|
|
--- |
|
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language: |
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- en |
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|
pretty_name: 'Comics: Pick-A-Panel' |
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tags: |
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- comics |
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|
dataset_info: |
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|
- config_name: caption_relevance |
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|
features: |
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- name: sample_id |
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|
dtype: string |
|
|
- name: context |
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|
sequence: image |
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|
- name: options |
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|
sequence: image |
|
|
- name: index |
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|
dtype: int32 |
|
|
- name: solution_index |
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|
dtype: int32 |
|
|
- name: split |
|
|
dtype: string |
|
|
- name: task_type |
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|
dtype: string |
|
|
- name: previous_panel_caption |
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|
dtype: string |
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|
splits: |
|
|
- name: val |
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|
num_bytes: 530485241 |
|
|
num_examples: 262 |
|
|
- name: test |
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|
num_bytes: 1670410617 |
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|
num_examples: 932 |
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|
download_size: 2200220497 |
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|
dataset_size: 2200895858 |
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|
- config_name: char_coherence |
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|
features: |
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|
- name: sample_id |
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|
dtype: string |
|
|
- name: context |
|
|
sequence: image |
|
|
- name: options |
|
|
sequence: image |
|
|
- name: index |
|
|
dtype: int32 |
|
|
- name: solution_index |
|
|
dtype: int32 |
|
|
- name: split |
|
|
dtype: string |
|
|
- name: task_type |
|
|
dtype: string |
|
|
- name: previous_panel_caption |
|
|
dtype: string |
|
|
splits: |
|
|
- name: val |
|
|
num_bytes: 379249617 |
|
|
num_examples: 143 |
|
|
- name: test |
|
|
num_bytes: 1139813763 |
|
|
num_examples: 489 |
|
|
- name: train |
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|
num_bytes: 27257333324.718 |
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|
num_examples: 10157 |
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|
download_size: 22180995422 |
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|
dataset_size: 28776396704.718 |
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|
- config_name: sequence_filling |
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|
features: |
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|
- name: sample_id |
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|
dtype: string |
|
|
- name: context |
|
|
sequence: image |
|
|
- name: options |
|
|
sequence: image |
|
|
- name: index |
|
|
dtype: int32 |
|
|
- name: solution_index |
|
|
dtype: int32 |
|
|
- name: split |
|
|
dtype: string |
|
|
- name: task_type |
|
|
dtype: string |
|
|
- name: previous_panel_caption |
|
|
dtype: string |
|
|
splits: |
|
|
- name: val |
|
|
num_bytes: 1230082746 |
|
|
num_examples: 262 |
|
|
- name: test |
|
|
num_bytes: 3889446893 |
|
|
num_examples: 932 |
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|
download_size: 4961489402 |
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|
dataset_size: 5119529639 |
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|
- config_name: text_closure |
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|
features: |
|
|
- name: sample_id |
|
|
dtype: string |
|
|
- name: context |
|
|
sequence: image |
|
|
- name: options |
|
|
sequence: image |
|
|
- name: index |
|
|
dtype: int32 |
|
|
- name: solution_index |
|
|
dtype: int32 |
|
|
- name: split |
|
|
dtype: string |
|
|
- name: task_type |
|
|
dtype: string |
|
|
- name: previous_panel_caption |
|
|
dtype: string |
|
|
splits: |
|
|
- name: test |
|
|
num_bytes: 2839781239 |
|
|
num_examples: 924 |
|
|
- name: val |
|
|
num_bytes: 886890050 |
|
|
num_examples: 259 |
|
|
download_size: 4657519865 |
|
|
dataset_size: 3726671289 |
|
|
- config_name: visual_closure |
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|
features: |
|
|
- name: sample_id |
|
|
dtype: string |
|
|
- name: context |
|
|
sequence: image |
|
|
- name: options |
|
|
sequence: image |
|
|
- name: index |
|
|
dtype: int32 |
|
|
- name: solution_index |
|
|
dtype: int32 |
|
|
- name: split |
|
|
dtype: string |
|
|
- name: task_type |
|
|
dtype: string |
|
|
- name: previous_panel_caption |
|
|
dtype: string |
|
|
splits: |
|
|
- name: val |
|
|
num_bytes: 1356539432 |
|
|
num_examples: 300 |
|
|
- name: test |
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|
num_bytes: 4020998551 |
|
|
num_examples: 1000 |
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|
download_size: 10043154153 |
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|
dataset_size: 5377537983 |
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|
configs: |
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- config_name: caption_relevance |
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data_files: |
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|
- split: val |
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|
path: caption_relevance/val-* |
|
|
- split: test |
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|
path: caption_relevance/test-* |
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|
- config_name: char_coherence |
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|
data_files: |
|
|
- split: val |
|
|
path: char_coherence/val-* |
|
|
- split: test |
|
|
path: char_coherence/test-* |
|
|
- split: train |
|
|
path: char_coherence/train-* |
|
|
- config_name: sequence_filling |
|
|
data_files: |
|
|
- split: val |
|
|
path: sequence_filling/val-* |
|
|
- split: test |
|
|
path: sequence_filling/test-* |
|
|
- config_name: text_closure |
|
|
data_files: |
|
|
- split: val |
|
|
path: text_closure/val-* |
|
|
- split: test |
|
|
path: text_closure/test-* |
|
|
- config_name: visual_closure |
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data_files: |
|
|
- split: val |
|
|
path: visual_closure/val-* |
|
|
- split: test |
|
|
path: visual_closure/test-* |
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|
license: cc-by-sa-4.0 |
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--- |
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# Comics: Pick-A-Panel |
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This is the dataset for the [ICDAR 2025 Competition on Comics Understanding in the Era of Foundational Models](https://rrc.cvc.uab.es/?ch=31&com=introduction) |
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The competition is hosted in the [Robust Reading Competition website](https://rrc.cvc.uab.es/?ch=31&com=introduction) and the leaderboard is available [here](https://rrc.cvc.uab.es/?ch=31&com=evaluation). |
