49 citations · 144 across the 12 of their papers we have counts for
7 papers · 1 filter
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation
Yuxiang Wei, Yupeng Shi, Xiao Liu +4
Unsupervised disentanglement learning is a crucial issue for understanding and exploiting deep generative models. Recently, SeFa tries to find latent disentangled directions by per…
UniCon: Unified Context Network for Robust Active Speaker Detection
Yuanhang Zhang, Susan Liang, Shuang Yang +4
We introduce a new efficient framework, the Unified Context Network (UniCon), for robust active speaker detection (ASD). Traditional methods for ASD usually operate on each candida…
Structured Multi-modal Feature Embedding and Alignment for Image-Sentence Retrieval
Xuri Ge, Fuhai Chen, Joemon M. Jose +3
The current state-of-the-art image-sentence retrieval methods implicitly align the visual-textual fragments, like regions in images and words in sentences, and adopt attention modu…
Locality-aware Channel-wise Dropout for Occluded Face Recognition
Mingjie He, Jie Zhang, Shiguang Shan +3
Face recognition remains a challenging task in unconstrained scenarios, especially when faces are partially occluded. To improve the robustness against occlusion, augmenting the tr…
Gaze Estimation with an Ensemble of Four Architectures
Xin Cai, Boyu Chen, Jiabei Zeng +7
This paper presents a method for gaze estimation according to face images. We train several gaze estimators adopting four different network architectures, including an architecture…
1st Place Solutions for UG2+ Challenge 2021 -- (Semi-)supervised Face detection in the low light condition
Pengcheng Wang, Lingqiao Ji, Zhilong Ji +2
In this technical report, we briefly introduce the solution of our team "TAL-ai" for (Semi-) supervised Face detection in the low light condition in UG2+ Challenge in CVPR 2021. By…
Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser
Yue Cao, Xiaohe Wu, Shuran Qi +3
The success of deep denoisers on real-world color photographs usually relies on the modeling of sensor noise and in-camera signal processing (ISP) pipeline. Performance drop will i…