We propose Homography Attention Module (HAM) which is shown to boost the performance of existing end-to-end multiview detection approaches by utilizing a novel channel gate and spatial gate. Additionally, we propose Booster-SHOT, an end-to-end convolutional approach to multiview pedestrian detection incorporating our proposed HAM as well as elements from previous approaches such as view-coherent augmentation or stacked homography transformations.

Training a reconstruction attacker can successfully recover the original image of existing Adversarial Representation Learning (ARL) methods. We introduce a novel ARL method enhanced through low-pass filtering, limiting the available information amount to be encoded in the frequency domain.

This work presents empirical findings that stronger attacks actually transfer better for the general top-k attack success rate indicated by the interest class rank after attack.

This work sets out to investigate the adversarial vulnerability of the ViT and MLP-Mixer architectures and compare their performance with CNNs.

Despite its wide usage, the gain of enhanced robustness from attack simulation layer (ASL) is usually interpreted through the lens of augmentation, while our work explores this gain from a new perspective by disentangling the forward and backward propagation of such ASL.