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Found 9 papers, 3 papers with code
Unleashing the Potential of Spiking Neural Networks by Dynamic Confidence
In this regard, Dynamic Confidence represents a meaningful step toward realizing the potential of SNNs.
Revisiting Modality Imbalance In Multimodal Pedestrian Detection
Multimodal learning, particularly for pedestrian detection, has recently received emphasis due to its capability to function equally well in several critical autonomous driving scenarios such as low-light, night-time, and adverse weather conditions.
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation
The proposed framework has improved state-of-the-art performances of pose estimation, pedestrian detection, and instance segmentation.
Ranked #18 on
Pose Estimation
on COCO test-dev
E-Scooter Rider Detection and Classification in Dense Urban Environments
Accurate detection and classification of vulnerable road users is a safety critical requirement for the deployment of autonomous vehicles in heterogeneous traffic.
An Objective Method for Pedestrian Occlusion Level Classification
Pedestrian detection is among the most safety-critical features of driver assistance systems for autonomous vehicles.
The Impact of Partial Occlusion on Pedestrian Detectability
RetinaNet has the lowest overall detection performance across the range of occlusion levels.
An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification
This paper details the FPGA implementation methodology for Convolutional Spiking Neural Networks (CSNN) and applies this methodology to low-power radioisotope identification using high-resolution data.
Spiking Neural Network Based Low-Power Radioisotope Identification using FPGA
this paper presents a detailed methodology of a Spiking Neural Network (SNN) based low-power design for radioisotope identification.
Event-based Signal Processing for Radioisotope Identification
This paper identifies the problem of unnecessary high power overhead of the conventional frame-based radioisotope identification process and proposes an event-based signal processing process to address the problem established.
