Zhidong Deng

TL;DR: This paper suggests that appropriate incorporation of semantic cues can greatly rectify prediction in commonly-used disparity estimation frameworks and proposes a unified model SegStereo, which employs semantic features from segmentation and introduces semantic softmax loss, which helps improve the prediction accuracy of disparity maps.

TL;DR: In this article, the authors divide semantic image segmentation methods into two categories: traditional and recent DNN method, and comprehensively investigate recent methods based on DNN which are described in the eight aspects: fully convolutional network, up-sample ways, FCN joint with CRF methods, dilated convolution approaches, progresses in backbone network, pyramid methods, multi-level feature and multi-stage method, supervised, weakly-supervised and unsupervised methods.

TL;DR: In this paper, the authors divide semantic image segmentation methods into two categories: traditional and recent DNN method, and comprehensively investigate recent methods based on DNN which are described in the eight aspects: fully convolutional network, upsample ways, FCN joint with CRF methods, dilated convolution approaches, progresses in backbone network, pyramid methods, multi-level feature and multi-stage method, supervised, weakly-supervised and unsupervised methods.

TL;DR: An end-to-end model called fully motion-aware network (MANet), which jointly calibrates the features of objects on both pixel-level and instance-level in a unified framework, which achieves leading performance on the large-scale ImageNet VID dataset.

TL;DR: A densely connected CNN with multi-scale feature attention with competitive performance against state-of-the-art baselines on five benchmark datasets and reveals the model's ability to select proper n-gram features for text classification.