arXiv:2403.05060v1 Announce Type: new
Abstract: Recent advancements in large-scale models have showcased remarkable generalization capabilities in various tasks. However, integrating multimodal processing into these models presents a significant challenge, as it often comes with a high computational burden. To address this challenge, we introduce a new parameter-efficient multimodal tuning strategy for large models in this paper, referred to as Multimodal Infusion Tuning (MiT). MiT leverages decoupled self-attention mechanisms within large language models to effectively integrate information from diverse modalities such as images and acoustics. In MiT, we also design a novel adaptive rescaling strategy at the head level, which optimizes the representation of infused multimodal features. Notably, all foundation models are kept frozen during the tuning process to reduce the computational burden(only 2.5% parameters are tunable). We conduct experiments across a range of multimodal tasks, including image-related tasks like referring segmentation and non-image tasks such as sentiment analysis. Our results showcase that MiT achieves state-of-the-art performance in multimodal understanding while significantly reducing computational overhead(10% of previous methods). Moreover, our tuned model exhibits robust reasoning abilities even in complex scenarios.
Integrating Multimodal Processing in Large-scale Models: The Future of Multimodal Understanding
In recent years, large-scale models have demonstrated remarkable generalization capabilities across various tasks. However, integrating multimodal processing into these models has been a challenging endeavor due to the high computational burden it often entails. In this groundbreaking paper, titled “Multimodal Infusion Tuning (MiT)”, the authors introduce a novel parameter-efficient strategy to address this challenge.
Multimodal Infusion Tuning (MiT) leverages decoupled self-attention mechanisms within large language models to effectively integrate information from diverse modalities, such as images and acoustics. By introducing a new adaptive rescaling strategy at the head level, MiT optimizes the representation of infused multimodal features. Importantly, the authors freeze all foundation models during the tuning process, reducing the computational burden significantly (only 2.5% of parameters are tunable).
The presented research is highly relevant to the wider field of multimedia information systems, as it addresses the inherent complexity of processing diverse modalities. Multimedia information systems deal with the management, retrieval, and understanding of multimedia data, which encompasses various modalities such as text, images, audio, and video. By developing a parameter-efficient strategy for multimodal processing, MiT contributes to the advancement of these systems by reducing the computational overhead while achieving state-of-the-art performance in multimodal understanding.
Furthermore, the concepts explored in this paper are closely related to the fields of animations, artificial reality, augmented reality, and virtual realities. The ability to effectively integrate information from multiple modalities is crucial for creating immersive and realistic experiences in these domains. MiT’s decoupled self-attention mechanisms and adaptive rescaling strategy can enhance the quality and realism of animations, improve the perception of artificial reality, enable more seamless integration of virtual objects in augmented reality, and enhance the overall immersive experience in virtual realities.
The experiments conducted by the authors across a range of multimodal tasks validate the effectiveness of MiT. Whether it is image-related tasks like referring segmentation or non-image tasks such as sentiment analysis, MiT achieves state-of-the-art performance while significantly reducing computational overhead – a notable advancement in the field. Additionally, the authors highlight that the tuned model exhibits robust reasoning abilities even in complex scenarios, further cementing the potential impact of MiT in real-world applications.
Overall, this paper on Multimodal Infusion Tuning (MiT) presents a groundbreaking approach to integrating multimodal processing into large-scale models. By developing a parameter-efficient strategy, the authors contribute to the wider field of multimedia information systems and open up new possibilities in animations, artificial reality, augmented reality, and virtual realities. With its state-of-the-art performance and reduced computational burden, MiT paves the way for future advancements in multimodal understanding and immersive experiences.