Probing Fundamental Visual Comprehend Capabilities on Vision Language Models via Visual Phrases from Structural Data

Does the model demonstrate exceptional proficiency in “item counting,” “color recognition,” or other Fundamental Visual Comprehension Capability (FVCC)? There have been remarkable advancements in the field of multimodal, the pretrained general Vision Language Models exhibit strong performance across a range of intricate Visual Language (VL) tasks and Multimodal Large Language Models (MLLMs) emerge novel visual reasoning abilities from several examples. But models tend to encounter difficulties when confronted with texts supplemented with specific details by simple visual phrases. Moreover, there is a scarcity of datasets in sufficient quantity, variety, and composability to enable the evaluation of each FVCC using statistical metrics. Accordingly, we decomposed the complete VL task into 9 M simple Visual Phrase Triplets (VPTs) across 16 categories representing 16 distinct FVCCs from the structural scene graph. Then, we reconstructed a Multilevel Scene Graph (MLSG) for each image and introduced our unbiased, balanced, and binary Visual Phrase Entailment benchmark with 20 times the data volume of SNLI-VE. The benchmark consisted of three exams and evaluated the performance of 8 widely used VLM and 10 MLLMs respectively. The results demonstrate the performance of each model across 16 classes in FVCC, as well as their lower and upper limits under conditions of increased text complexity or unnoised image input. Finally, we enhanced the efficiency of MLLM and evoked their In-Context Learning characteristics by appending multiple VPT generated QA pairs of identical types to the conversation history without tuning. The proposed structural VPTs and MLSG data hold promise for facilitating future explorations on FVCC.