In this supplemental file, we provide more details to support the paper.

These works are regarded as passive works for object detection as they take the input image as is. However, convergence to global minima is not guaranteed to be optimal in neural networks; therefore, we argue that the trained weights in the object detector are not optimal. To rectify this problem, we propose a wrapper based on proactive schemes, PrObeD, which enhances the performance of these object detectors by learning a signal. PrObeD consists of an encoder-decoder architecture, where the encoder network generates an image-dependent signal termed templates to encrypt the input images, and the decoder recovers this template from the encrypted images. We propose that learning the optimum template results in an object detector with an improved detection performance. The template acts as a mask to the input images to highlight semantics useful for the object detector. Finetuning the object detector with these encrypted images enhances the detection performance for both generic and camouflaged.

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

Mean A verage Precision (mAP). For VIC3D object detection, we focus on the obstacles around the ego vehicle. There are two metrics used for evaluation: BEV@mAP and 3D@mAP . BEV@mAP evaluates the 3D boxes in the bird's-eye view and ignores the In our implementation, we ignore the transmission cost of calibration files and timestamps. For early fusion, we calculate the transmission cost of transmitting raw data.

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By leveraging associated camera poses, these features are aggregated into a compact 3D template voxel.

Semi-supervised learning aims to leverage alarge amount of unlabeled data for performance boosting. Existing works primarily focus on image classification. Inthispaper,wedelveintosemi-supervised learning forobject detection, where labeled data are more labor-intensive to collect.