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Towards Top-Down Reasoning: An Explainable Multi-Agent Approach for Visual Question Answering
The Integrator agent combines information from the Seeker agent and the Responder agent to produce the final VQA answer.
A Continual Learning Paradigm for Non-differentiable Visual Programming Frameworks on Visual Reasoning Tasks
Specifically, our CLVP distills the capabilities of well-trained task-specific models into the visual sub-modules in a stepwise and anti-forgetting manner.
Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering
The experimental results show that our proposed method can improve current VQA models on OOD split without losing performance on the in-domain test data.
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