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Image Super-Resolution via Deep Recursive Residual Network

This paper proposes a very deep CNN model (up to 52 convolutional layers) named Deep Recursive Residual Network (DRRN) that strives for deep yet concise networks, and recursive learning is used to control the model parameters while increasing the depth.
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MemNet: A Persistent Memory Network for Image Restoration

A very deep persistent memory network (MemNet) is proposed that introduces a memory block, consisting of a recursive unit and a gate unit, to explicitly mine persistent memory through an adaptive learning process.
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Face Alignment Across Large Poses: A 3D Solution

3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN), is proposed, and a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling is proposed.
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Disentangled Representation Learning GAN for Pose-Invariant Face Recognition

Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.
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Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision

This paper argues the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues, and introduces a new face anti-spoofing database that covers a large range of illumination, subject, and pose variations.
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M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

M3D-RPN is able to significantly improve the performance of both monocular 3D Object Detection and Bird's Eye View tasks within the KITTI urban autonomous driving dataset, while efficiently using a shared multi-class model.
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FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors

A novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution face images without well-aligned requirement, is presented.
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Illuminating Pedestrians via Simultaneous Detection and Segmentation

This work proposes a segmentation infusion network to enable joint supervision on semantic segmentation and pedestrian detection, and provides an in-depth analysis to demonstrate how shared layers are shaped by the segmentation supervision.
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Towards Large-Pose Face Frontalization in the Wild

This work proposes a novel deep 3D Morphable Model (3DMM) conditioned Face Frontalization Generative Adversarial Network (GAN), termed as FF-GAN, to generate neutral head pose face images, which differs from both traditional GANs and 3DMM based modeling.
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CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition

This work proposes a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face recognition, which mainly addresses easy samples in the early training stage and hard ones in the later stage.
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