7 code implementations • CVPR 2017 • Raymond A. Yeh, Chen Chen, Teck Yian Lim, Alexander G. Schwing, Mark Hasegawa-Johnson, Minh N. Do
In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data.
1 code implementation • CVPR 2023 • Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A. Yeh, Greg Shakhnarovich
We propose to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through the Jacobian of a differentiable renderer, which we instantiate to be a voxel radiance field.
Ranked #6 on Text to 3D on T$^3$Bench
3 code implementations • ICCV 2017 • Ziwei Liu, Raymond A. Yeh, Xiaoou Tang, Yiming Liu, Aseem Agarwala
We combine the advantages of these two methods by training a deep network that learns to synthesize video frames by flowing pixel values from existing ones, which we call deep voxel flow.
Ranked #2 on Video Prediction on DAVIS 2017
1 code implementation • 8 Sep 2022 • Xiaodan Du, Raymond A. Yeh, Nicholas Kolkin, Eli Shechtman, Greg Shakhnarovich
We propose Fast text2StyleGAN, a natural language interface that adapts pre-trained GANs for text-guided human face synthesis.
1 code implementation • 7 Apr 2022 • Jiahao Li, Greg Shakhnarovich, Raymond A. Yeh
Our method for phrase localization requires no human annotations or additional training.
2 code implementations • 31 Oct 2019 • Iou-Jen Liu, Raymond A. Yeh, Alexander G. Schwing
Sample efficiency and scalability to a large number of agents are two important goals for multi-agent reinforcement learning systems.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • CVPR 2022 • Raymond A. Yeh, Yuan-Ting Hu, Zhongzheng Ren, Alexander G. Schwing
To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision.
1 code implementation • NeurIPS 2019 • Raymond A. Yeh, Yuan-Ting Hu, Alexander G. Schwing
We propose Chirality Nets, a family of deep nets that is equivariant to the "chirality transform," i. e., the transformation to create a chiral pair.
2 code implementations • ICCV 2019 • Khoi-Nguyen C. Mac, Dhiraj Joshi, Raymond A. Yeh, JinJun Xiong, Rogerio S. Feris, Minh N. Do
Fine-grained action detection is an important task with numerous applications in robotics and human-computer interaction.
1 code implementation • NeurIPS 2020 • Iou-Jen Liu, Raymond A. Yeh, Alexander G. Schwing
In contrast, asynchronous methods achieve high throughput but suffer from stability issues and lower sample efficiency due to `stale policies.'
2 code implementations • 30 Nov 2023 • Amber Yijia Zheng, Raymond A. Yeh
Advancements in text-to-image models and fine-tuning methods have led to the increasing risk of malicious adaptation, i. e., fine-tuning to generate harmful unauthorized content.
1 code implementation • 14 Oct 2022 • Renan A. Rojas-Gomez, Teck-Yian Lim, Alexander G. Schwing, Minh N. Do, Raymond A. Yeh
We propose learnable polyphase sampling (LPS), a pair of learnable down/upsampling layers that enable truly shift-invariant and equivariant convolutional networks.
1 code implementation • 24 Nov 2021 • Qian Jiang, Xiaofan Zhang, Deming Chen, Minh N. Do, Raymond A. Yeh
In this work, we propose End-to-end Hardware-aware DNAS (EH-DNAS), a seamless integration of end-to-end hardware benchmarking, and fully automated DNAS to deliver hardware-efficient deep neural networks on various platforms, including Edge GPUs, Edge TPUs, Mobile CPUs, and customized accelerators.
1 code implementation • 19 Mar 2024 • Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy, Daniel Leibovici, Zongyi Li, Boris Bonev, Colin White, Julius Berner, Raymond A. Yeh, Jean Kossaifi, Kamyar Azizzadenesheli, Anima Anandkumar
On complex downstream tasks with limited data, such as fluid flow simulations and fluid-structure interactions, we found CoDA-NO to outperform existing methods on the few-shot learning task by over $36\%$.
1 code implementation • NeurIPS 2023 • Md Ashiqur Rahman, Raymond A. Yeh
In computer vision, models must be able to adapt to changes in image resolution to effectively carry out tasks such as image segmentation; This is known as scale-equivariance.
1 code implementation • 13 Jun 2021 • Renan A. Rojas-Gomez, Raymond A. Yeh, Minh N. Do, Anh Nguyen
Despite unconditional feature inversion being the foundation of many image synthesis applications, training an inverter demands a high computational budget, large decoding capacity and imposing conditions such as autoregressive priors.
1 code implementation • 7 Apr 2022 • Raymond A. Yeh, Yuan-Ting Hu, Mark Hasegawa-Johnson, Alexander G. Schwing
Designing equivariance as an inductive bias into deep-nets has been a prominent approach to build effective models, e. g., a convolutional neural network incorporates translation equivariance.
no code implementations • NeurIPS 2017 • Raymond A. Yeh, JinJun Xiong, Wen-mei W. Hwu, Minh N. Do, Alexander G. Schwing
Textual grounding is an important but challenging task for human-computer interaction, robotics and knowledge mining.
no code implementations • CVPR 2018 • Raymond A. Yeh, Minh N. Do, Alexander G. Schwing
Textual grounding, i. e., linking words to objects in images, is a challenging but important task for robotics and human-computer interaction.
no code implementations • CVPR 2019 • Raymond A. Yeh, Alexander G. Schwing, Jonathan Huang, Kevin Murphy
In this paper, we propose a new generative model for multi-agent trajectory data, focusing on the case of multi-player sports games.
no code implementations • NeurIPS 2020 • Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing
Existing semi-supervised learning (SSL) algorithms use a single weight to balance the loss of labeled and unlabeled examples, i. e., all unlabeled examples are equally weighted.
no code implementations • CVPR 2021 • Yuan-Ting Hu, Jiahong Wang, Raymond A. Yeh, Alexander G. Schwing
Moreover, existing image-based datasets for mesh reconstruction don't permit to study models which integrate temporal information.
no code implementations • 23 Jul 2021 • Iou-Jen Liu, Unnat Jain, Raymond A. Yeh, Alexander G. Schwing
To address this shortcoming, in this paper, we propose cooperative multi-agent exploration (CMAE): agents share a common goal while exploring.
no code implementations • 6 Aug 2021 • Iou-Jen Liu, Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing
We evaluate `semantic tracklets' on the visual multi-agent particle environment (VMPE) and on the challenging visual multi-agent GFootball environment.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 11 Oct 2022 • William Gao, April Wang, Gal Metzer, Raymond A. Yeh, Rana Hanocka
We present TetGAN, a convolutional neural network designed to generate tetrahedral meshes.
no code implementations • CVPR 2023 • Adnan Firoze, Cameron Wingren, Raymond A. Yeh, Bedrich Benes, Daniel Aliaga
We present a novel approach to perform instance segmentation, and counting, for densely packed self-similar trees using a top-view RGB image sequence.
no code implementations • 25 May 2023 • Renan A. Rojas-Gomez, Teck-Yian Lim, Minh N. Do, Raymond A. Yeh
For computer vision, Vision Transformers (ViTs) have become one of the go-to deep net architectures.
no code implementations • 5 Dec 2023 • Boheng Zhao, Rana Hanocka, Raymond A. Yeh
Ambigrams are calligraphic designs that have different meanings depending on the viewing orientation.