  Is vision good enough for language? Recent advancements in multimodal models
primarily stem from the powerful reasoning abilities of large language models
(LLMs). However, the visual component typically depends only on the
instance-level contrastive language-image pre-training (CLIP). Our research
reveals that the visual capabilities in recent multimodal LLMs (MLLMs) still
exhibit systematic shortcomings. To understand the roots of these errors, we
explore the gap between the visual embedding space of CLIP and vision-only
self-supervised learning. We identify ''CLIP-blind pairs'' - images that CLIP
perceives as similar despite their clear visual differences. With these pairs,
we construct the Multimodal Visual Patterns (MMVP) benchmark. MMVP exposes
areas where state-of-the-art systems, including GPT-4V, struggle with
straightforward questions across nine basic visual patterns, often providing
incorrect answers and hallucinated explanations. We further evaluate various
CLIP-based vision-and-language models and found a notable correlation between
visual patterns that challenge CLIP models and those problematic for multimodal
LLMs. As an initial effort to address these issues, we propose a Mixture of
Features (MoF) approach, demonstrating that integrating vision self-supervised
learning features with MLLMs can significantly enhance their visual grounding
capabilities. Together, our research suggests visual representation learning
remains an open challenge, and accurate visual grounding is crucial for future
successful multimodal systems.
