| --- |
| pretty_name: KnowCP |
| language: |
| - zh |
| license: cc-by-4.0 |
| task_categories: |
| - visual-question-answering |
| task_ids: |
| - visual-question-answering |
| - multiple-choice-qa |
| - document-question-answering |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - chinese-painting |
| - cultural-heritage |
| - multimodal |
| - benchmark |
| configs: |
| - config_name: image |
| default: true |
| data_files: |
| - split: train |
| path: card_samples/parquet/image.parquet |
| --- |
| # KnowCP Dataset Repository |
|
|
| Project homepage and benchmark details: |
| https://41-edu.github.io/KnowCP-Benchmark/ |
|
|
| Script repository: |
| https://github.com/41-edu/KnowCP |
|
|
| ## Project Overview |
|
|
| KnowCP is a comprehensive benchmark designed to evaluate multimodal large language models on the understanding of Chinese paintings, ranging from basic recognition tasks to deep reasoning challenges. |
|
|
| This repository serves as the dataset repository for KnowCP. It stores the image corpus, annotation resources, question sets, and knowledge-base metadata utilized by the benchmark. |
|
|
| ## Dataset Scale |
|
|
| The following statistics are aligned with the configuration of the benchmark website and public content files. |
|
|
| 1. Paintings: 1210 artworks, some of which consist of multiple pages, resulting in a total of 2331 images. |
| 2. Seal Annotation: 8690 annotations, corresponding to seals on 1792 images, so we constructed 1792 seal recognition questions. |
| 3. Inscription Annotation: 2434 annotations, corresponding to inscriptions on 2351 images, so we constructed 2351 inscription recognition questions. |
| 4. Element Annotation: 5048 annotations. |
| 5. Technique Annotation: 1312 annotations. |
| 6. Deep Reasoning: |
| - Visual Analysis: 826 questions |
| - Cultural Context: 716 questions |
| - Provenance Research: 834 questions |
| 7. Total question items used by the benchmark: 24700 |
|
|
| ## Repository Contents |
|
|
| 1. **images**: All painting images and related sub-images used by the benchmark. |
|
|
| 2. **questions**: All benchmark QA files. We provide 14 question files under **questions/by_type**, with each file corresponding to one question source type. All QA content is in Chinese. For the specific presentation style of QA and English-facing examples, refer to **Cases** in https://41-edu.github.io/KnowCP-Benchmark/#distribution |
| |
| - **Foundational Knowledge** |
| |
| - **Image-to-Title (ITT)**: Determine the title of a painting composed of a single page. |
| |
| - ITT data is stored in **questions/by_type/ITT_MHQA_choice.jsonl** and **questions/by_type/ITT_MHQA_fillin.jsonl** |
| |
| - Example: |
| |
| - plaintext |
| |
| ``` |
| {"qid": "IMG000003-P1-Q1", "image_id": "IMG000003", "phase": "P1", "question_no": "Q1", |
| "type": "ITT", "answer_format": "single_choice", |
| "prompt": "请判断该作品标题（中英文对照）是以下哪一项？ |
