4 accepted papers
Continual learning for visual question answering (VQA) is typically implemented by training one expert per task and routing each query using task-ID supervision. Yet continual VQA tasks overlap substantially: on the VQA-v2 task stream, a non-native expert outperforms the task’s own expert on $49.9\%…
Recent advances in large language models (LLMs) have improved the accuracy of visual question answering (VQA) systems. However, directly applying LLMs to VQA still presents several challenges: (a) suboptimal performance when handling questions from specialized domains, (b) higher computational costs…
Deep learning models with large-scale backbones have been increasingly adopted to tackle complex visual question answering (VQA) problems in real settings. While providing powerful learning capacities to handle the high-dimensional and multimodal VQA data, these models tend to suffer from the memori…
Generative Adversarial Networks (GANs) have emerged as a powerful tool for generating high-fidelity content. This paper presents a new training procedure that leverages Neural Architecture Search (NAS) to discover the optimal architecture for image generation while employing the Maximum Mean Discrep…