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Omni-Dimensional Frequency Learner for General Time Series Analysis
Frequency domain representation of time series feature offers a concise representation for handling real-world time series data with inherent complexity and dynamic nature.
DearKD: Data-Efficient Early Knowledge Distillation for Vision Transformers
Our DearKD is a two-stage framework that first distills the inductive biases from the early intermediate layers of a CNN and then gives the transformer full play by training without distillation.
OH-Former: Omni-Relational High-Order Transformer for Person Re-Identification
Transformers have shown preferable performance on many vision tasks.
Prior Based Human Completion
One is a structure prior, it uses a human parsing map to represent the human body structure.
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