arXiv:2504.13218v1 Announce Type: cross
Abstract: Incremental learning aims to enable models to continuously acquire knowledge from evolving data streams while preserving previously learned capabilities. While current research predominantly focuses on unimodal incremental learning and multimodal incremental learning where the modalities are consistent, real-world scenarios often present data from entirely new modalities, posing additional challenges. This paper investigates the feasibility of developing a unified model capable of incremental learning across continuously evolving modal sequences. To this end, we introduce a novel paradigm called Modality Incremental Learning (MIL), where each learning stage involves data from distinct modalities. To address this task, we propose a novel framework named Harmony, designed to achieve modal alignment and knowledge retention, enabling the model to reduce the modal discrepancy and learn from a sequence of distinct modalities, ultimately completing tasks across multiple modalities within a unified framework. Our approach introduces the adaptive compatible feature modulation and cumulative modal bridging. Through constructing historical modal features and performing modal knowledge accumulation and alignment, the proposed components collaboratively bridge modal differences and maintain knowledge retention, even with solely unimodal data available at each learning phase.These components work in concert to establish effective modality connections and maintain knowledge retention, even when only unimodal data is available at each learning stage. Extensive experiments on the MIL task demonstrate that our proposed method significantly outperforms existing incremental learning methods, validating its effectiveness in MIL scenarios.
Analysis of Modality Incremental Learning (MIL)
In the field of multimedia information systems, the concept of incremental learning has gained significant attention. Incremental learning refers to the process of continuously acquiring knowledge from evolving data streams while retaining previously learned capabilities. Traditional research on incremental learning has predominantly focused on unimodal or multimodal learning where the modalities remain consistent. However, real-world scenarios often present data from entirely new modalities, posing additional challenges.
The paper introduces a novel paradigm called Modality Incremental Learning (MIL) to address the challenge of learning from continuously evolving modal sequences. MIL involves learning from distinct modalities at each learning stage. This multi-disciplinary approach is significant as it combines concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
The proposed framework, named Harmony, aims to achieve modal alignment and knowledge retention. It introduces adaptive compatible feature modulation and cumulative modal bridging. These components work together to bridge modal differences, establish effective modality connections, and maintain knowledge retention, even with solely unimodal data available at each learning stage.
The results of extensive experiments on the MIL task demonstrate that the Harmony framework significantly outperforms existing incremental learning methods. This validation of effectiveness in MIL scenarios is crucial for the broader field of multimedia information systems. It opens up possibilities for developing unified models capable of learning from diverse modalities in real-world applications.
Implications for Multimedia Information Systems
The concept of Modality Incremental Learning (MIL) presented in this paper has direct implications for the field of multimedia information systems. By addressing the challenges of learning from evolving modal sequences, MIL expands the capabilities of existing systems in several ways:
- Adaptability to New Modalities: MIL enables systems to adapt and learn from entirely new modalities that may emerge over time. This has significant implications for applications that rely on multimedia data, such as computer vision, speech recognition, and natural language processing. The ability to seamlessly incorporate new modalities into existing models can enhance the overall performance of these systems.
- Knowledge Retention: The Harmony framework’s focus on knowledge retention allows models to build upon previously learned capabilities while incorporating new modalities. This is essential in scenarios where information from different modalities is interconnected and requires a holistic understanding. The ability to retain and integrate knowledge across multiple modalities strengthens the overall knowledge base of multimedia information systems.
- Improved Performance: The experimental results demonstrate that the Harmony framework outperforms existing incremental learning methods. This improvement in performance is crucial in real-world scenarios where multimedia information systems need to adapt and learn continuously. The ability to handle evolving modal sequences effectively can lead to more accurate and robust models, enhancing the overall performance of multimedia information systems.
In conclusion, the introduction of Modality Incremental Learning (MIL) and the Harmony framework opens up new avenues for research and development in multimedia information systems. By addressing the challenges of learning from evolving modal sequences and incorporating new modalities, MIL extends the capabilities of existing systems and enhances their performance in real-world scenarios. The multi-disciplinary nature of MIL makes it relevant to various fields, including animations, artificial reality, augmented reality, and virtual realities.