1 code implementation • ECCV 2020 • Youngjoong Kwon, Stefano Petrangeli, Dahun Kim, Haoliang Wang, Eunbyung Park, Viswanathan Swaminathan, Henry Fuchs
Second, we introduce a novel loss to explicitly enforce consistency across generated views both in space and in time.
no code implementations • 7 Apr 2024 • Gyeongjin Kang, Younggeun Lee, Eunbyung Park
Neural Radiance Fields (NeRF) have achieved huge success in effectively capturing and representing 3D objects and scenes.
no code implementations • 10 Mar 2024 • Jaemin Oh, Seung Yeon Cho, Seok-Bae Yun, Eunbyung Park, Youngjoon Hong
In this study, we introduce a method based on Separable Physics-Informed Neural Networks (SPINNs) for effectively solving the BGK model of the Boltzmann equation.
1 code implementation • 28 Feb 2024 • Joo Chan Lee, Taejune Kim, Eunbyung Park, Simon S. Woo, Jong Hwan Ko
To tackle all of the above challenges, we propose CRAD, a novel anomaly detection method for representing normal features within a "continuous" memory, enabled by transforming spatial features into coordinates and mapping them to continuous grids.
no code implementations • NeurIPS 2023 • Seungtae Nam, Daniel Rho, Jong Hwan Ko, Eunbyung Park
In this work, we present mip-Grid, a novel approach that integrates anti-aliasing techniques into grid-based representations for radiance fields, mitigating the aliasing artifacts while enjoying fast training time.
1 code implementation • 1 Jan 2024 • Byeonghyeon Lee, Howoong Lee, Usman Ali, Eunbyung Park
Especially, defocus blur is quite common in the images when they are normally captured using cameras.
no code implementations • 1 Jan 2024 • Byeonghyeon Lee, Howoong Lee, Xiangyu Sun, Usman Ali, Eunbyung Park
However, it suffers from severe degradation in the rendering quality if the training images are blurry.
1 code implementation • 25 Nov 2023 • Joo Chan Lee, Daniel Rho, Seungtae Nam, Jong Hwan Ko, Eunbyung Park
Experimental results demonstrate that CAM enhances the performance of neural representation and improves learning stability across a range of signals.
1 code implementation • 22 Nov 2023 • Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, Eunbyung Park
On the other hand, 3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussisan-based representation and adopts the rasterization pipeline to render the images rather than volumetric rendering, achieving very fast rendering speed and promising image quality.
1 code implementation • 13 Sep 2023 • Sanghyeon Kim, Hyunmo Yang, Younghyun Kim, Youngjoon Hong, Eunbyung Park
The recent surge in large-scale foundation models has spurred the development of efficient methods for adapting these models to various downstream tasks.
1 code implementation • CVPR 2023 • Sanghyeon Kim, Eunbyung Park
This paper suggests an alternative approach to building a continuous convolution without neural networks, resulting in more computationally efficient and improved performance.
Ranked #1 on Sequential Image Classification on Sequential MNIST
1 code implementation • 23 Dec 2022 • Joo Chan Lee, Daniel Rho, Jong Hwan Ko, Eunbyung Park
Neural fields, also known as coordinate-based or implicit neural representations, have shown a remarkable capability of representing, generating, and manipulating various forms of signals.
Ranked #2 on Video Reconstruction on UVG
1 code implementation • CVPR 2023 • Daniel Rho, Byeonghyeon Lee, Seungtae Nam, Joo Chan Lee, Jong Hwan Ko, Eunbyung Park
There have been recent studies on how to reduce these computational inefficiencies by using additional data structures, such as grids or trees.
no code implementations • 16 Nov 2022 • Junwoo Cho, Seungtae Nam, Hyunmo Yang, Seok-Bae Yun, Youngjoon Hong, Eunbyung Park
SPINN operates on a per-axis basis instead of point-wise processing in conventional PINNs, decreasing the number of network forward passes.
2 code implementations • 26 Jul 2022 • Namgyu Kang, Byeonghyeon Lee, Youngjoon Hong, Seok-Bae Yun, Eunbyung Park
With the increases in computational power and advances in machine learning, data-driven learning-based methods have gained significant attention in solving PDEs.
