Appearances Page 外观页面

Appearances 页面提供了用于自定义用于绘制 View 元素的外观设置的设计时工具。具体来说,它提供对 View 的 BaseView.Appearance 集合的访问。

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列表框中列出了可用的外观。所选外观的设置由 Properties 选项页中的属性网格列出。所有更改都会立即反映在 Appearance preview 部分。

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如果所选视图不是卡片视图,则 Paint Style 页面还包含 GridOptionsView.EnableAppearanceEvenRow 和 GridOptionsView.EnableAppearanceOddRow 选项,这些选项指定是否分别使用 View 的 GridViewAppearances.EvenRow 和 GridViewAppearances.OddRow 属性提供的外观设置来绘制偶数行和奇数行。

Preview (预览) 窗格允许选择所需的 View 元素。当最终用户在预览窗格中移动鼠标指针时,将突出显示位于鼠标指针下的 View 元素。最终用户单击高亮显示的元素后,Appearances 列表中仅显示用于绘制此元素的外观。例如,如果单击列标题,则仅显示 FixedLine、HeaderPanel 和 HeaderPanelBackground 外观。

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要显示所有可用的外观,请单击按钮 Appearances Page_ShowAllButton 或按 ‘Ctrl’+‘Z’ 组合键。

您可以同时自定义多个外观的设置。按住 SHIFT 或 CTRL 键的同时单击外观名称以选择多个外观对象。要选择所有外观,请单击按钮 AppearancesPage_SelectAll 或按 ‘Ctrl’+‘A’ 组合键。要将所选外观对象的外观设置重置为默认值,请单击 AppearancesPage_Reset button 按钮或按 ‘Ctrl’+‘D’ 组合键。

外观布局(所有 AppearanceObject 对象的设置)可以保存到 XML 文件中,然后应用于其他视图。为此,请使用位于页面顶部的按钮。这些按钮如下所述。

  • 加载外观布局…- 调用“打开”对话框,该对话框允许从 XML 文件加载以前保存的外观布局。
  • 保存外观布局…- 调用“保存”对话框,该对话框允许将当前外观布局保存到 XML 文件。
Yes, Zero-shot learning (ZSL) can indeed be combined with CLIP visual embeddings, appearance features, and semantic embeddings to achieve cross-modal space mapping. This integration leverages the strengths of each component to enhance the model's ability to generalize across unseen classes and modalities. CLIP (Contrastive Language–Image Pre-training) is a multi-modal model that learns to align images and text in a shared embedding space. By utilizing CLIP's visual embeddings, models can benefit from the rich semantic information encoded during pre-training, which facilitates zero-shot transfer to various downstream tasks without requiring additional training data [^2]. The visual embeddings from CLIP capture high-level abstractions that are useful for identifying objects even when they belong to categories not seen during training. Appearance features, which are typically lower-level visual descriptors such as color histograms, texture, or shape information, can complement the higher-level semantic features extracted by CLIP. These appearance features can provide detailed information about the visual characteristics of objects, which might be crucial for distinguishing between similar classes in the zero-shot setting. Semantic embeddings, often derived from word vectors like Word2Vec, GloVe, or BERT, represent the meaning of words and phrases in a continuous vector space. In the context of ZSL, these embeddings serve as the semantic descriptors of both seen and unseen classes. They enable the model to understand the relationships between different classes based on their semantic meanings rather than just their visual appearances [^1]. The combination of these components allows for a more robust cross-modal space mapping where the model can project both visual and textual inputs into a common space. This projection enables the model to match visual inputs with the most appropriate semantic description, even if that particular class was not present in the training set. For example, a model could use CLIP to encode an image into a visual embedding and then compare this embedding against a set of semantic embeddings representing potential class labels. The class with the closest semantic embedding would be selected as the prediction. This process can be further refined by incorporating appearance features that provide additional discriminative power [^3]. Here is a simplified example of how one might use CLIP for zero-shot classification with semantic embeddings: ```python import clip import torch from PIL import Image # Load pre-trained CLIP model and preprocessing function device = "cuda" if torch.cuda.is_available() else "cpu" model, preprocess = clip.load("ViT-B/32", device=device) # Load image image = preprocess(Image.open("zebra.jpg")).unsqueeze(0).to(device) # Define class descriptions text = clip.tokenize(["a photo of a zebra", "a photo of a horse", "a photo of a tiger"]).to(device) # Perform inference with torch.no_grad(): image_features = model.encode_image(image) text_features = model.encode_text(text) logits_per_image, logits_per_text = model(image, text) probs = logits_per_image.softmax(dim=-1).cpu().numpy() print("Zero-Shot classification probabilities:", probs) # Output the matching probabilities between the image and each class ``` In summary, combining Zero-shot learning with CLIP visual embeddings, appearance features, and semantic embeddings offers a powerful approach for cross-modal space mapping. It allows models to recognize new classes by leveraging semantic descriptions and pre-trained knowledge, making it possible to perform tasks without explicit training on those classes.
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