MuTrans: Multiple Transformers for Fusing Feature Pyramid on 2D and 3D Object Detection.

One of the major components of the neural network, the feature pyramid plays a vital part in perception tasks, like object detection in autonomous driving. But it is a challenge to fuse multi-level and multi-sensor feature pyramids for object detection. This paper proposes a simple yet effective framework named MuTrans (Mu ltiple Trans formers) to fuse feature pyramid in single-stream 2D detector or two-stream 3D detector.

SPD: Semi-Supervised Learning and Progressive Distillation for 3-D Detection.

Current learning-based 3-D object detection accuracy is heavily impacted by the annotation quality. It is still a challenge to expect an overall high detection accuracy for all classes under different scenarios given the dataset sparsity. To mitigate this challenge, this article proposes a novel method called semi-supervised learning and progressive distillation (SPD), which uses semi-supervised learning (SSL) and knowledge distillation to improve label efficiency.