| A. 白鸽 / Pigeon |
| B. 江村秋晓图 / River Village in Autumn Dawn |
| C. 冬景山水图 / Wintry Mountains |
| D. 画天中华瑞 / Beautiful Scenery of the Tianzhong |
| 请只输出选项字母（A/B/C/D）。 输出格式硬约束： - 只输出一个选项字母（如 A）。 - 禁止输出解释、标点、额外文本。", |
| "ground_truth": "C", |
| "image_paths": ["images/IMG000003_0.jpg"]} |
| ``` |
| |
| - `"type": "ITT"` for **ITT** questions |
| - The image uploaded during runtime is specified in `"image_paths": ["images/IMG000003_0.jpg"]` |
| - The question content is in `"prompt"` |
| - The expected answer is in `"ground_truth"` |
|
|
| - **Multi-Image-to-Title (MITT)**: Determine the title of a painting composed of multiple pages. |
|
|
| - MITT data is stored in **questions/by_type/MITT_MHQA_choice.jsonl** and **questions/by_type/MITT_MHQA_fillin.jsonl** |
|
|
| - Example: |
|
|
| - plaintext |
|
|
| ``` |
| {"qid": "IMG000002-P1-Q1", "image_id": "IMG000002", "phase": "P1", "question_no": "Q1", |
| "type": "MITT", "answer_format": "single_choice", |
| "prompt": "请综合观察这些同一作品的多张图像，判断其标题（中英文对照）是以下哪一项？ |
| A. 春郊游骑图 / A Horseback Trip in a Spring Countryside |
| B. 春山青霁 / Clear Skies on Spring Mountain |
| C. 螽斯绵瓞 / Melon and Katydid |
| D. 山水图（十六开） / Landscapes with Poems (Sixteen Leaves) |
| 请只输出选项字母（A/B/C/D）。 输出格式硬约束： - 只输出一个选项字母（如 A）。 - 禁止输出解释、标点、额外文本。", |
| "ground_truth": "D", |
| "image_paths": ["images/IMG000002_0.jpg", "images/IMG000002_1.jpg", "images/IMG000002_2.jpg"]} |
| ``` |
| |
| - `"type": "MITT"` for **MITT** questions |
| - The images uploaded during runtime are specified in `"image_paths": ["images/IMG000002_0.jpg", "images/IMG000002_1.jpg", "images/IMG000002_2.jpg"]` |
| - The question content is in `"prompt"` |
| - The expected answer is in `"ground_truth"` |
|
|
| - **Title-to-Image (TTI)**: Select the image that matches a given title from multiple images. |
|
|
| - Example: |
|
|
| - plaintext |
|
|
| ``` |
| {"qid": "IMG000001-P1-Q6", "image_id": "IMG000001", "phase": "P1", "question_no": "Q6", |
| "type": "TTI", "answer_format": "single_choice", |
| "prompt": "已知作品标题为：山水图（十二开） / Landscapes and Trees (Twelve Leaves)。请判断它对应下列四张图中的哪一张？ |
| A. 图像A |
| B. 图像B |
| C. 图像C |
| D. 图像D |
| 请只输出选项字母（A/B/C/D）。 输出格式硬约束： - 只输出一个选项字母（如 A）。 - 禁止输出解释、标点、额外文本。", |
| "ground_truth": "A", |
| "image_paths": ["images/IMG000001_0.jpg", "images/IMG000002_0.jpg", "images/IMG000003_0.jpg", "images/IMG000004_0.jpg"]} |
| ``` |
| |
| - `"type": "TTI"` for **TTI** questions |
| - The images uploaded during runtime are specified in `"image_paths": ["images/IMG000001_0.jpg", "images/IMG000002_0.jpg", "images/IMG000003_0.jpg", "images/IMG000004_0.jpg"]}` |
| - The question content is in `"prompt"` |
| - The expected answer is in `"ground_truth"` |
|
|
| - **Multi-hop-QA (MHQA)**: QA on the metadata of each painting. |
|
|
| - MHQA data is stored in **questions/by_type/ITT_MHQA_choice.jsonl**, **questions/by_type/ITT_MHQA_fillin.jsonl**, **questions/by_type/MITT_MHQA_choice.jsonl** and **questions/by_type/MITT_MHQA_fillin.jsonl** |
