Multimodal Depression Estimation (MDE) aims to infer individual depression scores by analyzing various signals, such as visual, auditory, and language signals etc. Compared to Multimodal Depression Detection (MDD) methods that only provide discrete labels, MDE can provide a more refined score evaluation. However, symptom heterogeneity leads to differences in external behaviors among patients with similar depressive states, which limits the performance of direct regression MDE methods. To address this issue, we propose a Combined Depression Level and Deviation (CDLD) method for MDE, which separates samples at different depression levels and further analyzes subtle deviations within same level to improve estimation performance. Specifically, the Multilevel Depression Separation module constructs depression levels with inherent commonalities based on psychological theories and models the ordinality of these levels, thereby separating samples with different levels of depression. Building on this, the Level-specific Deviation Regression module contrasts sample features relative to level-specific anchors, regressing the subtle depression deviation. Finally, the depression level and deviation are integrated to infer the depression score more accurately. Experiments on the DAIC-WOZ, CMDC, SEARCH, and AVEC 2014 datasets demonstrate that the proposed coarse-to-fine method effectively mitigates the impact of symptom heterogeneity on depression estimation performance, showing significant advantages in MDE tasks. The code is publicly available athttps://github.com/LIU70KG/CDLD.

Unsupervised Cross-Domain Facial Expression Recognition (CD-FER) aims to transfer the recognition ability from annotated source domains to unlabeled target domains. Despite the advancements in CD-FER techniques based on marginal distribution matching, certain inherent properties of facial expressions, such as implicit class margins and imbalanced class distributions, still leave room for improvement in existing models. In this paper, we propose a Class-Adaptive Self-Training (CAST) model for unsupervised CD-FER. In addition to domain alignment, the CAST model leverages self-training to learn pseudo labels and dually enhance aligned representations for explicit class distinction, considering implicit class margins. Furthermore, the CAST model conducts a comprehensive analysis of the negative effects of class distributions on pseudo-label learning from perspectives of class-level representation distributions and predicted probabilities, and subsequently proposes specific solutions. By jointly matching class-level representation distributions and class distributions, the CAST model successfully alleviates conditional distribution discrepancies between domains, which is particularly pertinent for facial expression properties. Experimental results, including assessments on multiple target domains and evaluations of multiple FER models, demonstrate the effectiveness, superiority, and universality of the CAST model.

Change detection (CD) is one of the most important methods for monitoring land surface changes. Recently, transformer-based models have been employed to CD. However, most of them focus on modeling the correlation within each image, while cannot well model the difference between bi-temporal images. In this paper, we propose a difference transformer network (DiFormer) to address this issue. Specifically, we propose a Token Exchange-based Difference Evaluation (TEDE) module to generate the inconsistency between the changed region and the surrounding context to highlight the difference between the bi-temporal images. In addition, to obtain semantically rich exchangeable tokens, we design a Multi-scale Semantic Perception (MSP) module, which provides assistance for difference modeling. In order to test the performance of DiFormer, we conduct qualitative and quantitative experiments on two public datasets, including LEVIR-CD and S2Looking. Experimental results show that our proposed DiFormer is able to achieve better results than several state-of-the-art models, with F1 of 92.15% on the LEVIR-CD dataset and 66.31% on the S2Looking dataset.

Facial Expression Recognition (FER) suffers from misrecognition due to the similarities between expressions. To address this issue, popular works replace original annotations with soft labels to reflect expression similarities. However, existing soft label learning (SLL) modules are independent of FER modules. In this article, inspired by automatic control theory, we propose a bias-based soft label learning network for FER named EC-Net. For optimizing FER and SLL modules jointly, EC-Net constitutes the closed-loop feedback between the two modules by designing a module measuring and transmitting the bias between FER module predictions and target labels. Specifically, EC-Net contains three modules: E-subNet, C-subNet, and L-Transmitter. First, E-subNet, i.e., the FER module, attempts to converge to target labels under the supervision of soft labels, acting as the executor. Then, L-Transmitter measures the bias between E-subNet predictions and target labels. It converts multiple discrete biases to the bias-based label through spectral clustering and transmits it to C-subNet. Finally, C-SubNet, i.e., the SLL module, generates soft labels from the bias-based label with a cascaded learner and progressively distinguishes similar expressions. It updates the learned soft labels for E-subNet, performing like the controller. Supervised by the bias-based soft label, E-subNet effectively reduces the dominant bias caused by similar expressions. We conduct extensive experiments on four popular benchmarks, demonstrating the effectiveness of applying closed-loop feedback in the FER task.

