Search Results for author:
Found 10 papers, 6 papers with code
ComiCap: A VLMs pipeline for dense captioning of Comic Panels
The comic domain is rapidly advancing with the development of single- and multi-page analysis and synthesis models.
One missing piece in Vision and Language: A Survey on Comics Understanding
Our contributions are fivefold: (1) We analyze the structure of the comics medium, detailing its distinctive compositional elements; (2) We survey the widely used datasets and tasks in comics research, emphasizing their role in advancing the field; (3) We introduce the Layer of Comics Understanding (LoCU) framework, a novel taxonomy that redefines vision-language tasks within comics and lays the foundation for future work; (4) We provide a detailed review and categorization of existing methods following the LoCU framework; (5) Finally, we highlight current research challenges and propose directions for future exploration, particularly in the context of vision-language models applied to comics.
Towards Generative Class Prompt Learning for Fine-grained Visual Recognition
Specifically, we propose two novel methods: Generative Class Prompt Learning (GCPL) and Contrastive Multi-class Prompt Learning (CoMPLe).
CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding
We introduce a novel benchmark, CoMix, designed to evaluate the multi-task capabilities of models in comic analysis.
Comics Datasets Framework: Mix of Comics datasets for detection benchmarking
We have benchmarked a variety of detection architectures using the Comics Datasets Framework.
Multimodal Transformer for Comics Text-Cloze
This work explores a closure task in comics, a medium where visual and textual elements are intricately intertwined.
Error assessment of microwave holography inversion for shallow buried objects
Holographic imaging is a technique that uses microwave energy to create a three-dimensional image of an object or scene.
CTE: A Dataset for Contextualized Table Extraction
We define the task of Contextualized Table Extraction (CTE), which aims to extract and define the structure of tables considering the textual context of the document.
MUST-VQA: MUltilingual Scene-text VQA
In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion.
Graph Neural Networks and Representation Embedding for Table Extraction in PDF Documents
Tables are widely used in several types of documents since they can bring important information in a structured way.
