MacVQA: Adaptive Memory Allocation and Global Noise Filtering for Continual Visual Question Answering
This work addresses continual learning challenges in VQA, an incremental improvement for AI systems requiring multimodal reasoning over time.
The paper tackles the problem of balancing knowledge retention, adaptation, and robust feature representation in continual visual question answering (VQA) by proposing MacVQA, a framework with adaptive memory allocation and global noise filtering, which achieves 43.38% average accuracy and 2.32% average forgetting on standard tasks and 42.53% average accuracy and 3.60% average forgetting on novel composition tasks.
Visual Question Answering (VQA) requires models to reason over multimodal information, combining visual and textual data. With the development of continual learning, significant progress has been made in retaining knowledge and adapting to new information in the VQA domain. However, current methods often struggle with balancing knowledge retention, adaptation, and robust feature representation. To address these challenges, we propose a novel framework with adaptive memory allocation and global noise filtering called MacVQA for visual question answering. MacVQA fuses visual and question information while filtering noise to ensure robust representations, and employs prototype-based memory allocation to optimize feature quality and memory usage. These designs enable MacVQA to balance knowledge acquisition, retention, and compositional generalization in continual VQA learning. Experiments on ten continual VQA tasks show that MacVQA outperforms existing baselines, achieving 43.38% average accuracy and 2.32% average forgetting on standard tasks, and 42.53% average accuracy and 3.60% average forgetting on novel composition tasks.