At thet-th backward propagation step, we can derive the gradient il(wt)toupdatei-th module inM. The number in the bracket represents the batch size. We see that when the batch size is small (i.e.,32), the gradientvariancesaresimilar. N and K indicate the number of FPN levels and region proposals fed into the detection head. To evaluate this assumption, as shown in Figure 1, we have three observations. As illustrated by the second figure in Figure 1, the gradient misalignment phenomenon between detection head and backbone has been reduced.

Table A1: 3D semantic segmentation results on the SemanticKiTTI validation set. We implemented our method with Pytorch using the open-source OpenPCDet [1]. The faded strategy was used during the last 5 epochs. It provides 22 sequences with 19 semantic classes, captured by a 64-beam LiDAR sensor. The 4th and 5th models sequentially incorporate our proposed SED blocks and DED blocks. Center-based 3d object detection and tracking.

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

Training a large-scale deep neural network in a large-scale dataset is challenging and time-consuming. The recent breakthrough of large-batch optimization is a promising way to tackle this challenge.

The essence of object detection lies in training a detection backbone to extract visual features from images and a detection head to recognize and locate objects based on the visual features.

There are a few recent studies on the topic, such as [33, 37].

ABSTRACT Idling vehicle detection (IVD) uses surveillance video and multichannel audio to localize and classify vehicles in the last frame as moving, idling, or engine-off in pick-up zones. IVD faces three challenges: (i) modality heterogeneity between visual cues and audio patterns; (ii) large box scale variation requiring multi-resolution detection; and (iii) training instability due to coupled detection heads. The previous end-to-end (E2E) model [1] with simple CBAM-based [2] bi-modal attention fails to handle these issues and often misses vehicles. We propose HA VT -IVD, a heterogeneity-aware network with a visual feature pyramid and decoupled heads. Experiments show HA VT -IVD improves mAP by 7.66 over the disjoint baseline and 9.42 over the E2E baseline.