1 code implementation • 11 Apr 2024 • Tiange Luo, Justin Johnson, Honglak Lee
Scalable annotation approaches are crucial for constructing extensive 3D-text datasets, facilitating a broader range of applications.
1 code implementation • NeurIPS 2023 • Tiange Luo, Chris Rockwell, Honglak Lee, Justin Johnson
We introduce Cap3D, an automatic approach for generating descriptive text for 3D objects.
1 code implementation • 31 May 2023 • Yiwei Lyu, Tiange Luo, Jiacheng Shi, Todd C. Hollon, Honglak Lee
Diffusion probabilistic models have shown great success in generating high-quality images controllably, and researchers have tried to utilize this controllability into text generation domain.
no code implementations • 17 Feb 2023 • Yunseok Jang, Sungryull Sohn, Lajanugen Logeswaran, Tiange Luo, Moontae Lee, Honglak Lee
Real-world tasks consist of multiple inter-dependent subtasks (e. g., a dirty pan needs to be washed before it can be used for cooking).
no code implementations • 25 Dec 2022 • Tiange Luo, Honglak Lee, Justin Johnson
On Text2Shape, ShapeGlot, ABO, Genre, and Program Synthetic datasets, Neural Shape Compiler shows strengths in $\textit{Text}$ $\Longrightarrow$ $\textit{Point Cloud}$, $\textit{Point Cloud}$ $\Longrightarrow$ $\textit{Text}$, $\textit{Point Cloud}$ $\Longrightarrow$ $\textit{Program}$, and Point Cloud Completion tasks.
1 code implementation • ICLR 2019 • Tiange Luo, Tianle Cai, Mengxiao Zhang, Siyu Chen, Li-Wei Wang
We next investigate the adversarial examples which 'fool' a CNN with Random Mask.
1 code implementation • ICLR 2020 • Tiange Luo, Kaichun Mo, Zhiao Huang, Jiarui Xu, Siyu Hu, Li-Wei Wang, Hao Su
We address the problem of discovering 3D parts for objects in unseen categories.
1 code implementation • 19 Nov 2019 • Tiange Luo, Tianle Cai, Mengxiao Zhang, Siyu Chen, Di He, Li-Wei Wang
Robustness of convolutional neural networks (CNNs) has gained in importance on account of adversarial examples, i. e., inputs added as well-designed perturbations that are imperceptible to humans but can cause the model to predict incorrectly.
no code implementations • 25 Sep 2019 • Tiange Luo, Tianle Cai, Xiaomeng Zhang, Siyu Chen, Di He, LiWei Wang
We first show that predictions made by the defective CNN are less dependent on textural information, but more on shape information, and further find that adversarial examples generated by the defective CNN appear to have semantic shapes.
2 code implementations • ICCV 2019 • Tiange Luo, Aoxue Li, Tao Xiang, Weiran Huang, Li-Wei Wang
In this paper, we propose to tackle the challenging few-shot learning (FSL) problem by learning global class representations using both base and novel class training samples.
12 code implementations • ECCV 2018 • Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, Li-Wei Wang
In consideration of intrinsic consistency between informativeness of the regions and their probability being ground-truth class, we design a novel training paradigm, which enables Navigator to detect most informative regions under the guidance from Teacher.
Ranked #42 on Fine-Grained Image Classification on FGVC Aircraft