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Found 7 papers, 4 papers with code
AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model
Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data.
SAMVG: A Multi-stage Image Vectorization Model with the Segment-Anything Model
Vector graphics are widely used in graphical designs and have received more and more attention.
Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption
To address these problems, we propose a novel phasic content fusing few-shot diffusion model with directional distribution consistency loss, which targets different learning objectives at distinct training stages of the diffusion model.
Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region
To solve the problem, we propose Compositional Neural Painter, a novel stroke-based rendering framework which dynamically predicts the next painting region based on the current canvas, instead of dividing the image plane uniformly into painting regions.
DocPrompt: Large-scale continue pretrain for zero-shot and few-shot document question answering
In this paper, we propose Docprompt for document question answering tasks with powerful zero-shot and few-shot performance.
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding.
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Semantic entity labeling
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Audio Injection Adversarial Example Attack
We study the problem of audio adversarial example attacks with sparse perturbations.
