Trending Research
InstructPix2Pix: Learning to Follow Image Editing Instructions
timothybrooks/instruct-pix2pix
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We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image.
StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis
Text-to-image synthesis has recently seen significant progress thanks to large pretrained language models, large-scale training data, and the introduction of scalable model families such as diffusion and autoregressive models.
Ranked #9 on
Text-to-Image Generation
on COCO
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM).
Learning the Beauty in Songs: Neural Singing Voice Beautifier
MoonInTheRiver/DiffSinger
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ACL 2022
Furthermore, we propose a latent-mapping algorithm in the latent space to convert the amateur vocal tone to the professional one.
K-Planes: Explicit Radiance Fields in Space, Time, and Appearance
We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions.
Hungry Hungry Hippos: Towards Language Modeling with State Space Models
hazyresearch/h3
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First, we use synthetic language modeling tasks to understand the gap between SSMs and attention.
Learning-Rate-Free Learning by D-Adaptation
facebookresearch/dadaptation
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In this work, we describe a single-loop method, with no back-tracking or line searches, which does not require knowledge of $D$ yet asymptotically achieves the optimal rate of convergence for the complexity class of convex Lipschitz functions.
Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling
keyu-tian/spark
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This is the first use of sparse convolution for 2D masked modeling.
Ranked #1 on
Instance Segmentation
on COCO 2017 val
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping by casting blind face restoration as a code prediction task, while providing rich visual atoms for generating high-quality faces.
Ranked #1 on
Blind Face Restoration
on WIDER
Multiview Compressive Coding for 3D Reconstruction
We introduce a simple framework that operates on 3D points of single objects or whole scenes coupled with category-agnostic large-scale training from diverse RGB-D videos.
