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EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision
This repository contains the official implementation of the paper "EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision". EvRepSL introduces a novel self-supervised approach for generating event-stream representations, which significantly improves the quality of event-based vision tasks.
Overview
EvRepSL leverages a two-stage framework for self-supervised learning on event streams. The representation generator RepGen learns high-quality representations without requiring labeled data, making it versatile for downstream tasks such as classification and object detection in event-based vision. This repository includes the implementation of the core event representation methods EvRep and EvRepSL, along with the trained model weights for RepGen.
Repository Structure
- event_representations.py: Contains the implementation of the proposed event representation methods, EvRep and EvRepSL, along with some common representations such as voxel grid, two-channel, and four-channel.
- models.py: Defines the architecture for RepGen, the representation generator trained using self-supervised learning.
- RepGen.pth: Pretrained weights for RepGen that can be directly used for high-quality feature generation. You can download it from Google Drive.
Getting Started
Prerequisites
Make sure you have the following dependencies installed:
pip3 install torch numpy
python3 event_representation