103 citations · 343 across the 21 of their papers we have counts for
11 papers · 1 filter
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
Hao Tang, Dan Xu, Yan Yan +2
In this paper, we address the task of semantic-guided scene generation. One open challenge in scene generation is the difficulty of the generation of small objects and detailed loc…
Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks
Andrea Pilzer, Stéphane Lathuilière, Dan Xu +3
Recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance. However, they require costly ground truth annotations during…
Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection
Yingyue Xu, Dan Xu, Xiaopeng Hong +4
Recent saliency models extensively explore to incorporate multi-scale contextual information from Convolutional Neural Networks (CNNs). Besides direct fusion strategies, many appro…
Geometry-Aware Video Object Detection for Static Cameras
Dan Xu, Weidi Xie, Andrew Zisserman
In this paper we propose a geometry-aware model for video object detection. Specifically, we consider the setting that cameras can be well approximated as static, e.g. in video sur…
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation
Mihai Marian Puscas, Dan Xu, Andrea Pilzer +1
Inspired by the success of adversarial learning, we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Advers…
Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation
Hao Tang, Dan Xu, Gaowen Liu +3
In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (CGAN) for the task of keypoint-guided image generation. The proposed CGAN is a cross-modal f…