|
|
| - MHQA includes metadata QA for paintings with multiple pages and single pages. This QA is a sequential question-and-answer process covering four aspects: author, dynasty, museum, and medium. When asking a new question, we append the context of the previous question to the prompt. |
|
|
| - Example: |
|
|
| - plaintext |
|
|
| ``` |
| {"qid": "IMG000003-P1-Q2", "image_id": "IMG000003", "phase": "P1", "question_no": "Q2", |
| "type": "ITT_MHQA_choice", "answer_format": "single_choice", |
| "prompt": "该作品的作者是哪一项？ A. 龚贤 B. 樊圻 C. 吴宏 D. 高岑 请只输出选项字母（A/B/C/D）。 输出格式硬约束： - 只输出一个选项字母（如 A）。 - 禁止输出解释、标点、额外文本。", |
| "ground_truth": "A", |
| "image_paths": ["images/IMG000003_0.jpg"]} |
| |
| {"qid": "IMG000003-P1-Q3", "image_id": "IMG000003", "phase": "P1", "question_no": "Q3", |
| "type": "ITT_MHQA_choice", "answer_format": "single_choice", |
| "prompt": "该作品的朝代是哪一项？ A. 清 B. 晋 C. 金 D. 明 请只输出选项字母（A/B/C/D）。 输出格式硬约束： - 只输出一个选项字母（如 A）。 - 禁止输出解释、标点、额外文本。", |
| "ground_truth": "A", |
| "image_paths": ["images/IMG000003_0.jpg"]} |
| |
| {"qid": "IMG000003-P1-Q4", "image_id": "IMG000003", "phase": "P1", "question_no": "Q4", |
| "type": "ITT_MHQA_choice", "answer_format": "single_choice", |
| "prompt": "该作品的收藏机构为哪一项？ A. 佛利尔·赛克勒美术馆 B. 东京艺术大学 C. 东京国立博物馆 D. 大都会博物馆 请只输出选项字母（A/B/C/D）。 输出格式硬约束： - 只输出一个选项字母（如 A）。 - 禁止输出解释、标点、额外文本。", |
| "ground_truth": "D", |
| "image_paths": ["images/IMG000003_0.jpg"]} |
| |
| {"qid": "IMG000003-P1-Q5", "image_id": "IMG000003", "phase": "P1", "question_no": "Q5", |
| "type": "ITT_MHQA_choice", "answer_format": "single_choice", |
| "prompt": "该作品的材质是哪一项？ A. 棉布 B. 绫本 C. 纸本 D. 金笺 请只输出选项字母（A/B/C/D）。 输出格式硬约束： - 只输出一个选项字母（如 A）。 - 禁止输出解释、标点、额外文本。", |
| "ground_truth": "C", |
| "image_paths": ["images/IMG000003_0.jpg"]} |
| ``` |
| |
| - This is the structure of an entire sequence of MHQA questions. This type includes four sub-types: `ITT_MHQA_choice`, `ITT_MHQA_fillin`, `MITT_MHQA_choice`, and `MITT_MHQA_fillin`. |
|
|
| - The following is an example of a prompt with appended context: |
|
|
| - plaintext |
|
|
| ``` |
| {"qid": "IMG000003-P1-Q2", "image_id": "IMG000003", "phase": "P1", "question_no": "Q2", |
| "type": "ITT_MHQA_choice", "answer_format": "single_choice", |
| "prompt": "以下是同一作品前序问答，请结合上下文回答当前问题： |
| 第1轮问题： 请判断该作品标题（中英文对照）是以下哪一项？ A. 瓮牖图 / Round Window Scroll B. 秋江钓艇 / Fishing Boat on Autumn River C. 归渔图 / Returning Fishermen D. 冬景山水图 / Wintry Mountains 请只输出选项字母（A/B/C/D）。 输出格式硬约束： - 只输出一个选项字母（如 A）。 - 禁止输出解释、标点、额外文本。 |
| 第1轮回答：D |
| 当前问题： 该作品的作者是哪一项？ A. 龚贤 B. 樊圻 C. 吴宏 D. 高岑 请只输出选项字母（A/B/C/D）。 输出格式硬约束： - 只输出一个选项字母（如 A）。 - 禁止输出解释、标点、额外文本。", |
| "ground_truth": "A", |
| "image_paths": ["images/IMG000003_0.jpg"], |
| "request_image_paths": ["images\\IMG000003_0.jpg"], "answer": "A", "raw_answer": "A", "answer_structured": {"text": "A"}, "error": "", "skipped": false} |
