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Found 7 papers, 4 papers with code
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image
Experimentally, DAD-3DNet outperforms or is comparable to the state-of-the-art models in (i) 3D Head Pose Estimation on AFLW2000-3D and BIWI, (ii) 3D Face Shape Reconstruction on NoW and Feng, and (iii) 3D Dense Head Alignment and 3D Landmarks Estimation on DAD-3DHeads dataset.
FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
We introduce an architecture block for object model adaption, called dual-template representation, and a pixel-wise fusion block to achieve extra flexibility and efficiency of the model.
ActGAN: Flexible and Efficient One-shot Face Reenactment
We also introduce a solution to preserve a person's identity between synthesized and target person by adopting the state-of-the-art approach in deep face recognition domain.
Fast and Efficient Model for Real-Time Tiger Detection In The Wild
The highest accuracy object detectors to date are based either on a two-stage approach such as Fast R-CNN or one-stage detectors such as Retina-Net or SSD with deep and complex backbones.
DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better
We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring efficiency, quality, and flexibility.
Ranked #2 on
Blind Face Restoration
on CelebA-Test
Safe Augmentation: Learning Task-Specific Transformations from Data
We present a simple and explainable method called $\textbf{Safe Augmentation}$ that can learn task-specific data augmentation techniques that do not change the data distribution and improve the generalization of the model.
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
The quality of the deblurring model is also evaluated in a novel way on a real-world problem -- object detection on (de-)blurred images.
Ranked #3 on
Deblurring
on REDS
