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Myungsub Choi

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2 papers

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AAAI Conference 2020 Conference Paper

Channel Attention Is All You Need for Video Frame Interpolation

Myungsub Choi
Heewon Kim
Bohyung Han
Ning Xu
Kyoung Mu Lee

Prevailing video frame interpolation techniques rely heavily on optical ﬂow estimation and require additional model complexity and computational cost; it is also susceptible to error propagation in challenging scenarios with large motion and heavy occlusion. To alleviate the limitation, we propose a simple but effective deep neural network for video frame interpolation, which is end-to-end trainable and is free from a motion estimation network component. Our algorithm employs a special feature reshaping operation, referred to as PixelShuf- ﬂe, with a channel attention, which replaces the optical ﬂow computation module. The main idea behind the design is to distribute the information in a feature map into multiple channels and extract motion information by attending the channels for pixel-level frame synthesis. The model given by this principle turns out to be effective in the presence of challenging motion and occlusion. We construct a comprehensive evaluation benchmark and demonstrate that the proposed approach achieves outstanding performance compared to the existing models with a component for optical ﬂow computation.

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NeurIPS Conference 2020 Conference Paper

Meta-Learning with Adaptive Hyperparameters

Sungyong Baik
Myungsub Choi
Janghoon Choi
Heewon Kim
Kyoung Mu Lee

Despite its popularity, several recent works question the effectiveness of MAML when test tasks are different from training tasks, thus suggesting various task-conditioned methodology to improve the initialization. Instead of searching for better task-aware initialization, we focus on a complementary factor in MAML framework, inner-loop optimization (or fast adaptation). Consequently, we propose a new weight update rule that greatly enhances the fast adaptation process. Specifically, we introduce a small meta-network that can adaptively generate per-step hyperparameters: learning rate and weight decay coefficients. The experimental results validate that the Adaptive Learning of hyperparameters for Fast Adaptation (ALFA) is the equally important ingredient that was often neglected in the recent few-shot learning approaches. Surprisingly, fast adaptation from random initialization with ALFA can already outperform MAML.

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