Multimodal learning serves as a promising approach for applications with diverse information sources. However, there are numerous challenges when scaling up multimodal learning data from all modalities due to availability or varied cost of data collection. We formulate and study multimodal learning under limited data collection budgets as a modality selection problem, demonstrating that selectively collecting a subset of modalities and synthesizing the missing ones can match or outperform models trained with uniformly augmented full modalities. Our research problem is formulated as: given a limited data collection budget, how to find the appropriate modalities to collect new data and generate for the rest to maximize model performance gain? To answer this, we propose a new paradigm - Select the Key modality, then generate the rest to enable learning with limited data (SK-ll). SK-ll contains two essential components: (1) Select the key. We propose a modality importance indicator to find the optimal modalities by assessing their single modal marginal contribution and cross-modal interactions. (2) Generate the rest. We substitute with generated embeddings for the rest of modalities. We conducted extensive experiments applying SK-ll across affective computing, healthcare with diverse multimodal learning backbones, obtaining average accuracy gains of 2.37%, 6.55%, 6.73% on MOSI, MOSEI, ADNI respectively. Meanwhile, we present interesting empirical insights such as the data efficiency. Codes are provided in https://github.com/UNITES-Lab/SK-ll.

Multi-modal large language models (MLLMs) have recently shown impressive capabilities but are also highly vulnerable to jailbreak attacks. While white-box methods can generate adversarial visual inputs via gradient-based optimization, such approaches fail in realistic black-box settings where model parameters are inaccessible. Zeroth-order (ZO) optimization offers a natural path for black-box attacks by estimating gradients from queries, yet its application to MLLMs is challenging due to sequence-conditioned objectives, limited feedback, and massive model scales. To address these issues, we propose Zer0-Jack, the first direct black-box jailbreak framework for MLLMs based on ZO optimization. Zer0-Jack focuses on generating malicious images and introduces a patch-wise block coordinate descent strategy that stabilizes gradient estimation and reduces query complexity, enabling efficient optimization on billion-scale models. Experiments show that Zer0-Jack achieves 98.2% success on MiniGPT-4 and 95% on the Harmful Behaviors Multi-modal dataset, while directly jailbreaking commercial models such as GPT-4o. These results demonstrate that ZO optimization can be effectively adapted to jailbreak large-scale multi-modal LLMs. Codes are provided here.