1 code implementation • 20 Jul 2022 • Junwoo Cho, Seungtae Nam, Daniel Rho, Jong Hwan Ko, Eunbyung Park
Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations.
1 code implementation • 12 Jan 2022 • Daniel Rho, Junwoo Cho, Jong Hwan Ko, Eunbyung Park
Inspired by standard video compression algorithms, we propose a neural field architecture for representing and compressing videos that deliberately removes data redundancy through the use of motion information across video frames.
no code implementations • 29 Sep 2021 • Dongsun Kim, Sinwoong Yun, Jemin Lee, Eunbyung Park
Recently, multi-agent reinforcement learning (MARL) adopts the centralized training with decentralized execution (CTDE) framework that trains agents using the data from all agents at a centralized server while each agent takes an action from its observation.
1 code implementation • 2 Oct 2019 • John F. J. Mellor, Eunbyung Park, Yaroslav Ganin, Igor Babuschkin, tejas kulkarni, Dan Rosenbaum, Andy Ballard, Theophane Weber, Oriol Vinyals, S. M. Ali Eslami
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804. 01118).
no code implementations • 15 Apr 2019 • Sergei Alyamkin, Matthew Ardi, Alexander C. Berg, Achille Brighton, Bo Chen, Yiran Chen, Hsin-Pai Cheng, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Abhinav Goel, Alexander Goncharenko, Xuyang Guo, Soonhoi Ha, Andrew Howard, Xiao Hu, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Jong Gook Ko, Alexander Kondratyev, Junhyeok Lee, Seungjae Lee, Suwoong Lee, Zichao Li, Zhiyu Liang, Juzheng Liu, Xin Liu, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Hong Hanh Nguyen, Eunbyung Park, Denis Repin, Liang Shen, Tao Sheng, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
1 code implementation • NeurIPS 2019 • Eunbyung Park, Junier B. Oliva
We propose meta-curvature (MC), a framework to learn curvature information for better generalization and fast model adaptation.
no code implementations • 3 Oct 2018 • Sergei Alyamkin, Matthew Ardi, Achille Brighton, Alexander C. Berg, Yiran Chen, Hsin-Pai Cheng, Bo Chen, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Jongkook Go, Alexander Goncharenko, Xuyang Guo, Hong Hanh Nguyen, Andrew Howard, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Alexander Kondratyev, Seungjae Lee, Suwoong Lee, Junhyeok Lee, Zhiyu Liang, Xin Liu, Juzheng Liu, Zichao Li, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Eunbyung Park, Denis Repin, Tao Sheng, Liang Shen, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
no code implementations • ECCV 2018 • Eunbyung Park, Alexander C. Berg
The meta learning is driven by the goal of deep networks that can quickly be adapted to robustly model a particular target in future frames.
2 code implementations • CVPR 2017 • Eunbyung Park, Jimei Yang, Ersin Yumer, Duygu Ceylan, Alexander C. Berg
Instead of taking a 'blank slate' approach, we first explicitly infer the parts of the geometry visible both in the input and novel views and then re-cast the remaining synthesis problem as image completion.
no code implementations • 27 Feb 2017 • Phil Ammirato, Patrick Poirson, Eunbyung Park, Jana Kosecka, Alexander C. Berg
We present a new public dataset with a focus on simulating robotic vision tasks in everyday indoor environments using real imagery.
no code implementations • ICCV 2015 • Licheng Yu, Eunbyung Park, Alexander C. Berg, Tamara L. Berg
In this paper, we introduce a new dataset consisting of 360, 001 focused natural language descriptions for 10, 738 images.
no code implementations • 19 Nov 2015 • Eunbyung Park, Alexander C. Berg
Although deep convolutional neural networks(CNNs) have achieved remarkable results on object detection and segmentation, pre- and post-processing steps such as region proposals and non-maximum suppression(NMS), have been required.
no code implementations • 31 May 2015 • Licheng Yu, Eunbyung Park, Alexander C. Berg, Tamara L. Berg
In this paper, we introduce a new dataset consisting of 360, 001 focused natural language descriptions for 10, 738 images.