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Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial Animation
Moreover, to facilitate disentangled representation learning, we introduce four well-designed constraints: an auxiliary style classifier, an auxiliary inverse classifier, a content contrastive loss, and a pair of latent cycle losses, which can effectively contribute to the construction of the identity-related style space and semantic-related content space.
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.
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