2023-12-13 更新
Stable diffusion for Data Augmentation in COCO and Weed Datasets
Authors:Boyang Deng
Generative models have increasingly impacted relative tasks, from computer vision to interior design and other fields. Stable diffusion is an outstanding diffusion model that paves the way for producing high-resolution images with thorough details from text prompts or reference images. It will be an interesting topic about gaining improvements for small datasets with image-sparse categories. This study utilized seven common categories and three widespread weed species to evaluate the efficiency of a stable diffusion model. In detail, Stable diffusion was used to generate synthetic images belonging to these classes; three techniques (i.e., Image-to-image translation, Dreambooth, and ControlNet) based on stable diffusion were leveraged for image generation with different focuses. Then, classification and detection tasks were conducted based on these synthetic images, whose performance was compared to the models trained on original images. Promising results have been achieved in some classes. This seminal study may expedite the adaption of stable diffusion models to different fields.
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Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition
Authors:Bingjun Luo, Zewen Wang, Jinpeng Wang, Junjie Zhu, Xibin Zhao, Yue Gao
Illumination variation has been a long-term challenge in real-world facial expression recognition(FER). Under uncontrolled or non-visible light conditions, Near-infrared (NIR) can provide a simple and alternative solution to obtain high-quality images and supplement the geometric and texture details that are missing in the visible domain. Due to the lack of existing large-scale NIR facial expression datasets, directly extending VIS FER methods to the NIR spectrum may be ineffective. Additionally, previous heterogeneous image synthesis methods are restricted by low controllability without prior task knowledge. To tackle these issues, we present the first approach, called for NIR-FER Stochastic Differential Equations (NFER-SDE), that transforms face expression appearance between heterogeneous modalities to the overfitting problem on small-scale NIR data. NFER-SDE is able to take the whole VIS source image as input and, together with domain-specific knowledge, guide the preservation of modality-invariant information in the high-frequency content of the image. Extensive experiments and ablation studies show that NFER-SDE significantly improves the performance of NIR FER and achieves state-of-the-art results on the only two available NIR FER datasets, Oulu-CASIA and Large-HFE.
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