| ``` |
| |
| - **Visual Content** |
|
|
| - **Seal-Recognition (SR)** |
|
|
| - Example |
|
|
| - plaintext |
|
|
| ``` |
| {"qid": "IMG000001-P2-Q1-Q01-IMG01", "image_id": "IMG000001", "phase": "P2", "question_no": "Q1", |
| "type": "SR", "answer_format": "json", |
| "prompt": "请检测这张图中的所有印章。 输出必须是 JSON 数组，每个元素代表一个印章。 每个元素至少包含字段：bbox、text。 - bbox: 印章边界框坐标（具体坐标格式将由系统补充说明）。 - text: 该印章印文；无法识别时可填空字符串。 - text 字段请统一输出简体中文；若识别到繁体或异体字，请在不改变语义的前提下尽量转换为简体。 请只输出 JSON，不要输出解释。 输出格式硬约束： - 只输出合法 JSON（对象或数组）。 - 禁止输出 Markdown 代码块、解释性文字。\ \ 坐标协议： bbox 使用 [x_min, y_min, x_max, y_max]，坐标为 0~1 归一化浮点数（voc_xyxy_norm_01）。 输出约束： 1) 仅输出 JSON 数组。 2) 每个元素必须包含 bbox 和 text 字段。 3) 字段名必须是 text，不要使用 text_content / textContent。 4) 坐标必须满足 x_min < x_max 且 y_min < y_max。 5) 未检测到目标时输出 []。", |
| "ground_truth": {"task": "seal_detection", "items": [{"bbox": [0.587156, 0.735297, 0.630989, 0.85107], "text": "印章", "sub_image_path": "images/IMG000001_0.jpg"}, {"bbox": [0.01631, 0.830936, 0.057085, 0.926575], "text": "印章", "sub_image_path": "images/IMG000001_0.jpg"}], "texts_from_seals_json": ["印章"], "image_paths": ["images/IMG000001_0.jpg"]}, |
| "image_paths": ["images/IMG000001_0.jpg"]} |
| ``` |
| |
| - The question is in `"prompt"`, which requires the model to output the bounding boxes of all seals in the image. |
|
|
| - The expected answer is in `"ground_truth"`, which contains the annotation results from our annotators as the ground truth. |
|
|
| - **Inscription-Recognition (IR)** |
|
|
| - Example |
|
|
| - plaintext |
|
|
| ``` |
| {"qid": "IMG000012-P2-Q2-IMG01", "image_id": "IMG000012", "phase": "P2", "question_no": "Q2", |
| "type": "IR", "answer_format": "json", |
| "prompt": "请检测这张图中的所有题跋/款识文字区域。 输出必须是 JSON 数组，每个元素代表一个题跋区域。 每个元素至少包含字段：bbox、text。 - bbox: 题跋区域边界框（具体坐标格式将由系统补充说明）。 - text: 该区域识别出的题跋文字；无法识别时可填空字符串。 - text 字段请统一输出简体中文；若识别到繁体或异体字，请在不改变语义的前提下尽量转换为简体。 请只输出 JSON，不要输出解释。 输出格式硬约束： - 只输出合法 JSON（对象或数组）。 - 禁止输出 Markdown 代码块、解释性文字。\ \ 坐标协议： bbox 使用 [x_min, y_min, x_max, y_max]，坐标为 0~1 归一化浮点数（voc_xyxy_norm_01）。 输出约束： 1) 仅输出 JSON 数组。 2) 每个元素必须包含 bbox 和 text 字段。 3) 字段名必须是 text，不要使用 text_content / textContent。 4) 坐标必须满足 x_min < x_max 且 y_min < y_max。 5) 未检测到目标时输出 []。", |
| "ground_truth": {"task": "inscription_detection", "items": [{"bbox": [0.076534, 0.037318, 0.199841, 0.249891], "text": "太古蛰龙醒，蚕丛霹雳开。五溪云不去，三峡雪飞来。瞿山清。", "text_gt_available": true, "sub_image_path": "images/IMG000012_0.jpg"}], "kb_inscription": "太古蛰龙醒，蚕丛霹雳开。五溪云不去，三峡雪飞来。瞿山清。", "image_paths": ["images/IMG000012_0.jpg"]}, |
| "image_paths": ["images/IMG000012_0.jpg"]} |
| ``` |
| |
| - The question is in `"prompt"`, which requires the model to output the bounding boxes of all inscriptions in the image. |
|
|
| - The expected answer is in `"ground_truth"`, which contains the annotation results from our annotators as the ground truth. |
|
|
| - **Element-Recognition (ER)**: Provided in fill-in formats. |
