- The paper introduces a dual-task framework, iQAN, that jointly trains visual question answering and generation to enhance overall model performance.
- It employs a novel dual Mutan fusion module and shared LSTM encoders to enable bidirectional inference between questions and answers.
- Experiments on CLEVR and VQA2 reveal significant improvements in reasoning ability and data efficiency, supporting robust cross-modal understanding.
Invertible Question Answering Network (iQAN): A Dual Task Framework for VQG and VQA
The paper "Visual Question Generation as Dual Task of Visual Question Answering" presents the Invertible Question Answering Network (iQAN), a novel framework designed to integrate the tasks of Visual Question Answering (VQA) and Visual Question Generation (VQG). The authors propose that these two tasks, which traditionally have been approached independently, can be considered dual tasks due to their complementary nature; each task can be used to enhance the other. The iQAN framework introduces a unified model that leverages this relationship to improve performance on both tasks, providing a more efficient way to utilize shared data for joint training.
Central to the iQAN framework is the concept of dual learning, which capitalizes on the intrinsic image-feature dependencies shared between VQA and VQG. Specifically, iQAN is formulated to allow the generation of either questions or answers given their counterpart and an image as input. This is achieved through a novel dual Mutan fusion module that operates in a bidirectional manner, empowering the model to infer answers from questions and generate questions from answers.
The model is evaluated on well-established datasets, including CLEVR and VQA2. Results demonstrate that iQAN improves VQA accuracy compared to traditional models, with substantial gains in reasoning ability on CLEVR, attributed to the dual task framework's capacity to enhance cross-modal understanding. Furthermore, the model's dual learning approach is shown to generalize across various VQA architectures, consistently boosting performance in both VQA and VQG tasks.
iQAN's design incorporates several key components:
- Dual Mutan Fusion Module: Extends the state-of-the-art Mutan fusion approach by introducing bidirectional feature inference. Through parameter sharing across VQA and VQG, the module reduces complexity while maintaining expressivity.
- Parameter Sharing Schemes: LSTM encoders used for VQA and VQG share parameters, fostering mutual enhancements in understanding and generation capabilities.
- Duality Regularizer: Introduced to enforce representation similarity between questions and answers, this regularizer acts as a guiding constraint during training, ensuring that learned representations are cohesive across tasks.
The implications of this work are significant, particularly in demonstrating that duality can be effectively utilized to craft models achieving superior performance with comparatively fewer resources. By reimagining VQA and VQG as a cooperative unit, iQAN encourages the reassessment of traditionally separate AI tasks as potentially dual in nature, offering pathways for advancements in other paired modalities. Additionally, the ability to augment VQA dataset generation through VQG highlights practical applications where expensive human annotation can be supplemented or partially automated through model-generated content.
In future directions, the technique of utilizing duality might find application across various domains of AI where similar dependencies exist. Furthermore, the introduction of richer training datasets that support cross-modal reasoning could potentially enhance the efficacy of dual learning models like iQAN, spurring advancements in building AI systems with improved holistic comprehension of visual and linguistic contexts.