Trending Research
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
OCR-free Document Understanding Transformer
clovaai/donut
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Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs.
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
microsoft/biogpt
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Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain.
Ranked #1 on
Question Answering
on PubMedQA
DAMO-YOLO : A Report on Real-Time Object Detection Design
In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO, which achieves higher performance than the state-of-the-art YOLO series.
Ranked #12 on
Real-Time Object Detection
on COCO
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
Scaling Language-Image Pre-training via Masking
We present Fast Language-Image Pre-training (FLIP), a simple and more efficient method for training CLIP.
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
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.
ProGen2: Exploring the Boundaries of Protein Language Models
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design.
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning
MTLab/MorphMLP
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With such multi-dimension and multi-scale factorization, our MorphMLP block can achieve a great accuracy-computation balance.
Ranked #18 on
Action Recognition
on Something-Something V2
(using extra training data)
