ComicsPAP: understanding comic strips by picking the correct panel
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912619509579776 |
|---|---|
| author | Vivoli, Emanuele Llabrés, Artemis Souibgui, Mohamed Ali Bertini, Marco Llobet, Ernest Valveny Karatzas, Dimosthenis |
| author_facet | Vivoli, Emanuele Llabrés, Artemis Souibgui, Mohamed Ali Bertini, Marco Llobet, Ernest Valveny Karatzas, Dimosthenis |
| contents | Large multimodal models (LMMs) have made impressive strides in image captioning, VQA, and video comprehension, yet they still struggle with the intricate temporal and spatial cues found in comics. To address this gap, we introduce ComicsPAP, a large-scale benchmark designed for comic strip understanding. Comprising over 100k samples and organized into 5 subtasks under a Pick-a-Panel framework, ComicsPAP demands models to identify the missing panel in a sequence. Our evaluations, conducted under both multi-image and single-image protocols, reveal that current state-of-the-art LMMs perform near chance on these tasks, underscoring significant limitations in capturing sequential and contextual dependencies. To close the gap, we adapted LMMs for comic strip understanding, obtaining better results on ComicsPAP than 10x bigger models, demonstrating that ComicsPAP offers a robust resource to drive future research in multimodal comic comprehension. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ComicsPAP: understanding comic strips by picking the correct panel Vivoli, Emanuele Llabrés, Artemis Souibgui, Mohamed Ali Bertini, Marco Llobet, Ernest Valveny Karatzas, Dimosthenis Computer Vision and Pattern Recognition Large multimodal models (LMMs) have made impressive strides in image captioning, VQA, and video comprehension, yet they still struggle with the intricate temporal and spatial cues found in comics. To address this gap, we introduce ComicsPAP, a large-scale benchmark designed for comic strip understanding. Comprising over 100k samples and organized into 5 subtasks under a Pick-a-Panel framework, ComicsPAP demands models to identify the missing panel in a sequence. Our evaluations, conducted under both multi-image and single-image protocols, reveal that current state-of-the-art LMMs perform near chance on these tasks, underscoring significant limitations in capturing sequential and contextual dependencies. To close the gap, we adapted LMMs for comic strip understanding, obtaining better results on ComicsPAP than 10x bigger models, demonstrating that ComicsPAP offers a robust resource to drive future research in multimodal comic comprehension. |
| title | ComicsPAP: understanding comic strips by picking the correct panel |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.08561 |