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The dataset contains five subtask or skills: |
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<details> |
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<summary>Sequence Filling</summary> |
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 |
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Given a sequence of comic panels, a missing panel, and a set of option panels, the task is to pick the panel that best fits the sequence. |
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</details> |
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<details> |
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<summary>Character Coherence, Visual Closure, Text Closure</summary> |
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 |
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These skills require understanding the context sequence to then pick the best panel to continue the story, focusing on the characters, the visual elements, and the text: |
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- Character Coherence: Given a sequence of comic panels, pick the panel from the two options that best continues the story in a coherent with the characters. Both options are the same panel, but the text in the speech bubbles has been swapped. |
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- Visual Closure: Given a sequence of comic panels, pick the panel from the options that best continues the story in a coherent way with the visual elements. |
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- Text Closure: Given a sequence of comic panels, pick the panel from the options that best continues the story in a coherent way with the text. All options are the same panel, but with text in the speech retrieved from different panels. |
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</details> |
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<details> |
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<summary>Caption Relevance</summary> |
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 |
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Given a caption from the previous panel, select the panel that best continues the story. |
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</details> |
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## Loading the Data |
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```python |
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from datasets import load_dataset |
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skill = "sequence_filling" # "sequence_filling", "char_coherence", "visual_closure", "text_closure", "caption_relevance" |
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split = "val" # "val", "test" |
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dataset = load_dataset("VLR-CVC/ComicsPAP", skill, split=split) |
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``` |
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<details> |
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<summary>Map to single images</summary> |
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If your model can only process single images, you can render each sample as a single image: |
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 |
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```python |
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from PIL import Image, ImageDraw, ImageFont |
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import numpy as np |
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from datasets import Features, Value, Image as ImageFeature |
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class SingleImagePickAPanel: |
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def __init__(self, max_size=500, margin=10, label_space=20, font_path=None): |
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if font_path is None: |
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raise ValueError("Font path must be provided. Testing was done with 'Arial.ttf'") |
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self.max_size = max_size |
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self.margin = margin |
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self.label_space = label_space |
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# Add separate font sizes |
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self.label_font_size = 20 |
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self.number_font_size = 24 |
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self.font_path = font_path |
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def resize_image(self, img): |
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"""Resize image keeping aspect ratio if longest edge > max_size""" |
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if max(img.size) > self.max_size: |
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ratio = self.max_size / max(img.size) |
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new_size = tuple(int(dim * ratio) for dim in img.size) |
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return img.resize(new_size, Image.Resampling.LANCZOS) |
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return img |
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def create_mask_panel(self, width, height): |
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"""Create a question mark panel""" |
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mask_panel = Image.new("RGB", (width, height), (200, 200, 200)) |
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draw = ImageDraw.Draw(mask_panel) |
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font_size = int(height * 0.8) |
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try: |
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font = ImageFont.truetype(self.font_path, font_size) |
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except: |
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raise ValueError("Font file not found") |
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text = "?" |
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bbox = draw.textbbox((0, 0), text, font=font) |
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text_x = (width - (bbox[2] - bbox[0])) // 2 |
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text_y = (height - (bbox[3] - bbox[1])) // 2 |
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draw.text((text_x, text_y), text, fill="black", font=font) |
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return mask_panel |
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def draw_number_on_panel(self, panel, number, font): |
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"""Draw number on the bottom of the panel with background""" |
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draw = ImageDraw.Draw(panel) |
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# Get text size |
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bbox = draw.textbbox((0, 0), str(number), font=font) |
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text_width = bbox[2] - bbox[0] |
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text_height = bbox[3] - bbox[1] |
