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pro vyhledávání: '"Lee, Hyungtae"'
Recently, 3D Gaussian splatting has gained attention for its capability to generate high-fidelity rendering results. At the same time, most applications such as games, animation, and AR/VR use mesh-based representations to represent and render 3D sce
Externí odkaz:
http://arxiv.org/abs/2410.08941
The potential of synthetic data to replace real data creates a huge demand for synthetic data in data-hungry AI. This potential is even greater when synthetic data is used for training along with a small number of real images from domains other than
Externí odkaz:
http://arxiv.org/abs/2408.14559
Autor:
Yim, Jinsub, Lee, Hyungtae, Eum, Sungmin, Shen, Yi-Ting, Zhang, Yan, Kwon, Heesung, Bhattacharyya, Shuvra S.
We introduce Synthetic Playground (SynPlay), a new synthetic human dataset that aims to bring out the diversity of human appearance in the real world. We focus on two factors to achieve a level of diversity that has not yet been seen in previous work
Externí odkaz:
http://arxiv.org/abs/2408.11814
We present a framework for diversifying human poses in a synthetic dataset for aerial-view human detection. Our method firstly constructs a set of novel poses using a pose generator and then alters images in the existing synthetic dataset to assume t
Externí odkaz:
http://arxiv.org/abs/2405.15939
Aerial-view human detection has a large demand for large-scale data to capture more diverse human appearances compared to ground-view human detection. Therefore, synthetic data can be a good resource to expand data, but the domain gap with real-world
Externí odkaz:
http://arxiv.org/abs/2405.15203
Autor:
Maxey, Christopher, Choi, Jaehoon, Lee, Yonghan, Lee, Hyungtae, Manocha, Dinesh, Kwon, Heesung
In this paper, we present a new approach to bridge the domain gap between synthetic and real-world data for unmanned aerial vehicle (UAV)-based perception. Our formulation is designed for dynamic scenes, consisting of small moving objects or human ac
Externí odkaz:
http://arxiv.org/abs/2405.02762
Tremendous variations coupled with large degrees of freedom in UAV-based imaging conditions lead to a significant lack of data in adequately learning UAV-based perception models. Using various synthetic renderers in conjunction with perception models
Externí odkaz:
http://arxiv.org/abs/2310.16255
Autor:
Hasan, Zahid, Faridee, Abu Zaher Md, Ahmed, Masud, Purushotham, Sanjay, Kwon, Heesung, Lee, Hyungtae, Roy, Nirmalya
Novel Categories Discovery (NCD) aims to cluster novel data based on the class semantics of known classes using the open-world partial class space annotated dataset. As an alternative to the traditional pseudo-labeling-based approaches, we leverage t
Externí odkaz:
http://arxiv.org/abs/2307.03856
Autor:
Hasan, Zahid, Ahmed, Masud, Faridee, Abu Zaher Md, Purushotham, Sanjay, Kwon, Heesung, Lee, Hyungtae, Roy, Nirmalya
Novel Categories Discovery (NCD) facilitates learning from a partially annotated label space and enables deep learning (DL) models to operate in an open-world setting by identifying and differentiating instances of novel classes based on the labeled
Externí odkaz:
http://arxiv.org/abs/2304.07354
To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a
Externí odkaz:
http://arxiv.org/abs/2211.01778