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Vision language models (VLM) have demonstrated remarkable performance acrossvarious downstream tasks. However, understanding fine-grained visual-linguisticconcepts, such as attributes and inter-object relationships, remains asignificant challenge. While several benchmarks aim to evaluate VLMs in finergranularity, their primary focus remains on the linguistic aspect, neglectingthe visual dimension. Here, we highlight the importance of evaluating VLMs fromboth a textual and visual perspective. We introduce a progressive pipeline tosynthesize images that vary in a specific attribute while ensuring consistencyin all other aspects. Utilizing this data engine, we carefully design abenchmark, SPEC, to diagnose the comprehension of object size, position,existence, and count. Subsequently, we conduct a thorough evaluation of fourleading VLMs on SPEC. Surprisingly, their performance is close to random guess,revealing significant limitations. With this in mind, we propose a simply yeteffective approach to optimize VLMs in fine-grained understanding, achievingsignificant improvements on SPEC without compromising the zero-shotperformance. Results on two additional fine-grained benchmarks also showconsistent improvements, further validating the transferability of ourapproach.

Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding