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Found 9 papers, 4 papers with code
Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers
Equipped with the learned unstructured attention pattern, sparse attention ViT (Sparsifiner) produces a superior Pareto-optimal trade-off between FLOPs and top-1 accuracy on ImageNet compared to token sparsity.
Exploring Gradient-based Multi-directional Controls in GANs
Generative Adversarial Networks (GANs) have been widely applied in modeling diverse image distributions.
Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers
In this paper we propose a novel framework based on deep learning to build a real-time inverse graphics encoder that learns to map a single example image into the parameter space of a given augmented reality rendering engine.
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
We further show that GIST and RIST can be combined with existing semi-supervised learning methods to boost performance.
LOHO: Latent Optimization of Hairstyles via Orthogonalization
Therefore, we propose Latent Optimization of Hairstyles via Orthogonalization (LOHO), an optimization-based approach using GAN inversion to infill missing hair structure details in latent space during hairstyle transfer.
SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
SST extracts per-pixel representations for each object in a video using sparse attention over spatiotemporal features.
Lightweight Real-time Makeup Try-on in Mobile Browsers with Tiny CNN Models for Facial Tracking
Recent works on convolutional neural networks (CNNs) for facial alignment have demonstrated unprecedented accuracy on a variety of large, publicly available datasets.
Nail Polish Try-On: Realtime Semantic Segmentation of Small Objects for Native and Browser Smartphone AR Applications
We also provide a postprocessing and rendering algorithm for nail polish try-on, which integrates with our semantic segmentation and fingernail base-tip direction predictions.
Generalized Hadamard-Product Fusion Operators for Visual Question Answering
We propose a generalized class of multimodal fusion operators for the task of visual question answering (VQA).
