3 accepted papers
Multi-task perception networks hold great potential as they can improve performance and computational efficiency compared to their single-task counterparts, facilitating online deployment. However, current multi-task architectures in point cloud perception combine multiple task-specific point cloud
Self-supervised feature learning enables perception systems to benefit from the vast raw data recorded by vehicle fleets worldwide. While video-level self-supervised approaches have shown strong generalizability on classification tasks, the potential to learn dense representations from sequential da
Self-supervised multi-object trackers have tremendous potential as they enable learning from raw domain-specific data. However, their re-identification accuracy still falls short compared to their supervised counterparts. We hypothesize that this drawback results from formulating self-supervised obj