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Joint Attribute and Model Generalization Learning for Privacy-Preserving Action Recognition
In this case, accurately recognizing the action requires richer contextual information from the background and surroundings.
Efficient Hyper-parameter Optimization with Cubic Regularization
As hyper-parameters are ubiquitous and can significantly affect the model performance, hyper-parameter optimization is extremely important in machine learning.
Segment Any Change
We perform extensive experiments to confirm the effectiveness of AnyChange for zero-shot change detection.
Stable Diffusion is Unstable
Recently, text-to-image models have been thriving. Despite their powerful generative capacity, our research has uncovered a lack of robustness in this generation process.
Towards Distribution-Agnostic Generalized Category Discovery
While several previous works have focused on classifying close-set samples and detecting open-set samples during testing, it's still essential to be able to classify unknown subjects as human beings.
Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learning
Several recent works have attempted to deal with the above issues, which concentrate on the following two aspects.
