TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks

TinyAlign optimizes lightweight VLMs, improving mutual information with less data. It features a memory bank for enriched context, excelling in resource-constrained applications. Experiments show superior performance in convergence, alignment, and task execution compared to conventional methods. Six papers focus on multimodal and language models, including MM-VET, TINYGPT-V, MMMU, SIGMOID LOSS, TINYLLAMA, and MINIGPT-4. Kun Zhu et al.'s paper introduces an information bottleneck for noise filtering in retrieval-augmented generation tasks. The discussion covers guidelines for asset documentation, human subject research, and NeurIPS ethics.