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A Point-Based Approach to Efficient LiDAR Multi-Task Perception
Unlike other LiDAR-based multi-task architectures, our proposed PAttFormer does not require separate feature encoders for multiple task-specific point cloud representations, resulting in a network that is 3x smaller and 1. 4x faster while achieving competitive performance on the nuScenes and KITTI benchmarks for autonomous driving perception.
Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales
We hypothesize that this drawback results from formulating self-supervised objectives that are limited to single frames or frame pairs.
Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences
Self-supervised feature learning enables perception systems to benefit from the vast raw data recorded by vehicle fleets worldwide.
On Hyperbolic Embeddings in 2D Object Detection
Object detection, for the most part, has been formulated in the euclidean space, where euclidean or spherical geodesic distances measure the similarity of an image region to an object class prototype.
Contrastive Object Detection Using Knowledge Graph Embeddings
Object recognition for the most part has been approached as a one-hot problem that treats classes to be discrete and unrelated.
