Recently, the Transformer-based technique has emerged as a promising solution for modeling contextual information in Remote Sensing (RS) scenes and has found widespread applications in RS scene classification. However, how to make full use of intermediate features learned in Transformers is of crucial importance in the RS scene classification tasks. Therefore, this paper proposes a Hierarchical Feature Fusion of Transformer with Patch Dilating (HFFT-PD), which aims to capture rich contextual information from hierarchical features to enhance the performance of RS scene classification. Specifically, the HFFT-PD model consists of a Hierarchical Transformer Merging (HTM) block and a Lightweight Adaptive Channel Compression (LACC) module, in which the HTM is specially designed for the Transformer architecture to bridge the semantic gaps between features from different hierarchical blocks, and the LACC accounts for the significance of distinct channels in the ultimate classification features. In addition, a brand-new Patch Dilating strategy is uniquely designed for the Transformer paradigm, functioning as a reassembly operator predicated on patch features. Contrasting with conventional upsampling techniques, Patch Dilating facilitates upsampling without requiring supplementary information, while concurrently preserving the semantic content of local spatial structure. Extensive and rigorous experiments conducted on the UCM, AID, and NWPU-45 datasets, with training ratios of 80%, 50%, and 20% respectively, demonstrate that our proposed HFFT-PD outperforms the baseline at least by 0.59%, 0.44%, and 0.99% respectively, showcasing the significant superiority of our HFFT-PD over contemporary state-of-the-art methodologies.

From a sketch image or text description, generating a semantic and photographic face image has always been an extremely important issue in computer vision. Sketch images generally contain only simple profile information but not the detail of the face. Therefore, it is difficult to generate facial attributes accurately. In this paper, we treat the sketch to face problem as a face hallucination reconstruction problem. In order to solve this problem, we propose an image translation network by exploiting attributes with generated adversarial network (GAN). And it can significantly contribute to the authenticity of the generated face by supplementing sketch image with the additional facial attribute feature. The generator network is composed of a feature extracting network and a downsampling-upsampling network, both networks use skip-connection to reduce the number of layers without affecting network performance. The discriminator network is designed to examine whether the generated faces contain the desired attributes or not. In the underlying feature extraction phase, our network is different from most attribute-embedded networks, we fuse the sketch images and attributes perceptually. We set the network sub Branch A and B, which receive sketch image and attribute vector in order to extract low-level profile information and highlevel semantic features. Compared with the state-of-the-arts methods of image translation, the performance of the proposed network is excellent.

In recent years, remote depression detection is becoming a new favorite of the Internet of Things because of its promise. However, deployable and sensitive information security has not been properly addressed. Thus, a novel depression detection framework is proposed to successfully solve the above problems. This article concentrates solely on the development and execution of our proposed core algorithm, LKCT. Further details concerning the system’s construction will be disclosed separately as future work. The contribution of this article can be summarized as follows: 1) proposing a depression detection framework that is easy to promote and can effectively solve the security of sensitive information; 2) proposing a novel two-stream deep network LKCT based on the global–local concept; 3) designing a novel network called purified vision transformer (PIT) to enhance the model’s ability to capture local details in images; and 4) introducing decoupled knowledge distillation to distill LKCT into the lightweight network MobileNet for deployability. To evaluate the true performance of our proposed method, we conducted sufficient experiments on the Chinese Academy of Sciences Institute of Automation (CASIA), facial expression recognition challenge (FERC), AVEC2014, and private depression data sets. LKCT achieved an accuracy of 93.4% on CASIA, demonstrating its strong ability to distinguish facial features. The accuracy on FERC reached 99%, proving its ability to accurately distinguish simple emotions. On AVEC2014, the model achieved state-of-the-art results with an RMSE of 7.41 and MAE of 5.48, validating its ability to identify depression. The distilled model obtained an average accuracy of 83% on the private data set, indicating good generalization ability.

Although face recognition has made impressive progress in recent years, we ignore the racial bias of the recognition system when we pursue a high level of accuracy. Previous work found that for different races, face recognition networks focus on different facial regions, and the sensitive regions of darker-skinned people are much smaller. Based on this discovery, we propose a new de-bias method based on gradient attention, called Gradient Attention Balance Network (GABN). Specifically, we use the gradient attention map (GAM) of the face recognition network to track the sensitive facial regions and make the GAMs of different races tend to be consistent through adversarial learning. This method mitigates the bias by making the network focus on similar facial regions. In addition, we also use masks to erase the Top-N sensitive facial regions, forcing the network to allocate its attention to a larger facial region. This method expands the sensitive region of darker-skinned people and further reduces the gap between GAM of darker-skinned people and GAM of Caucasians. Extensive experiments show that GABN successfully mitigates racial bias in face recognition and learns more balanced performance for people of different races.