矩形面积交作者: Turbo时间限制: 1s章节: 基本练习问题描述平面上有两个矩形它们的边平行于直角坐标系的X轴或Y轴。对于每个矩形我们给出它的一对相对顶点的坐标请你编程算出两个矩形的交的面积。输入说明输入仅包含两行每行描述一个矩形。在每行中给出矩形的一对相对顶点的坐标每个点的坐标都用两个绝对值不超过10^7的实数表示。输出说明输出仅包含一个实数为交的面积保留到小数后两位。#include bits/stdc.h using namespace std; int main() { double x1,y1,x2,y2; double a1,b1,a2,b2; cin x1 y1 x2 y2; cin a1 b1 a2 b2; // 规范矩形1 double l1 min(x1, x2); double r1 max(x1, x2); double b1_ min(y1, y2); double t1 max(y1, y2); // 规范矩形2 double l2 min(a1, a2); double r2 max(a1, a2); double b2_ min(b1, b2); double t2 max(b1, b2); // 交集宽高 double width min(r1, r2) - max(l1, l2); double height min(t1, t2) - max(b1_, b2_); // 防止负数 width max(0.0, width); height max(0.0, height); double area width * height; cout fixed setprecision(2) area; return 0; }总结就是找长度之间的交集 因为一定相交其实就两个情况一个是包含一个不包含 用max和min模拟两个逻辑翻译Multimodallearning is an important research direction in artificial intelligence, aiming to enable models to process different types of data such as text, images, and speech simultaneously. In the real world, humans obtain information through multiple senses, so data from a singlemodalityoften cannot fully represent complex scenarios. Multimodal learning integrates information from differentmodalities, allowing models to gain a more comprehensive understanding. For example, in imagecaptioningtasks, a model must understand visual content and generate correspondingtextualdescriptions. With the advancement of deep learning technologies, multimodal models have achieved significant progress in areas such as visual question answering, cross-modal retrieval, and intelligent assistants.多模态学习是人工智能的重要研究方向旨在让模型能够同时处理文本、图像、语音等不同类型的数据。在现实世界中人类通过多种感官获取信息因此单一模态的数据往往无法完整表征复杂场景。多模态学习通过融合不同模态的信息使模型获得更全面的认知。例如在图像描述生成任务中模型既要理解视觉内容又要生成对应的文字描述。随着深度学习技术的发展多模态模型在视觉问答、跨模态检索、智能助手等领域取得了显著进展。
3.24 OJ
发布时间：2026/6/12 13:34:16
矩形面积交作者: Turbo时间限制: 1s章节: 基本练习问题描述平面上有两个矩形它们的边平行于直角坐标系的X轴或Y轴。对于每个矩形我们给出它的一对相对顶点的坐标请你编程算出两个矩形的交的面积。输入说明输入仅包含两行每行描述一个矩形。在每行中给出矩形的一对相对顶点的坐标每个点的坐标都用两个绝对值不超过10^7的实数表示。输出说明输出仅包含一个实数为交的面积保留到小数后两位。#include bits/stdc.h using namespace std; int main() { double x1,y1,x2,y2; double a1,b1,a2,b2; cin x1 y1 x2 y2; cin a1 b1 a2 b2; // 规范矩形1 double l1 min(x1, x2); double r1 max(x1, x2); double b1_ min(y1, y2); double t1 max(y1, y2); // 规范矩形2 double l2 min(a1, a2); double r2 max(a1, a2); double b2_ min(b1, b2); double t2 max(b1, b2); // 交集宽高 double width min(r1, r2) - max(l1, l2); double height min(t1, t2) - max(b1_, b2_); // 防止负数 width max(0.0, width); height max(0.0, height); double area width * height; cout fixed setprecision(2) area; return 0; }总结就是找长度之间的交集 因为一定相交其实就两个情况一个是包含一个不包含 用max和min模拟两个逻辑翻译Multimodallearning is an important research direction in artificial intelligence, aiming to enable models to process different types of data such as text, images, and speech simultaneously. In the real world, humans obtain information through multiple senses, so data from a singlemodalityoften cannot fully represent complex scenarios. Multimodal learning integrates information from differentmodalities, allowing models to gain a more comprehensive understanding. For example, in imagecaptioningtasks, a model must understand visual content and generate correspondingtextualdescriptions. With the advancement of deep learning technologies, multimodal models have achieved significant progress in areas such as visual question answering, cross-modal retrieval, and intelligent assistants.多模态学习是人工智能的重要研究方向旨在让模型能够同时处理文本、图像、语音等不同类型的数据。在现实世界中人类通过多种感官获取信息因此单一模态的数据往往无法完整表征复杂场景。多模态学习通过融合不同模态的信息使模型获得更全面的认知。例如在图像描述生成任务中模型既要理解视觉内容又要生成对应的文字描述。随着深度学习技术的发展多模态模型在视觉问答、跨模态检索、智能助手等领域取得了显著进展。
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