vita-epfl/detection-attributes-fields
PyTorch implementation of "Detecting 32 Pedestrian Attributes for Autonomous Vehicles"
Implements a Multi-Task Learning framework using composite fields for joint pedestrian detection and 32-attribute recognition (appearance, behavior, crossing intent) optimized for low-resolution autonomous driving scenarios. Introduces fork-normalization to stabilize gradient flow across numerous tasks during backpropagation, addressing scale imbalances in multi-task learning. Operates as an OpenPifPaf plugin, leveraging spatial field representations and instance-wise decoding on the JAAD dataset.
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Oct 16, 2021
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