The field of multimodal learning is rapidly advancing, with a focus on improving robustness and cultural understanding in vision-language models. Recent developments have highlighted the importance of considering non-additive perturbations, dialectal variations, and cultural biases in facial expression recognition. Researchers are exploring new methodologies, such as contrastive learning and diffusion-based denoising, to enhance the performance of multimodal models. Noteworthy papers include: CoDefend, which proposes a supervised diffusion-based denoising framework for defending against adversarial threats in multimodal models. DialectGen, which benchmarks and improves dialect robustness in multimodal generation, achieving significant performance gains with a general encoder-based mitigation strategy.