6 citations · 8 across the 7 of their papers we have counts for
9 papers
Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers
Cong Wei, Brendan Duke, Ruowei Jiang +3
Vision Transformers (ViT) have shown their competitive advantages performance-wise compared to convolutional neural networks (CNNs) though they often come with high computational c…
Exploring Gradient-based Multi-directional Controls in GANs
Zikun Chen, Ruowei Jiang, Brendan Duke +2
Generative Adversarial Networks (GANs) have been widely applied in modeling diverse image distributions. However, despite its impressive applications, the structure of the latent s…
Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers
Robin Kips, Ruowei Jiang, Sileye Ba +4
Augmented reality applications have rapidly spread across online platforms, allowing consumers to virtually try-on a variety of products, such as makeup, hair dying, or shoes. Howe…
LOHO: Latent Optimization of Hairstyles via Orthogonalization
Rohit Saha, Brendan Duke, Florian Shkurti +2
Hairstyle transfer is challenging due to hair structure differences in the source and target hair. Therefore, we propose Latent Optimization of Hairstyles via Orthogonalization (LO…
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
Eu Wern Teh, Terrance DeVries, Brendan Duke +3
We consider the task of semi-supervised semantic segmentation, where we aim to produce pixel-wise semantic object masks given only a small number of human-labeled training examples…
SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
Brendan Duke, Abdalla Ahmed, Christian Wolf +2
In this paper we introduce a Transformer-based approach to video object segmentation (VOS). To address compounding error and scalability issues of prior work, we propose a scalable…
Lightweight Real-time Makeup Try-on in Mobile Browsers with Tiny CNN Models for Facial Tracking
TianXing Li, Zhi Yu, Edmund Phung +3
Recent works on convolutional neural networks (CNNs) for facial alignment have demonstrated unprecedented accuracy on a variety of large, publicly available datasets. However, the…
Nail Polish Try-On: Realtime Semantic Segmentation of Small Objects for Native and Browser Smartphone AR Applications
Brendan Duke, Abdalla Ahmed, Edmund Phung +2
We provide a system for semantic segmentation of small objects that enables nail polish try-on AR applications to run client-side in realtime in native and web mobile applications.…
Generalized Hadamard-Product Fusion Operators for Visual Question Answering
Brendan Duke, Graham W. Taylor
We propose a generalized class of multimodal fusion operators for the task of visual question answering (VQA). We identify generalizations of existing multimodal fusion operators b…