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# Calculate position (bottom-right corner) |
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padding = 2 |
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text_x = panel.size[0] - text_width - padding |
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text_y = panel.size[1] - text_height - padding |
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# Draw semi-transparent background |
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bg_rect = [(text_x - padding, text_y - padding), |
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(text_x + text_width + padding, text_y + text_height + padding)] |
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draw.rectangle(bg_rect, fill=(255, 255, 255, 180)) |
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# Draw text |
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draw.text((text_x, text_y), str(number), fill="black", font=font) |
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return panel |
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def map_to_single_image(self, examples): |
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"""Process a batch of examples from a HuggingFace dataset""" |
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single_images = [] |
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for i in range(len(examples['sample_id'])): |
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# Get context and options for current example |
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context = examples['context'][i] if len(examples['context'][i]) > 0 else [] |
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options = examples['options'][i] |
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# Resize all images |
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context = [self.resize_image(img) for img in context] |
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options = [self.resize_image(img) for img in options] |
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# Calculate common panel size (use median size to avoid outliers) |
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all_panels = context + options |
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if len(all_panels) > 0: |
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widths = [img.size[0] for img in all_panels] |
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heights = [img.size[1] for img in all_panels] |
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panel_width = int(np.median(widths)) |
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panel_height = int(np.median(heights)) |
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# Resize all panels to common size |
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context = [img.resize((panel_width, panel_height)) for img in context] |
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options = [img.resize((panel_width, panel_height)) for img in options] |
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# Create mask panel for sequence filling tasks if needed |
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if 'index' in examples and len(context) > 0: |
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mask_idx = examples['index'][i] |
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mask_panel = self.create_mask_panel(panel_width, panel_height) |
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context.insert(mask_idx, mask_panel) |
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# Calculate canvas dimensions based on whether we have context |
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if len(context) > 0: |
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context_row_width = panel_width * len(context) + self.margin * (len(context) - 1) |
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options_row_width = panel_width * len(options) + self.margin * (len(options) - 1) |
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canvas_width = max(context_row_width, options_row_width) |
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canvas_height = (panel_height * 2 + |
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self.label_space * 2) |
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else: |
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# Only options row for caption_relevance |
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canvas_width = panel_width * len(options) + self.margin * (len(options) - 1) |
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canvas_height = (panel_height + |
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self.label_space) |
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# Create canvas |
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final_image = Image.new("RGB", (canvas_width, canvas_height), "white") |
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draw = ImageDraw.Draw(final_image) |
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try: |
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label_font = ImageFont.truetype(self.font_path, self.label_font_size) |
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number_font = ImageFont.truetype(self.font_path, self.number_font_size) |
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except: |
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raise ValueError("Font file not found") |
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current_y = 0 |
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# Add context section if it exists |
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if len(context) > 0: |
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# Draw "Context" label |
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bbox = draw.textbbox((0, 0), "Context", font=label_font) |
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text_x = (canvas_width - (bbox[2] - bbox[0])) // 2 |
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draw.text((text_x, current_y), "Context", fill="black", font=label_font) |
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current_y += self.label_space |
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# Paste context panels |
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x_offset = (canvas_width - (panel_width * len(context) + |
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self.margin * (len(context) - 1))) // 2 |
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for panel in context: |
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final_image.paste(panel, (x_offset, current_y)) |
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x_offset += panel_width + self.margin |
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current_y += panel_height |
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# Add "Options" label |
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bbox = draw.textbbox((0, 0), "Options", font=label_font) |
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text_x = (canvas_width - (bbox[2] - bbox[0])) // 2 |
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draw.text((text_x, current_y), "Options", fill="black", font=label_font) |
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current_y += self.label_space |
