MBZUAI · Notable
Thamar Solorio from the University of Houston presented preliminary work on multimodal representation learning for detecting objectionable content in videos at MBZUAI. The research investigates two multimodal pretraining mechanisms, finding contrastive learning more effective than unimodal representation prediction. The study also assesses the value of common multimodal corpora for this task. Why it matters: This research contributes to the development of AI techniques for content moderation, an important issue for online platforms in the Middle East and globally.
multimodal · pretraining · objectionable content · MBZUAI · contrastive learning
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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 ·
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 ·
Video-ChatGPT is a new multimodal model that combines a video-adapted visual encoder with a large language model (LLM) to enable detailed video understanding and conversation. The authors introduce a new dataset of 100,000 video-instruction pairs for training the model. They also develop a quantitative evaluation framework for video-based dialogue models.