|
|
| - Example |
|
|
| - plaintext |
|
|
| ``` |
| {"qid": "IMG000001-P2-Q3_O001_L2_FILLIN", "image_id": "IMG000001", "phase": "P2", "question_no": "Q3_O001_L2_FILLIN", |
| "type": "ER_fillin", "answer_format": "text", |
| "prompt": "请观察红框中的主体物象，并判断它具体是什么。 请只输出一个最合适的物象名称（例如：松树、拱桥、高士），不要输出路径或解释。 输出格式硬约束： - 仅输出最终答案，不要解释。", |
| "ground_truth": "茅屋", |
| "image_paths": ["images/IMG000001_4.jpg"], "focus_bbox": [213.36073997944501, 233.20400194911036, 384.7893114080165, 407.0991715277332], "focus_image_index": 5, "focus_sub_image_path": "images/IMG000001_4.jpg"} |
| ``` |
| |
| - The uploaded materials include the image itself (`"image_paths": ["images/IMG000001_4.jpg"]`) and a red annotation box (`"focus_bbox": [213.36073997944501, 233.20400194911036, 384.7893114080165, 407.0991715277332]`). |
|
|
| - The question is in `"prompt"`, which requires the model to identify the object within the given red box. |
|
|
| - The expected answer is in `"ground_truth"`. |
|
|
| - **Technique-Recognition (TR)**: Provided in fill-in formats. |
|
|
| - Example |
|
|
| - plaintext |
|
|
| ``` |
| {"qid": "IMG000001-P3-Q1_R001", "image_id": "IMG000001", "phase": "P3", "question_no": "Q1_R001", |
| "type": "TR_fillin", "answer_format": "text", |
| "prompt": "请观察红框区域，判断主要绘画技法。 请直接输出技法名称。 输出格式硬约束： - 仅输出最终答案，不要解释。", |
| "ground_truth": "披麻皴", |
| "image_paths": ["images/IMG000001_0.jpg"], "focus_bbox": [158.2181562604731, 537.6836388751759, 783.4618818175318, 727.151434498527], "focus_image_index": 1, "focus_sub_image_path": "images/IMG000001_0.jpg"} |
| ``` |
| |
| - The uploaded materials include the image itself (`"image_paths": ["images/IMG000001_0.jpg"]]`) and a red annotation box (`"focus_bbox": [158.2181562604731, 537.6836388751759, 783.4618818175318, 727.151434498527]`). |
|
|
| - The question is in `"prompt"`, which requires the model to identify the painting technique used in the given red box. |
|
|
| - The expected answer is in `"ground_truth"`. |
|
|
| - **Deep Reasoning** |
|
|
| - **Visual-Analysis (VA)** |
|
|
| - Example |
|
|
| - plaintext |
|
|
| ``` |
| {"qid": "IMG000002-P4-Q3A", "image_id": "IMG000002", "phase": "P4", "question_no": "Q3A", |
| "type": "VA", "answer_format": "text", |
| "prompt": "请客观描述画面主要内容、主体关系与关键视觉要素。 要求： 1）仅基于图像本身描述，不引入外部史料。 2）控制在 200 字以内。 3）优先覆盖主体、动作、空间关系。 输出格式硬约束： - 仅输出最终答案，不要解释。", |
| "ground_truth": "本册笔墨清雅，描摹精妙，所写奇山异水各具风致。第一开绘一座半岛伸入湖中，三面环水，对岸群山间垂瀑而下，景物安排繁简得当，形成含蓄悠远的空间效果。第三开写近处山泉、石桥，对岸层峦叠嶂，半山有洞门半开，境界如仙。第六开则绘江边高台，四面空阔，山光与清风之感相互生发，景象疏朗高远。 全册山石树木多经反复烘染，呈现出阴阳分明而又朦胧混沌的视觉效果。程正揆所谓“用笔墨如龙驭风，如云行空，隐现变幻”，点出了其笔墨不以雄强取胜、而以韵致见长的特点。这种不可捉摸、恍惚迷幻的笔墨表现，构成了龚贤绘画特有的意境。", |
| "image_paths": ["images/IMG000002_0.jpg", "images/IMG000002_1.jpg", "images/IMG000002_2.jpg"]} |
| ``` |
| |
| - The question is in `"prompt"`, which requires the model to provide a textual description of the visual information of the painting. |
|
|
