103 citations · 502 across the 43 of their papers we have counts for
26 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…
Unified Generative Adversarial Networks for Controllable Image-to-Image Translation
Hao Tang, Hong Liu, Nicu Sebe
We propose a unified Generative Adversarial Network (GAN) for controllable image-to-image translation, i.e., transferring an image from a source to a target domain guided by contro…
Curriculum Self-Paced Learning for Cross-Domain Object Detection
Petru Soviany, Radu Tudor Ionescu, Paolo Rota +1
Training (source) domain bias affects state-of-the-art object detectors, such as Faster R-CNN, when applied to new (target) domains. To alleviate this problem, researchers proposed…
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 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…