In this paper, we propose a similarity-aware deep adversarial learning (SADAL) approach for facial age estimation. Instead of making full access to the limited training samples which likely leads to bias age prediction, our SADAL aims to seek batches of unobserved hard-negative samples based on existing training samples, which typically reinforces the discriminativeness of the learned feature representation for facial ages. Motivated by the fact that age labels are usually correlated in real-world scenarios, we carefully develop a similarity-aware function to well measure the distance of each face pair based on the age value gaps. Consequently, the age-difference information is exploited in the synthetic feature space for robust age estimation. During the learning process, we jointly optimize both procedures of generating hard negatives and learning discriminative age ranker via a sequence of adversarial-game iterations. Another major issue lies on that existing methods only enforce the indiscriminativeness within each class, which is probably trapped into model overfitting and thus the generation capacity is limited particularly on unseen age classes with many individuals. To circumvent this problem, we propose a variational deep adversarial learning (VDAL) paradigm, which learns to encode each face sample in two factorized parts, i.e., the intra-class variance distribution and the intra-class invariant class center. Moreover, our VDAL principally optimizes the variational confidence lower bound on the variational factorized feature representation. To better enhance the discriminativeness of the age representation, our VDAL further learns to encode the ordinal relationship among age labels in the reconstructed subspace. Experimental results on folds of widely-evaluated benchmarking datasets demonstrate that our approach achieves promising performance in contrast to most state-of-the-art age estimation methods.

In this paper, we propose an end-to-end reasoning-decision networks (RDN) approach for robust face alignment via policy gradient. Unlike the conventional coarse-to-fine approaches which likely lead to bias prediction due to poor initialization, our approach aims to learn a policy by leveraging raw pixels to reason a subset of shape candidates, sequentially making plausible decisions to remove outliers for robust initialization. To achieve this, we formulate face alignment as a Markov decision process by defining an agent, which typically interacts with a trajectory of states, actions, state transitions and rewards. The agent seeks an optimal shape searching policy over the whole shape space by maximizing a discounted sum of the received values. To further improve the alignment performance, we develop an LSTM-based value function to evaluate the shape quality. During the training procedure, we adjust the gradient of our value function in directions of the policy gradient. This prevents our training goal from being trapped into local optima entangled by both the pose deformations and appearance variations especially in unconstrained environments. Experimental results show that our proposed RDN consistently outperforms most state-of-the-art approaches on four widely-evaluated challenging datasets.

Acquiring complementary information about tissue morphology from multimodal medical images is beneficial to clinical disease diagnosis, but it cannot be widely used due to the cost of scans. In such cases, medical image synthesis has become a popular area. Recently, generative adversarial network (GAN) models are applied to many medical image synthesis tasks and show prior performance, since they enable to capture structural details clearly. However, GAN still builds the main framework based on convolutional neural network (CNN) that exhibits a strong locality bias and spatial invariance through the use of shared weights across all positions. Therefore, the long‐range dependencies have been destroyed in this processing. To address this issue, we introduce a double‐scale deep learning method for cross‐modal medical image synthesis. More specifically, the proposed method captures locality feature via local discriminator based on CNN and utilizes long‐range dependencies to learning global feature through global discriminator based on transformer architecture. To evaluate the effectiveness of double‐scale GAN, we conduct folds of experiments on the standard benchmark IXI dataset and experimental results demonstrate the effectiveness of our method.

In this paper, we propose a probabilistic regression diffusion model for head pose estimation, dubbed HeadDiff, which typically addresses the rotation uncertainty, especially when faces are captured in wild conditions. Unlike conventional image-to-pose methods which cannot explicitly establish the rotational manifold of head poses, our HeadDiff aims to ensure the pose rotation via the diffusion process and in parallel, refine the mapping process iteratively. Specifically, we initially formulate the head pose estimation problem as a reverse diffusion process, defining a paradigm for progressive denoising on the manifold, which explores the uncertainty by decomposing the large gap into intermediate steps. Moreover, our HeadDiff is equipped with an isotropic Gaussian distribution by encoding the incoherence information in our rotation representation. Finally, we learn the facial relationship of nearest neighbors with a cycle-consistent constraint for robust pose estimation versus diverse shape variations. Experimental results on multiple datasets demonstrate that our proposed method outperforms existing state-of-the-art techniques without auxiliary data.

In the recent years, convolutional neural networks (CNN)-based super resolution (SR) methods are widely used in the field of remote sensing. However, complicated remote sensing images contain abundant high-frequency details, which are difficult to capture and reconstruct effectively. To address this problem, we propose a dense channel attention network (DCAN) to reconstruct high-resolution (HR) remote sensing images. The proposed method learns multi-level feature information and pays more attention to the important and useful regions in order to better reconstruct the final image. Specifically, we construct a dense channel attention mechanism (DCAM), which densely uses the feature maps from the channel attention block via skip connection. This mechanism makes better use of multi-level feature maps which contain abundant high-frequency information. Further, we add a spatial attention block, which makes the network have more flexible discriminative ability. Experimental results demonstrate that the proposed DCAN method outperforms several state-of-the-art methods in both quantitative evaluation and visual quality.