Stitching Vision Encoders into LLMs: Clip vs. I-JEPA vs. ViT Comparison (teendifferent.substack.com)

A recent comparative analysis has explored the integration of different vision encoders into large language models (LLMs) to enhance vision-language model (VLM) performance. The study examined three architectures: I-JEPA (Image Joint-Embedding Predictive Architecture), CLIP (Contrastive Language-Image Pre-training), and ViT (Vision Transformer). The research aimed to determine whether the pre-training strategy of a vision encoder has a significant impact on downstream VLM tasks. With clear implications for advancing capabilities in AI systems, the findings reveal that while CLIP excels in object-level language alignment, I-JEPA shows strong potential for compositional reasoning, challenging CLIP's dominance in specific areas. The experiments revealed intriguing outcomes. Though CLIP initially achieved superior performance on tasks requiring object recognition, I-JEPA outperformed it in tasks requiring spatial reasoning, reflecting its ability to capture high-level semantic structures from visual data. With an LLM's scale increasing, CLIP benefited the most, demonstrating that the combination of language-aligned embeddings and a larger model architecture significantly improved performance. The authors suggest that while CLIP remains the go-to for current VLM implementations, I-JEPA's unique strengths in understanding spatial relationships position it as a noteworthy competitor in the evolving landscape of AI.