Multimodal machine learning is often hindered by two critical challenges: modality missingness and modality imbalance. These challenges significantly degrade the performance of multimodal models. The majority of existing methods either require the availability of full-modality data during the training phase or necessitate explicit annotations to detect missing modalities. These dependencies severely limit the models’ applicability in the real world. To tackle these problems, we propose a Dynamic modality Recognition and Enhancement for Adaptive Multimodal fusion framework *DREAM*. Within DREAM, we innovatively employ a sample-level dynamic modality assessment mechanism to direct selective reconstruction of missing or underperforming modalities. Additionally, we introduce a soft masking fusion strategy that adaptively integrates different modalities according to their estimated contributions, enabling more accurate and robust predictions. Experimental results on three benchmark datasets consistently demonstrate that DREAM outperforms several representative baseline and state-of-the-art models, marking its robustness against modality missingness and imbalanced modality.

Gender bias in vision-language models (VLMs) can reinforce harmful stereotypes and discrimination. In this paper, we focus on mitigating gender bias towards vision-language tasks. We identify object hallucination as the essence of gender bias in VLMs. Existing VLMs tend to focus on salient or familiar attributes in images but ignore contextualized nuances. Moreover, most VLMs rely on the co-occurrence between specific objects and gender attributes to infer the ignored features, ultimately resulting in gender bias. We propose GAMA, a task-agnostic generation framework to mitigate gender bias. GAMA consists of two stages: narrative generation and answer inference. During narrative generation, GAMA yields all-sided but gender-obfuscated narratives, which prevents premature concentration on localized image features, especially gender attributes. During answer inference, GAMA integrates the image, generated narrative, and a task-specific question prompt to infer answers for different vision-language tasks. This approach allows the model to rethink gender attributes and answers. We conduct extensive experiments on GAMA, demonstrating its debiasing and generalization ability.