activity
20172019
most citedRIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal

14 citations · 16 across the 4 of their papers we have counts for

collaborators

6 papers

eess.IV201914 cited

RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal

Ling Zhang, Chengjiang Long, Xiaolong Zhang +1

Residual images and illumination estimation have been proved very helpful in image enhancement. In this paper, we propose a general and novel framework RIS-GAN which explores resid…

cs.CV20191 cited

Iterative and Adaptive Sampling with Spatial Attention for Black-Box Model Explanations

Bhavan Vasu, Chengjiang Long

Deep neural networks have achieved great success in many real-world applications, yet it remains unclear and difficult to explain their decision-making process to an end-user. In t…

cs.CV2019

ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal

Bin Ding, Chengjiang Long, Ling Zhang +1

In this paper we propose an attentive recurrent generative adversarial network (ARGAN) to detect and remove shadows in an image. The generator consists of multiple progressive step…

cs.CV2019

VITAL: A Visual Interpretation on Text with Adversarial Learning for Image Labeling

Tao Hu, Chengjiang Long, Leheng Zhang +1

In this paper, we propose a novel way to interpret text information by extracting visual feature presentation from multiple high-resolution and photo-realistic synthetic images gen…

cs.CV20181 cited

Deep Neural Networks In Fully Connected CRF For Image Labeling With Social Network Metadata

Chengjiang Long, Roddy Collins, Eran Swears +1

We propose a novel method for predicting image labels by fusing image content descriptors with the social media context of each image. An image uploaded to a social media site such…

cs.CV2017

L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face Contour Extraction

Yongwei Nie, Xu Cao, Chengjiang Long +2

Current face alignment algorithms can robustly find a set of landmarks along face contour. However, the landmarks are sparse and lack curve details, especially in chin and cheek ar…