Robust Visual Intelligence via Learning to Adapt
#computer
#vision
There have been significant advances in computer vision in the past decade. Current computer vision systems usually learn a generic model. But in many real-world applications, a single generic model is often not sufficient to handle the diverse scenarios. In this talk, I will introduce some of our recent work on building robust computer vision systems via learning to adapt. Instead of learning and deploying one generic model, our goal is to learn a model that can effectively adapt itself to different environments during testing. I will present applications of this framework in several computer vision tasks.
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- Shanghai, Shanghai
- China