Hao Zhou

TL;DR: Three data augmentation methods dedicated to underwater-imaging are proposed, the inverse process of underwater image restoration is used to simulate different marine turbulence environments, and perspective transformation and Illumination synthesis are proposed to simulateDifferent marine uneven illuminating environments.

TL;DR: Experimental results on two benchmark pedestrian trajectory prediction datasets demonstrate the competitive performances of the proposed method in terms of both the final displace error and the average displacement error metrics as compared with state-of-the-art trajectory prediction methods.

TL;DR: Li et al. as mentioned in this paper propose a co-attention network (CANet) to build sound interaction between RGB and depth features, which includes three modules: position and channel coattention fusion modules adaptively fuse RGB and D features in spatial and channel dimensions.

TL;DR: A co-attention Network (CANet) to capture the fine-grained interplay between RGB and D features, achieving the state-of-the-art performance on two challenging RGB-D semantic segmentation datasets, i.e., NYUDv2, SUN-RGBD.

TL;DR: Three data augmentation methods are proposed dedicated to underwater-imaging, where the inverse process of underwater image restoration is used to simulate different marine turbulence environments, and Perspective transformation and Illumination synthesis are proposed.