The region proposal network (RPN) plays a critical role in object detection for a two-stage domain adaptive 3D object detector. However, current methods usually minimize the disparity between source and target domains by reducing the bias in intrinsic geometric information or by undertaking feature alignment according to the geometric disparity but ignore the transferability of RPN-related features and neglect the discriminability between foreground and background, resulting in generating low-quality RPN proposals. Thus, we propose a novel domain adaptation method to distinguish the discriminability between foreground and background. It could implicitly avoid the geometric disparity of objects in feature alignment. Specifically, we first construct learnable and geometry-insensitive foreground RPN prototype and background RPN prototype. Then, we enforce the foreground RPN features and background RPN features to align with the foreground RPN prototype and background RPN prototype, respectively. By this way, the distributional discrepancy is effectively decreased and the adaptability is promoted for existing 3D detectors. We demonstrate that our approach achieves promising results compared with other domain adaptation works on multiple cross-domain detection scenarios.

Existing methods often utilize temporal motion information and spatial layout information in video to predict video saliency. However, the fixations are not always consistent with the moving object of interest, because human eye fixations are determined not only by the spatio-temporal information, but also by the velocity of eye movement. To address this issue, a new saliency prediction method via deep eye movement learning (EML) is proposed in this paper. Compared with previous methods that use human fixations as ground truth, our method uses the optical flow of fixations between successive frames as an extra ground truth for the purpose of eye movement learning. Experimental results on DHF1K, Hollywood2, and UCF-sports datasets show the proposed EML model achieves a promising result across a wide of metrics.

The existing researches reveal that a significant impact is introduced by viewing conditions for visual perception when viewing media on mobile screens. This brings two issues in the area of visual saliency that we need to address: how the saliency models perform in mobile conditions, and how to consider the mobile conditions when designing a saliency model. To investigate the performance of saliency models in mobile environment, eye fixations in four typical mobile conditions are collected as the mobile ground truth in this work. To consider the mobile conditions when designing a saliency model, we combine viewing factors and visual stimuli as two modalities, and a cross-modal based deep learning architecture is proposed for visual attention prediction. Experimental results demonstrate the model with the consideration of mobile viewing factors often outperforms the models without such consideration.