552 citations · 816 across the 123 of their papers we have counts for
9 papers · 1 filter
Ego2Act: Evaluating Goal-Directed Manipulation in Egocentric Video Generation
Patrick Amadeus Irawan, Iskandar Muda Rizky Parlambang, Rava Maulana +10
Video generation models are increasingly being explored as world simulators for embodied planning and learning. To do so effectively, these models must not only generate visually a…
LinguDistill: Recovering Linguistic Ability in Vision-Language Models via Selective Cross-Modal Distillation
Patrick Amadeus Irawan, Erland Hilman Fuadi, Shanu Kumar +2
Turning a pretrained language model (LM) into a vision-language model (VLM) through multimodal fine-tuning often erodes its native language ability, a form of catastrophic forgetti…
Vision Language Models are Confused Tourists
Patrick Amadeus Irawan, Ikhlasul Akmal Hanif, Muhammad Dehan Al Kautsar +3
Although the cultural dimension has been one of the key aspects in evaluating Vision-Language Models (VLMs), their ability to remain stable across diverse cultural inputs remains l…
Behind Maya: Building a Multilingual Vision Language Model
Nahid Alam, Karthik Reddy Kanjula, Surya Guthikonda +16
In recent times, we have seen a rapid development of large Vision-Language Models (VLMs). They have shown impressive results on academic benchmarks, primarily in widely spoken lang…
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia
Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz +89
Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often resu…
Can Multimodal LLMs do Visual Temporal Understanding and Reasoning? The answer is No!
Mohamed Fazli Imam, Chenyang Lyu, Alham Fikri Aji
Multimodal Large Language Models (MLLMs) have achieved significant advancements in tasks like Visual Question Answering (VQA) by leveraging foundational Large Language Models (LLMs…
Maya: An Instruction Finetuned Multilingual Multimodal Model
Nahid Alam, Karthik Reddy Kanjula, Surya Guthikonda +16
The rapid development of large Vision-Language Models (VLMs) has led to impressive results on academic benchmarks, primarily in widely spoken languages. However, significant gaps r…
CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image Collections
Mohamed Fazli Imam, Rufael Fedaku Marew, Jameel Hassan +3
In the era of foundation models, CLIP has emerged as a powerful tool for aligning text & visual modalities into a common embedding space. However, the alignment objective used to t…
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo +73
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge pres…