Back to Reality: Learning Data-Efficient 3D Object Detector With Shape Guidance.

In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i.e. annotations of object centers and categories).

Quantformer: Learning Extremely Low-Precision Vision Transformers.

In this article, we propose extremely low-precision vision transformers called Quantformer for efficient inference. Conventional network quantization methods directly quantize weights and activations of fully-connected layers without considering properties of transformer architectures. Quantization sizably deviates the self-attention compared with full-precision counterparts, and the shared quantization strategy for diversely distributed patch features causes severe quantization errors.

PointRas: Uncertainty-Aware Multi-Resolution Learning for Point Cloud Segmentation.

In this paper, we propose an uncertainty-aware multi-resolution learning for point cloud segmentation, named PointRas. Most existing works for point cloud segmentation design encoder networks to obtain better representation of local space in point cloud. However, few of them investigate the utilization of features in the lower resolutions produced by encoders and consider the contextual learning between various resolutions in decoder network.