The computational overhead of CluB is 1.2 / 1.3 times that of the BEV -only A detailed comparison is shown in the following table. GPUs and the batch size per GPU is set as 2. Table 2: Ablation study on the effect of the two kinds of object queries for the transformer decoder. Red boxes and green boxes are the predictions and ground-truth, respectively. Transfusion: Robust lidar-camera fusion for 3d object detection with transformers. Fully sparse 3d object detection.

Neural Information Processing Systems http://nips.cc/

Optimal decision making requires that classifiers produce uncertainty estimates consistent with their empirical accuracy. However, deep neural networks are often under-orover-confident intheir predictions. Consequently,methods have been developed to improve the calibration of their predictive uncertainty, both during training and post-hoc.

These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization.

In this section, we present the training algorithm for Self-Tuning Networks. The full algorithm is showninAlg.2.

An automatic approach to adapting these hyperparameters would improve performance and simplifytheengineering process.