| - The expected answer is in `"ground_truth"`, which is a carefully compiled text by experts in Chinese painting. |
|
|
| - **Cultural-Context (CC)** |
|
|
| - Example |
|
|
| - plaintext |
|
|
| ``` |
| {"qid": "IMG000002-P4-Q3B", "image_id": "IMG000002", "phase": "P4", "question_no": "Q3B", |
| "type": "CC", "answer_format": "text", |
| "prompt": "请结合题材来源与图文对应关系，解释这幅作品的文化含义。 要求： 1）说明图像元素与题材文本的对应。 2）控制在 220 字以内。 3）避免泛泛而谈。 输出格式硬约束： - 仅输出最终答案，不要解释。", |
| "ground_truth": "第六开涉及昭明太子题材。昭明太子萧统在文学史上享有盛名，南京、镇江、常熟等地都流传其读书遗迹，虽多有附会，但这一文化记忆为画中“江边高台、四面空阔”的景象提供了可供联想的人文背景，使画面不仅是山水描写，也带有追怀古人、寄托文雅想象的意味。 有研究者对册中诗题作过考释，认为此册“诗画参读，极耐玩味”，体现出画与诗的紧密结合。册后两开画跋又集中阐述龚贤关于“作画”与“识画”关系的理论，强调“未学画先知看画”，将“看画”视为作画精进的重要前提。这些诗题与跋文共同构成了理解此册的重要文本资源，有助于从题咏、画论与观画方法的层面把握作品意涵。", |
| "image_paths": ["images/IMG000002_0.jpg", "images/IMG000002_1.jpg", "images/IMG000002_2.jpg"]} |
| ``` |
| |
| - The question is in `"prompt"`, which requires the model to provide an explanation of the cultural background of the painting. |
|
|
| - The expected answer is in `"ground_truth"`, which is a document verified by experts in Chinese painting through multiple sources. |
|
|
| - **Provenance-Research (PR)** |
|
|
| - Example |
|
|
| - plaintext |
|
|
| ``` |
| {"qid": "IMG000002-P4-Q3C", "image_id": "IMG000002", "phase": "P4", "question_no": "Q3C", |
| "type": "PR", "answer_format": "text", |
| "prompt": "请概述这幅作品的版本流传、归属争议与断代判断。 要求： 1）覆盖款识/著录/收藏或学界判断中的关键点。 2）控制在 200 字以内。 3）结论需有依据，不要编造来源。 输出格式硬约束： - 仅输出最终答案，不要解释。", "ground_truth": "本册绘于康熙二十七年（1688），为龚贤去世前一年的重要作品之一，在其晚年创作中具有代表性。程正揆称龚贤“盖以韵胜，不以力雄者也”，这一评价也提示了此册在龚贤艺术风格中的位置，即以“韵胜”为区别于同时代画家的重要特征。 册后跋文提及宋代宣和内府、倪瓒清閟阁、顾瑛玉山草堂等历史著名藏画之所，虽主要用于申说“识画”理论，但同时关联到古代鉴藏传统与绘画鉴识史的脉络。据钤印可知，本册曾经金城、张学良等人鉴藏。", |
| "image_paths": ["images/IMG000002_0.jpg", "images/IMG000002_1.jpg", "images/IMG000002_2.jpg"]} |
| ``` |
| |
| - The question is in `"prompt"`, which requires the model to provide a textual description of the provenance of the painting. |
|
|
| - The expected answer is in `"ground_truth"`, which is a document verified by experts in Chinese painting through multiple sources. |
|
|
| 3. **annotations** |
|
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| Fine-grained annotations produced by our annotators for each painting image, including seals, inscriptions, elements, and techniques. |
|
|
| - Annotation visualization references: https://41-edu.github.io/KnowCP-Benchmark/#distribution |
|
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| 4. **kb** |
|
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| Core metadata per painting, including identity and background attributes used by benchmark tasks. |
|
|
| ## If You Want to Run Evaluation |
|
|
| This repository provides data only. |
|
|
| For actual model benchmarking and score computation, please use the script repository: |
|
|
| https://github.com/41-edu/KnowCP |
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|
