arXiv ·
Researchers introduce PALO, a polyglot large multimodal model with visual reasoning capabilities in 10 major languages including Arabic. A semi-automated translation approach was used to adapt the multimodal instruction dataset from English to the target languages. The models are trained across three scales (1.7B, 7B and 13B parameters) and a multilingual multimodal benchmark is proposed for evaluation.
MBZUAI ·
Manling Li from UIUC proposes a new research direction: Event-Centric Multimodal Knowledge Acquisition, which transforms traditional entity-centric single-modal knowledge into event-centric multi-modal knowledge. The approach addresses challenges in understanding multimodal semantic structures using zero-shot cross-modal transfer (CLIP-Event) and long-horizon temporal dynamics through the Event Graph Model. Li's work aims to enable machines to capture complex timelines and relationships, with applications in timeline generation, meeting summarization, and question answering. Why it matters: This research pioneers a new approach to multimodal information extraction, moving from static entity-based understanding to dynamic, event-centric knowledge acquisition, which is essential for advanced AI applications in understanding complex scenarios.
arXiv ·
A new benchmark, LongShOTBench, is introduced for evaluating multimodal reasoning and tool use in long videos, featuring open-ended questions and diagnostic rubrics. The benchmark addresses the limitations of existing datasets by combining temporal length and multimodal richness, using human-validated samples. LongShOTAgent, an agentic system, is also presented for analyzing long videos, with both the benchmark and agent demonstrating the challenges faced by state-of-the-art MLLMs.
arXiv ·
A new benchmark, ViMUL-Bench, is introduced to evaluate video LLMs across 14 languages, including Arabic, with a focus on cultural inclusivity. The benchmark includes 8k manually verified samples across 15 categories and varying video durations. A multilingual video LLM, ViMUL, is also presented, along with a training set of 1.2 million samples, with both to be publicly released.
arXiv ·
This paper introduces MOTOR, a multimodal retrieval and re-ranking approach for medical visual question answering (MedVQA) that uses grounded captions and optimal transport to capture relationships between queries and retrieved context, leveraging both textual and visual information. MOTOR identifies clinically relevant contexts to augment VLM input, achieving higher accuracy on MedVQA datasets. Empirical analysis shows MOTOR outperforms state-of-the-art methods by an average of 6.45%.