Automated driving systems (ADSs) promise a safe, comfortable and efficient driving
experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The …

Driven by deep learning techniques, perception technology in autonomous driving has
developed rapidly in recent years, enabling vehicles to accurately detect and interpret …

Non-uniformed 3D sparse data, eg, point clouds or voxels in different spatial positions, make
contribution to the task of 3D object detection in different ways. Existing basic components in …

FlowFormer introduces a transformer architecture into optical flow estimation and achieves
state-of-the-art performance. The core component of FlowFormer is the transformer-based …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

The performance of video prediction has been greatly boosted by advanced deep neural
networks. However, most of the current methods suffer from large model sizes and require …

While general object detection with deep learning has achieved great success in the past
few years, the performance and efficiency of detecting small objects are far from satisfactory …

In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object
detection. Towards this goal we present an end-to-end learnable architecture that reasons …

Vision Transformers (ViT) become widely-adopted architectures for various vision tasks.
Masked auto-encoding for feature pretraining and multi-scale hybrid convolution-transformer …

Abstract Vision Transformers (ViT) become widely-adopted architectures for various vision
tasks. Masked auto-encoding for feature pretraining and multi-scale hybrid convolution …