Search Results for author:
Found 5 papers, 2 papers with code
MaskSketch: Unpaired Structure-guided Masked Image Generation
We show that intermediate self-attention maps of a masked generative transformer encode important structural information of the input image, such as scene layout and object shape, and we propose a novel sampling method based on this observation to enable structure-guided generation.
Muse: Text-To-Image Generation via Masked Generative Transformers
Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding.
Ranked #1 on
Text-to-Image Generation
on MS-COCO
(FID metric)
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
In this paper, we introduce a random forest semantic hashing scheme that embeds tiny convolutional neural networks (CNN) into shallow random forests, with near-optimal information-theoretic code aggregation among trees.
Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-spectral Hallucination and Low-rank Embedding
We observe that it is often equally effective to perform hallucination to input NIR images or low-rank embedding to output deep features for a VIS deep model for cross-spectral recognition.
Finding Vanishing Points via Point Alignments in Image Primal and Dual Domains
We present a novel method for automatic vanishing point detection based on primal and dual point alignment detection.
