How does the in-domain performance of MMICL on MSCOCO compare to its performance on other standard object dete

This survey presents a comprehensive analysis of the phenomenon of hallucination in multimodal large language models (MLLMs), also known as Large Vision-Language Models (LVLMs), which have demonstrated significant advancements and remarkable abilities in multimodal tasks. Despite these promising developments, MLLMs often generate outputs that are inconsistent with the visual content, a challenge known as hallucination, which poses substantial obstacles to their practical deployment and raises concerns regarding their reliability in real-world applications. This problem has attracted increasing

Research goal: How does the in-domain performance of MMICL on MSCOCO compare to its performance on other standard object detection benchmarks like COCO-Stuff or Visual Genome when using the same recall@K evaluation metrics?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.7/10.