Jiashi Feng

TL;DR: Correlation alignment (CORAL) as discussed by the authors minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels, and it can be implemented in four lines of Matlab code.

TL;DR: This work solves the problem of salient object detection by investigating how to expand the role of pooling in convolutional neural networks by building a global guidance module (GGM) and designing a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down path- way.

TL;DR: SAF R-CNN as discussed by the authors introduces multiple built-in subnetworks which detect pedestrians with scales from disjoint ranges, and outputs from all of the sub-networks are then adaptively combined to generate the final detection results that are shown to be robust to large variance in instance scales.

TL;DR: A recurrent rain detection and removal network that removes rain streaks and clears up the rain accumulation iteratively and progressively is proposed and a new contextualized dilated network is developed to exploit regional contextual information and to produce better representations for rain detection.

TL;DR: PANet as mentioned in this paper learns class-specific prototype representations from a few support images within an embedding space and then performs segmentation over the query images through matching each pixel to the learned prototypes.