2022 • Jisoo Jeong, Jamie Menjay Lin, Fatih Porikli, Nojun Kwak
Imposing consistency through proxy tasks has been shown to enhance data-driven learning and enable self-supervision in various tasks.
1 code implementation • CVPR 2022 • Jongmok Kim, Jooyoung Jang, Seunghyeon Seo, Jisoo Jeong, Jongkeun Na, Nojun Kwak
Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information.
1 code implementation • 22 Nov 2021 • Jongmok Kim, Jooyoung Jang, Seunghyeon Seo, Jisoo Jeong, Jongkeun Na, Nojun Kwak
Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information.
no code implementations • 9 Mar 2021 • Hakdong Kim, Heonyeong Lim, Minkyu Jee, Yurim Lee, Jisoo Jeong, Kyudam Choi, MinSung Yoon, Cheongwon Kim
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 holographic 3D contents.
1 code implementation • CVPR 2021 • Jisoo Jeong, Vikas Verma, Minsung Hyun, Juho Kannala, Nojun Kwak
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 have not been studied much.
1 code implementation • 17 Feb 2020 • Minsung Hyun, Jisoo Jeong, Nojun Kwak
First, we analyze existing SSL methods in imbalanced environments and examine how the class imbalance affects SSL methods.
2 code implementations • NeurIPS 2019 • Jisoo Jeong, Seungeui Lee, Jeesoo Kim, Nojun Kwak
Making a precise annotation in a large dataset is crucial to the performance of object detection.
Ranked #18 on
Semi-Supervised Object Detection
on COCO 2% labeled data
no code implementations • 19 Nov 2019 • Daesik Kim, Gyujeong Lee, Jisoo Jeong, Nojun Kwak
In the source domain, we fully train an object detector and the RRPN with full supervision of HOI.
no code implementations • ICLR 2019 • Jisoo Jeong, Seungeui Lee, Nojun Kwak
While the conventional methods cannot be applied to the new SSL problems where the separated data do not share the classes, our method does not show any performance degradation even if the classes of unlabeled data are different from those of the labeled data.
1 code implementation • 17 Jul 2017 • Kyoungmin Lee, Jae-Seok Choi, Jisoo Jeong, Nojun Kwak
They are much faster than two stage detectors that use region proposal networks (RPN) without much degradation in the detection performances.
1 code implementation • 30 Jun 2017 • Hyojin Park, Jisoo Jeong, Youngjoon Yoo, Nojun Kwak
Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks.
no code implementations • 26 May 2017 • Jisoo Jeong, Hyojin Park, Nojun Kwak
In this paper, we propose and analyze how to use feature maps effectively to improve the performance of the conventional SSD.