The spatial information of Electroencephalography (EEG) is essential for emotion recognition model to learn discriminative feature. The convolutional networks and recurrent networks are the conventional choices to learn the complex spatial dependencies through a number of electrodes and brain regions. However, these models have difficulty in capturing long-range dependencies due to the operations of local feature learning. To enhance EEG spatial dependencies capturing and improve the accuracy of emotion recognition, we propose a transformer- based model to hierarchically learn the discriminative spatial information from electrode level to brain-region-level. In the electrode-level spatial learning, the transformer encoders are adopted to integrate information within different brain regions. Next, in view of the different roles of brain regions in the emotion recognition, the self-attention within the transformer could emphasize the contributive brain regions. Hence, in the brain-region-level spatial learning, a transformer encoder is utilized to capture the spatial dependencies among the brain regions. Finally, to validate the effectiveness of the proposed model, the subject-independent experiments are conducted on the DEAP and MAHNOB-HCI database. The experimental results demonstrate that the proposed model achieves outstanding performance in emotion recognition with arousal and valence level. Moreover, the visualization of self-attention indicates that the proposed model could emphasize the discriminative spatial information from pre-frontal lobe, frontal lobe, temporal lobe and parietal lobe.

Automatic detection of casting defects on radiography images is an important technology to automatize digital radiography (DR) defect inspection. Traditionally, in an industrial application, conventional methods are inefficient when the detection targets are small, local and subtle in the complex scenario. Meanwhile, the outperformance of deep learning models, such as the convolution neural network (CNN), is limited by a huge volume of data with precise annotations. To overcome these challenges, an efficient CNN model, only trained with image-level labels, is first proposed for detection of tiny casting defects in a complicated industrial scene. Then, we present a novel training strategy which can form a new object-level attention mechanism for the model during the training phase, and bilinear pooling is utilized to improve the model capability of detecting local contrast casting defects. Moreover, to enhance the interpretability, we extend class activation maps (CAM) to bilinear CAM (Bi-CAM) which is adapted to bilinear architectures as a visualization technique to describe the reason about the model output. Experimental results show that the proposed model achieves superior performance in terms of each quantitative metric and is suitable for most actual applications. The real-time defect detection of castings is efficiently implemented in the complex scenario.

Using machine-learning methodologies to analyze EEG signals becomes increasingly attractive for recognizing human emotions because of the objectivity of physiological data and the capability of the learning principles on modeling emotion classifiers from heterogeneous features. However, the conventional subject-specific classifiers may induce additional burdens to each subject for preparing multiple-session EEG data as training sets. To this end, we developed a new EEG feature selection approach, transfer recursive feature elimination (T-RFE), to determine a set of the most robust EEG indicators with stable geometrical distribution across a group of training subjects and a specific testing subject. A validating set is introduced to independently determine the optimal hyper-parameter and the feature ranking of the T-RFE model aiming at controlling the overfitting. The effectiveness of the T-RFE algorithm for such cross-subject emotion classification paradigm has been validated by DEAP database. With a linear least square support vector machine classifier implemented, the performance of the T-RFE is compared against several conventional feature selection schemes and the statistical significant improvement has been found. The classification rate and F-score achieve 0.7867, 0.7526, 0.7875, and 0.8077 for arousal and valence dimensions, respectively, and outperform several recent reported works on the same database. In the end, the T-RFE based classifier is compared against two subject-generic classifiers in the literature. The investigation of the computational time for all classifiers indicates the accuracy improvement of the T-RFE is at the cost of the longer training time.

X-ray images of castings are widely used in manufacturing for quality assurance. This paper investigates the X-ray-image-based defective detection. The main contributions in this paper are twofold: 1) a new fullimage method is proposed to classify defective castings and non-defective ones; and 2) By combining two technologies, spatial attention mechanism and bilinear pooling used in deep convolutional neural networks, a new spatial attention bilinear convolutional neural network (SA-BCNN) is proposed to enhance the representation power of CNN. To validate above initiatives, extensive experimental studies have been carried out to show advantages of the new method over a number of existing ones.

An automatic vision-based sewer inspection plays a vital role of sewage system in a modern city. Recent advances focus on modeling a deep learning-based method to realize the sewer inspection system, benefiting from the capability of data-driven feature extraction. Although the acceptable performances of sewer defect classification are achieved, there is still a gap between the emerged methods and actual application scenarios. The first issue is that the multi-focus complementarity is ignored to represent the sewer defect, resulting in capturing the multi-scale information of sewer defect inefficiently. Second, the inherent uncertainty of sewer defect is not considered, while the serious unknown sewer defect categories would be missed, resulting in the untrustworthy sewer inspection. In this paper, we focus on quick-view (QV)-based sewer inspection, while a trustworthy multi-focus fusion framework (TMFF) is proposed, jointly combining multi-label classification and uncertainty estimation. Specifically, focal segment module (FSM) is designed based on optical flow to split the QV sewer video into long-focus and short-focus segments, where the multi-focus segments can be modeled to represent the multi-scale information of sewer defect. Then, evidential deep learning (EDL) is introduced to quantify the uncertainty, while joint expert scheme (JES) is designed to aggregate the expert opinions of multi-focus segments. Moreover, evidential disambiguating strategy (EDS) is proposed to alleviate the ambiguity of uncertainty estimation. Extensive experiments are conducted on VideoPipe, in which the superiority of TMFF is demonstrated compared with the state-of-the-art methods. Furthermore, we validate the potential capability of TMFF against the unknown cases of sewer defects.