no code implementations • ICCV 2023 • Jeffrey Gu, Kuan-Chieh Wang, Serena Yeung
Neural fields, which represent signals as a function parameterized by a neural network, are a promising alternative to traditional discrete vector or grid-based representations.
no code implementations • 11 May 2023 • Pascal Mettes, Mina Ghadimi Atigh, Martin Keller-Ressel, Jeffrey Gu, Serena Yeung
In this paper, we provide a categorization and in-depth overview of current literature on hyperbolic learning for computer vision.
no code implementations • CVPR 2023 • Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou, João Pedro Araújo, Jeffrey Gu, Karen Liu, Serena Yeung
Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.
1 code implementation • 28 Dec 2022 • Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou, Joao Pedro Araujo, Jeffrey Gu, C. Karen Liu, Serena Yeung
Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.
no code implementations • 8 Jul 2021 • Jeffrey Gu, Serena Yeung
Creating representations of shapes that are invari-ant to isometric or almost-isometric transforma-tions has long been an area of interest in shape anal-ysis, since enforcing invariance allows the learningof more effective and robust shape representations. Most existing invariant shape representations arehandcrafted, and previous work on learning shaperepresentations do not focus on producing invariantrepresentations.
no code implementations • NeurIPS 2021 • Joy Hsu, Jeffrey Gu, Gong-Her Wu, Wah Chiu, Serena Yeung
To that end, we consider encoder-decoder architectures with a hyperbolic latent space, to explicitly capture hierarchical relationships present in subvolumes of the data.
no code implementations • 28 Sep 2020 • Joy Hsu, Jeffrey Gu, Gong Her Wu, Wah Chiu, Serena Yeung
There exists a need for unsupervised 3D segmentation on complex volumetric data, particularly when annotation ability is limited or discovery of new categories is desired.