FATE combines pillar encoding via orthogonal polynomial basis with frequency-aware training to enable event-based object detection at up to 200 Hz without internal temporal sub-binning.
HDI-Former: Hybrid dynamic interaction ANN-SNN transformer for object detection using frames and events
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
CMTFormer proposes hierarchical cross-modal modules (SAM, CEM, LDFM) plus a spatial prior to fuse RGB and event streams, outperforming prior detectors on DSEC-Detection and PKU-DAVIS-SOD.
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FATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection
FATE combines pillar encoding via orthogonal polynomial basis with frequency-aware training to enable event-based object detection at up to 200 Hz without internal temporal sub-binning.
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CMTFormer: Marrying Transformer with Hierarchical Information Interaction for RGB-Event Object Detection
CMTFormer proposes hierarchical cross-modal modules (SAM, CEM, LDFM) plus a spatial prior to fuse RGB and event streams, outperforming prior detectors on DSEC-Detection and PKU-DAVIS-SOD.