1 code implementation • ECCV 2020 • Sangpil Kim, Hyung-gun Chi, Xiao Hu, Qi-Xing Huang, Karthik Ramani
We introduce a large-scale annotated mechanical components benchmark for classification and retrieval tasks named MechanicalComponents Benchmark (MCB): a large-scale dataset of 3D objects of mechanical components.
2 code implementations • 16 Oct 2023 • Seunggeun Chi, Hyung-gun Chi, QiXing Huang, Karthik Ramani
To overcome this barrier, we introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed for online skeleton-based action recognition.
no code implementations • CVPR 2023 • Hyung-gun Chi, Kwonjoon Lee, Nakul Agarwal, Yi Xu, Karthik Ramani, Chiho Choi
SALF is challenging because it requires understanding the underlying physics of video observations to predict future action locations accurately.
1 code implementation • CVPR 2022 • Hyung-gun Chi, Myoung Hoon Ha, Seunggeun Chi, Sang Wan Lee, QiXing Huang, Karthik Ramani
Human skeleton-based action recognition offers a valuable means to understand the intricacies of human behavior because it can handle the complex relationships between physical constraints and intention.
Ranked #7 on Skeleton Based Action Recognition on N-UCLA
no code implementations • 29 Sep 2021 • Min Liu, Zhiqiang Cai, Karthik Ramani
This paper presents RitzNet, an unsupervised learning method which takes any point in the computation domain as input, and learns a neural network model to output its corresponding function value satisfying the underlying governing PDEs.
no code implementations • 8 Sep 2021 • Sangpil Kim, Jihyun Bae, Hyunggun Chi, Sunghee Hong, Byoung Soo Koh, Karthik Ramani
We introduce a multi-stage framework that uses mean curvature on a hand surface and focuses on learning interaction between hand and object by analyzing hand grasp type for hand action recognition in egocentric videos.
no code implementations • 28 Jul 2019 • Zongyue Zhao, Min Liu, Karthik Ramani
Traditional grid/neighbor-based static pooling has become a constraint for point cloud geometry analysis.
no code implementations • ICLR 2019 • Min Liu, Fupin Yao, Chiho Choi, Sinha Ayan, Karthik Ramani
The ground-breaking performance obtained by deep convolutional neural networks (CNNs) for image processing tasks is inspiring research efforts attempting to extend it for 3D geometric tasks.
no code implementations • 12 Jul 2018 • Sangpil Kim, Nick Winovich, Guang Lin, Karthik Ramani
We propose a fully-convolutional conditional generative model, the latent transformation neural network (LTNN), capable of view synthesis using a light-weight neural network suited for real-time applications.
no code implementations • 12 Dec 2017 • Zhangjie Cao, Qi-Xing Huang, Karthik Ramani
Our main idea is to project a 3D object onto a spherical domain centered around its barycenter and develop neural network to classify the spherical projection.
no code implementations • ICCV 2017 • Chiho Choi, Sang Ho Yoon, Chin-Ning Chen, Karthik Ramani
Our main insight is that the shape of an object causes a configuration of the hand in the form of a hand grasp.
no code implementations • ICCV 2017 • Chiho Choi, Sangpil Kim, Karthik Ramani
As an additional modality to depth data, we present a function of geometric properties on the surface of the hand described by heat diffusion.
1 code implementation • CVPR 2017 • Ayan Sinha, Asim Unmesh, Qi-Xing Huang, Karthik Ramani
3D shape models are naturally parameterized using vertices and faces, \ie, composed of polygons forming a surface.
no code implementations • NeurIPS 2016 • Ayan Sinha, David F. Gleich, Karthik Ramani
Collaborative filtering is a popular technique to infer users' preferences on new content based on the collective information of all users preferences.
no code implementations • CVPR 2016 • Ayan Sinha, Chiho Choi, Karthik Ramani
Our matrix completion algorithm uses these 'spatio-temporal' activation features and the corresponding known pose parameter values to to estimate the unknown pose parameters of the input feature vector.
no code implementations • ICCV 2015 • Chiho Choi, Ayan Sinha, Joon Hee Choi, Sujin Jang, Karthik Ramani
Specifically, we recast the hand pose estimation problem as the cold-start problem for a new user with unknown item ratings in a recommender system.