BanglaVerse is a new benchmark that evaluates multilingual vision‑language models on Bengali culture, covering nine visual domains and expanding to four languages and five Bangla dialects for a total of about 32,200 artifacts. It includes visual question answering and captioning tasks built from 1,152 manually curated images. Experiments show that models perform worse on dialectal variants and that missing cultural knowledge, rather than visual grounding, is the main bottleneck.
By Nurul Labib Sayeedi, Md. Faiyaz Abdullah Sayeedi, Shubhashis Roy Dipta, Mahbub E Sobhani, Rubaya Tabassum, Ariful Ekraj Hridoy, Mehraj Mahmood, Md. Tarek Hasan, Swakkhar Shatabda
arXiv:2609.00491v1 Announce Type: new
Abstract: Communicating across cultures is inherently challenging, especially through culturally dense and ambiguous formats like memes. While people expect larg...
By Hangxiao Zhu, Suliu Qin, Zhuoyan Li, Ming Jiang, Yu Zhang, Meng Xia
arXiv:2607. 03981v1 Announce Type: cross Abstract: Memes have become influential communication tools on social media, combining viral visuals with concise messaging to convey impactful ideas.
By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Md. Mahfuzur Rahman, Pronay Debnath, Asif Iftekher Fahim, Faisal Muhammad Shah
arXiv:2508. 05502v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) perform strongly in high-resource languages, yet often produce fluent but culturally "thin" descriptions in low-resource settings.
By Yufei Gao, Jiaying Fei, Nuo Chen, Ruirui Chen, Guohang Yan, Yunshi Lan, Botian Shi
The study investigates how large language models (LLMs) handle diverse Indian oral traditions, using the Rajasthani Pabuji epic, Tamil Sangam poetry, and Bengali folk tales as case studies. By prompting Claude Sonnet and Gemini with 54 generation requests across generic, culturally specific, and regional-language prompts, the authors measured reference drift and cross-tradition convergence using Sentence‑BERT embeddings. Results show that while outputs stay closer to their own tradition than to others, there is significant cross‑tradition similarity (0.52–0.66), indicating partial homogenisation; moreover, regional‑language prompting consistently reduced fidelity to authentic traditions.
By Paarth Singh Rathore
The paper introduces CMPM, a Chinese Multi-Panel Meme benchmark comprising 1,214 annotated samples that capture five structural types, ordering dependencies, panel-order constraints, and optional comment context. It defines a two-layer evaluation: Task 1 tests structure typing and order-sensitive panel sequencing, while Task 2 assesses Chinese meme explanation generation using human ratings across visual, panel, humor, context, and faithfulness dimensions. Benchmarking five large vision‑language models shows that accuracy on canonical displays does not guarantee order understanding, as performance drops sharply under shuffled conditions, and that Gemini 3.1 Pro and GPT‑5.5 outperform open models in Task 2, with comment context providing only modest gains.
By Haihan Li, Haihao Li, Zhenfei Xu, Jize Qian
TransMeme introduces a multi‑agent framework for cross‑cultural meme transcreation, addressing the unique challenges of preserving intent, adapting cultural meaning, and maintaining multimodal consistency. The system coordinates specialized agents for cultural adaptation, text rewriting, revision, and visual adjustment, and is evaluated on Chinese‑English meme pairs. Human and LLM‑based evaluations show that TransMeme outperforms baselines, achieving a 33.1% average improvement in human scores and a 60% Top‑1 ranking rate in LLM judgments.
By Jingyi Zheng, Yule Liu, Zifan Peng, Tianyi Hu, Yuemeng Zhao, Xinhu Zheng, Xinlei He
The Cultural Moment Benchmark (CMB) evaluates video cultural reasoning in Southeast Asia by testing three distinct abilities: naming a cultural concept, visually recognizing it in a video, and locating its sub‑events in time. It contains 306 expert‑curated concepts from seven countries across five categories, with each concept assessed through three stages that use semantic‑similarity distractors, unlabeled video moments, and free‑form temporal localization. Experiments on six vision‑language models reveal varied failure modes, limited cascading between abilities, and differing impacts of audio and subtitles, while a human study shows even experts struggle with concepts from neighboring countries.
By Burak Satar, Zhixin Ma, Cheng Yu-Tong, Huy Hoang Tran, Phuong Anh Nguyen, Chong-Wah Ngo
Internet memes are a pervasive form of multimodal online communication; however, such communication often involves users from diverse linguistic and cultural backgrounds. Therefore, adapting memes acr...
arXiv:2606. 05531v1 Announce Type: cross Abstract: Despite the rapid progress of Vision-Language Models (VLMs), the field lacks benchmarks that rigorously diagnose their true reasoning abilities and chart meaningful progress toward human-like multimodal intelligence.
By Mohammad Mahdi Abootorabi, Omid Ghahroodi, Anas Madkoor, Marzia Nouri, Doratossadat Dastgheib, Mohamed Hefeeda, Ehsaneddin Asgari
arXiv:2607. 15442v1 Announce Type: new Abstract: Internet memes intertwine visual cues, textual content, and cultural context, making them particularly challenging to interpret in scenarios where humor, sarcasm, and harmful intent coexist.
By Shanhong Liu, Pai Chet Ng, De Wen Soh, Malika Meghjani, Konstantinos N. Plataniotis
arXiv:2607. 23493v1 Announce Type: cross Abstract: Automated analysis of multimodal content on social networks has become a critical task for understanding public sentiment and information diffusion in the digital age.
By Musa Tur Farazi, Nufayer Jahan Reza