Search Results for author:
Found 11 papers, 4 papers with code
Audio-driven Talking Face Generation by Overcoming Unintended Information Flow
Specifically, this involves unintended flow of lip, pose and other information from the reference to the generated image, as well as instabilities during model training.
Face-Dubbing++: Lip-Synchronous, Voice Preserving Translation of Videos
The system is designed to combine multiple component models and produces a video of the original speaker speaking in the target language that is lip-synchronous with the target speech, yet maintains emphases in speech, voice characteristics, face video of the original speaker.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Exposure Correction Model to Enhance Image Quality
We show that after applying exposure correction with the proposed model, the portrait matting quality increases significantly.
Alpha Matte Generation from Single Input for Portrait Matting
We first generate a coarse segmentation map from the input image and then predict the alpha matte by utilizing the image and segmentation map.
CAGAN: Text-To-Image Generation with Combined Attention GANs
Generating images according to natural language descriptions is a challenging task.
Ear2Face: Deep Biometric Modality Mapping
We have achieved very promising results, especially on the FERET dataset, generating visually appealing face images from ear image inputs.
Multimodal Age and Gender Classification Using Ear and Profile Face Images
Experimental results indicated that profile face images contain a rich source of information for age and gender classification.
The Unconstrained Ear Recognition Challenge 2019 - ArXiv Version With Appendix
The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i. e. gender and ethnicity.
Age and Gender Classification From Ear Images
Although there have been a few previous work on gender classification using ear images, to the best of our knowledge, this study is the first work on age classification from ear images.
Domain Adaptation for Ear Recognition Using Deep Convolutional Neural Networks
We have first shown the importance of domain adaptation, when deep convolutional neural network models are used for ear recognition.
The Unconstrained Ear Recognition Challenge
In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions.