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# Paste options with numbers on panels |
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x_offset = (canvas_width - (panel_width * len(options) + |
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self.margin * (len(options) - 1))) // 2 |
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for idx, panel in enumerate(options): |
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# Create a copy of the panel to draw on |
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panel_with_number = panel.copy() |
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if panel_with_number.mode != 'RGBA': |
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panel_with_number = panel_with_number.convert('RGBA') |
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# Draw number on panel |
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panel_with_number = self.draw_number_on_panel( |
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panel_with_number, |
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idx, |
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number_font |
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) |
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# Paste the panel with number |
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final_image.paste(panel_with_number, (x_offset, current_y), panel_with_number) |
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x_offset += panel_width + self.margin |
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# Convert final_image to PIL Image format (instead of numpy array) |
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single_images.append(final_image) |
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# Prepare batch output |
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examples['single_image'] = single_images |
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return examples |
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|
|
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from datasets import load_dataset |
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|
|
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skill = "sequence_filling" # "sequence_filling", "char_coherence", "visual_closure", "text_closure", "caption_relevance" |
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split = "val" # "val", "test" |
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dataset = load_dataset("VLR-CVC/ComicsPAP", skill, split=split) |
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processor = SingleImagePickAPanel() |
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dataset = dataset.map( |
|
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processor.map_to_single_image, |
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batched=True, |
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batch_size=32, |
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remove_columns=['context', 'options'] |
|
|
) |
|
|
dataset.save_to_disk(f"ComicsPAP_{skill}_{split}_single_images") |
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|
``` |
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</details> |
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|
|
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## Evaluation |
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The evaluation metric for all tasks is the accuracy of the model's predictions. The overall accuracy is calculated as the weighted average of the accuracy of each subtask, with the weights being the number of examples in each subtask. |
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To evaluate on the test set you must submit your predictions to the [Robust Reading Competition website](https://rrc.cvc.uab.es/?ch=31&com=introduction), as a json file with the following structure: |
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|
|
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```json |
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[ |
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{ "sample_id" : "sample_id_0", "correct_panel_id" : 3}, |
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{ "sample_id" : "sample_id_1", "correct_panel_id" : 1}, |
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|
{ "sample_id" : "sample_id_2", "correct_panel_id" : 4}, |
|
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..., |
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] |
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``` |
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|
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Where `sample_id` is the id of the sample, `correct_panel_id` is the prediction of your model as the index of the correct panel in the options. |
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|
<details> |
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|
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<summary>Pseudocode for the evaluation on val set, adapt for your model:</summary> |
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```python |
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skills = { |
|
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"sequence_filling": { |
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|
"num_examples": 262 |
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|
}, |
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|
"char_coherence": { |
|
|
"num_examples": 143 |
|
|
}, |
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|
"visual_closure": { |
|
|
"num_examples": 300 |
|
|
}, |
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|
"text_closure": { |
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|
"num_examples": 259 |
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|
}, |
|
|
"caption_relevance": { |
|
|
"num_examples": 262 |
|
|
} |
|
|
} |
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|
|
|
for skill in skills: |
|
|
dataset = load_dataset("VLR-CVC/ComicsPAP", skill, split="val") |
|
|
correct = 0 |
|
|
total = 0 |
|
|
for example in dataset: |
|
|
# Your model prediction |
|
|
prediction = model.generate(**example) |
|
|
prediction = post_process(prediction) |
|
|
if prediction == example["solution_index"]: |
|
|
correct += 1 |
|
|
total += 1 |
|
|
accuracy = correct / total |
|
|
print(f"Accuracy for {skill}: {accuracy}") |
|
|
|
|
|
assert total == skills[skill]["num_examples"] |
|
|
skills[skill]["accuracy"] = accuracy |
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|
|
|
# Calculate overall accuracy |
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total_examples = sum(skill["num_examples"] for skill in skills.values()) |
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overall_accuracy = sum(skill["num_examples"] * skill["accuracy"] for skill in skills.values()) / total_examples |
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print(f"Overall accuracy: {overall_accuracy}") |
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``` |
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</details> |
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## Baselines |
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_Results and Code for baselines coming on 25/02/2025_ |
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## Citation |
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_coming soon_ |
