Abstract: While deep neural networks have made remarkable progress in various tasks, their performance typically deteriorates and faces insecurity when tested in out-of-distribution (OOD) scenarios.
Abstract: Typical approaches that learn crowd density maps are limited to extracting the supervisory information from the loosely organized spatial information in the crowd dot/density maps. This ...
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