Traditional relational databases are nowadays over-whelmed by the increasing data volume and concurrent access. NoSQL databases can manage large-scale data, but most of them do not support complete transactions and standard SQL languages. NewSQL is proposed for both high scalability and transactional properties with SQL languages support. One type of NewSQL builds distributed systems from scratch, which is too radical for some critical applications. The other type of NewSQL, i.e., data sharding among relational databases, is a better option for these scenarios. This paper presents Apache ShardingSphere, the first top-level open-source platform for data sharding in Apache, which enables developers to use sharded databases like one database. Specifically Apache, ShardingSphere integrates six databases and designs and implements a complete SQL engine to route requests correctly and intelligently. Additionally it encapsulates three types of distributed transactions and provides two adaptors for different scenarios. Moreover it proposes a novel AutoTable strategy and a query language i.e DistSQL allowing database maintainers to easily configure the sharded databases. Further-more it provides many other pluggable features to better shard data. Extensive experiments are conducted using two famous benchmarking tools proving that Apache ShardingSphere is more efficient than eight state-of-the-art systems in our settings. All experimental source codes are publicly released. More than 170 companies are currently using Apache ShardingSphere.

Automatic Dynamic Facial Expression Recognition (DFER) is a challenging task, since how to effectively capture facial temporal dynamics is still an open problem. In this article, we regard variations of facial expressions as a dynamic system in accord with certain rules, and try to explore the fundamental temporal properties for recognizing dynamic expressions. Inspired by the phase space reconstruction method for time series analysis, we propose a novel network named Phase Space Reconstruction Network (PSRNet) for learning spatio-temporal features of facial expressions. First, 3D convolutional neural networks are used to extract spatial and short-term temporal features, which indicate the state of each frame and are termed as observations in the phase space. All the observations compose the trajectory of the dynamical system. Then, a data-driven across-correlation matrix is inferred to reveal the relationship of the observations. With this matrix, the phase space reconstruction module reconstructs the trajectory by aggregating the observations adaptively in the phase space. Reconstructed observations represent the gradual process of dynamic facial expressions, which is beneficial to recognize these expressions. The experiment results on three databases (Oulu, MMI, and CK+) demonstrate that the proposed PSRNet can extract more informative and representative spatio-temporal features for DFER. Moreover, the visualization of intermediate features reveals that the reconstructed features have global consistency in facial regions and the underlying evolutionary pattern of dynamic facial expression.

Facial expression recognition (FER) in the wild received broad concerns in which occlusion and pose variation are two key issues. This paper proposed a global multi-scale and local attention network (MA-Net) for FER in the wild. Specifically, the proposed network consists of three main components: a feature pre-extractor, a multi-scale module, and a local attention module. The feature pre-extractor is utilized to pre-extract middle-level features, the multi-scale module to fuse features with different receptive fields, which reduces the susceptibility of deeper convolution towards occlusion and variant pose, while the local attention module can guide the network to focus on local salient features, which releases the interference of occlusion and non-frontal pose problems on FER in the wild. Extensive experiments demonstrate that the proposed MA-Net achieves the state-of-the-art results on several in-the-wild FER benchmarks: CAER-S, AffectNet-7, AffectNet-8, RAFDB, and SFEW with accuracies of 88.42%, 64.53%, 60.29%, 88.40%, and 59.40% respectively. The codes and training logs are publicly available at https://github.com/zengqunzhao/MA-Net.

In the past decades, face alignment has been studied widely, but it has long been impeded by the problem of pose variation. Recent studies show that pose information used as additional source of information can help address the above problem. In this paper, we adopt a multi-task cascaded CNNs based framework for simultaneous face detection, dense face alignment and fine head pose estimation. Especially, our framework exploits the inherent correlation between face alignment and fine head pose estimation to boost up landmark detection robustness in the case of various poses. Experiments show that our method not only demonstrates real-time performance for face detection, dense face alignment and fine head pose estimation, but also outperforms most state-of-the-art methods for face alignment on the challenging 300-W benchmark. Especially in the case of large pose variations, it achieves outstanding results.