no code implementations • 30 Apr 2024 • Chun Feng, Joy Hsu, Weiyu Liu, Jiajun Wu
We propose the Language-Regularized Concept Learner (LARC), which uses constraints from language as regularization to significantly improve the accuracy of neuro-symbolic concept learners in the naturally supervised setting.
no code implementations • 9 Apr 2024 • Zhenhailong Wang, Joy Hsu, Xingyao Wang, Kuan-Hao Huang, Manling Li, Jiajun Wu, Heng Ji
By casting an image to a text-based representation, we can leverage the power of language models to learn alignment from SVG to visual primitives and generalize to unseen question-answering tasks.
1 code implementation • 24 Oct 2023 • Joy Hsu, Jiayuan Mao, Joshua B. Tenenbaum, Jiajun Wu
We propose the Logic-Enhanced Foundation Model (LEFT), a unified framework that learns to ground and reason with concepts across domains with a differentiable, domain-independent, first-order logic-based program executor.
1 code implementation • 15 May 2023 • Mark Endo, Joy Hsu, Jiaman Li, Jiajun Wu
In order to build artificial intelligence systems that can perceive and reason with human behavior in the real world, we must first design models that conduct complex spatio-temporal reasoning over motion sequences.
no code implementations • 26 Apr 2023 • Renhao Wang, Jiayuan Mao, Joy Hsu, Hang Zhao, Jiajun Wu, Yang Gao
Robots operating in the real world require both rich manipulation skills as well as the ability to semantically reason about when to apply those skills.
no code implementations • CVPR 2023 • Joy Hsu, Jiayuan Mao, Jiajun Wu
Different functional modules in the programs are implemented as neural networks.
1 code implementation • 30 Nov 2022 • Joy Hsu, Jiajun Wu, Noah D. Goodman
In contrast, low-level and high-level visual features from standard computer vision models pretrained on natural images do not support correct generalization.
1 code implementation • LTEDI (ACL) 2022 • Kyle Swanson, Joy Hsu, Mirac Suzgun
Using a dataset of Reddit posts that exhibit stress, we demonstrate the ability of our MCTS algorithm to identify interpretable explanations for a person's feeling of stress in both a context-dependent and context-independent manner.
1 code implementation • CVPR 2021 2021 • Joy Hsu
We showcase DARCNN’sperformance for unsupervised instance segmentation on nu-merous biomedical datasets.
1 code implementation • CVPR 2021 • Joy Hsu, Wah Chiu, Serena Yeung
In the biomedical domain, there is an abundance of dense, complex data where objects of interest may be challenging to detect or constrained by limits of human knowledge.
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.
no code implementations • 22 Sep 2020 • Joy Hsu, Sonia Phene, Akinori Mitani, Jieying Luo, Naama Hammel, Jonathan Krause, Rory Sayres
For instance, Noisy Cross-Validation splits the training data into halves, and has been shown to identify low-quality labels in computer vision tasks; but it has not been applied to medical imaging tasks specifically.