预训练模型的调整:AdaCLIP 采用了预训练的 CLIP 模型,并对其进行了适配。CLIP 模型原本用于图像和文本的嵌入学习,但 AdaCLIP 通过学习性提示和辅助数据的结合,将其调整为适合于零样本异常检测的任务。 在多领域的广泛实验验证:AdaCLIP 在覆盖工业和医学领域的 14 个数据集上进行了广泛的实验验证,展示了其在新类别...
$ conda create -n adaclip python=3.7 -y $ conda activate adaclip $ conda install -y pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge $ pip install -r requirements.txt Updatecudatoolkit=11.6with the appropriate CUDA version on your machine...
AdaCLIP incorporates learnable prompts into CLIP and optimizes them through training on auxiliary annotated anomaly detection data. Two types of learnable prompts are proposed: static and dynamic. Static prompts are shared across all images, serving to preliminarily adapt CLIP for ZSAD. In contrast,...
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CLIP(Contrastive Language-Image Pre-training)模型作为连接视觉与文本理解的桥梁,在多模态学习领域占据着举足轻重的地位。CoreNet通过集成高效的训练策略,使得CLIP模型的训练变得更加直观且高效。首先,利用CoreNet内置的大规模预训练数据集,用户...
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Zero-shot anomaly detection (ZSAD) targets the identification of anomalies within images from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveraging a pre-trained vision-language model (VLM), CLIP. AdaCLIP incorporates learnable prompts into CLIP and optimizes them...