The robust generalization of deep learning models in the presence of inherent noise remains a significant challenge, especially when labels are ambiguous due to their subjective nature and noise is indiscernible in natural settings. In this article, we address a specific and important scenario of monitoring suicidal ideation (SI), where time-series data, such as galvanic skin response (GSR) and photoplethysmography (PPG), are susceptible to such noise. Current methods predominantly focus on image and text data or address artificially introduced noise, neglecting the complexities of natural noise in time-series analysis. To tackle this, we introduce a novel neural network model tailored for analyzing noisy physiological time-series data, named DBN_ConvNet, which integrates advanced encoding techniques with confidence learning training to enhance prediction performance. Another main contribution of our work is the collection of a specialized dataset of GSR and PPG signals derived from real-world environments for SI prediction. By employing this dataset, our DBN_ConvNet achieves a prediction accuracy of 76.67% and an F1 score of 0.74 in a binary classification task, outperforming state-of-the-art methods. Furthermore, comprehensive evaluations have been conducted on three other well-known public datasets with artificially introduced noise to test the DBN_ConvNet’s capabilities rigorously. These tests consistently demonstrated DBN_ConvNet’s superior performance by achieving an improvement of more than 10% in both accuracy and F1 score compared to the baseline methods.

Electroencephalogram (EEG) signals have been widely studied in human emotion recognition. The majority of existing EEG emotion recognition algorithms utilize dozens or hundreds of electrodes covering the whole scalp region (denoted as full-channel EEG devices in this paper). Nowadays, more and more portable and miniature EEG devices with only a few electrodes (denoted as few-channel EEG devices in this paper) are emerging. However, emotion recognition from few-channel EEG data is challenging because the device can only capture EEG signals from a portion of the brain area. Moreover, existing full-channel algorithms cannot be directly adapted to few-channel EEG signals due to the significant inter-variation between full-channel and few-channel EEG devices. To address these challenges, we propose a novel few-channel EEG emotion recognition framework from the perspective of knowledge transfer. We leverage full-channel EEG signals to provide supplementary information for few-channel signals via a transfer learning-based model CD-EmotionNet, which consists of a base emotion model for efficient emotional feature extraction and a cross-device transfer learning strategy. This strategy helps to enhance emotion recognition performance on few-channel EEG data by utilizing knowledge learned from full-channel EEG data. To evaluate our cross-device EEG emotion transfer learning framework, we construct an emotion dataset containing paired 18-channel and 5-channel EEG signals from 25 subjects, as well as 5-channel EEG signals from 13 other subjects. Extensive experiments show that our framework outperforms state-of-the-art EEG emotion recognition methods by a large margin.

Multi-modal emotion analysis, as an important direction in affective computing, has attracted increasing attention in recent years. Most existing multi-modal emotion recognition studies are targeted at a classification task that aims to assign a specific emotion category to a combination of several heterogeneous input data, including multimedia signals and physiological signals. Compared to single-class emotion recognition, a growing number of recent psychological evidence suggests that different discrete emotions may co-exist at the same time, which promotes the development of mixed-emotion recognition to identify a mixture of basic emotions. Although most current studies treat it as a multi-label classification task, in this work, we focus on a challenging situation where both positive and negative emotions are presented simultaneously, and propose a multi-modal mixed emotion recognition framework, namely EmotionDict. The key characteristics of our EmotionDict include the following. (1) Inspired by the psychological evidence that such a mixed state can be represented by combinations of basic emotions, we address mixed emotion recognition as a label distribution learning task. An emotion dictionary has been designed to disentangle the mixed emotion representations into a weighted sum of a set of basic emotion elements in a shared latent space and their corresponding weights. (2) While many existing emotion distribution studies are built on a single type of multimedia signal (such as text, image, audio, and video), we incorporate physiological and overt behavioral multi-modal signals, including electroencephalogram (EEG), peripheral physiological signals, and facial videos, which directly display the subjective emotions. These modalities have diverse characteristics given that they are related to the central or peripheral nervous system, and the motor cortex. (3) We further design auxiliary tasks to learn modality attentions for modality integration. Experiments on two datasets show that our method outperforms existing state-of-the-art approaches on mixed-emotion recognition.

Generative Adversarial networks (GANs) have demonstrated their powerful capability of synthesizing high-resolution images, and great efforts have been made to interpret the semantics in the latent spaces of GANs. However, existing works still have the following limitations: (1) the majority of works rely on either pretrained attribute predictors or large-scale labeled datasets, which are difficult to collect in most cases, and (2) some other methods are only suitable for restricted cases, such as focusing on interpretation of human facial images using prior facial semantics. In this paper, we propose a GAN-based method called FEditNet, aiming to discover latent semantics using very few labeled data without any pretrained predictors or prior knowledge. Specifically, we reuse the knowledge from the pretrained GANs, and by doing so, avoid overfitting during the few-shot training of FEditNet. Moreover, our layer-wise objectives which take content consistency into account also ensure the disentanglement between attributes. Qualitative and quantitative results demonstrate that our method outperforms the state-of-the-art methods on various datasets. The code is available at https://github.com/THU-LYJ-Lab/FEditNet.

Emotional information plays an important role in various multimedia applications. Movies, as a widely available form of multimedia content, can induce multiple positive emotions and stimulate people's pursuit of a better life. Different from negative emotions, positive emotions are highly correlated and difficult to distinguish in the emotional space. Since different positive emotions are often induced simultaneously by movies, traditional single-target or multi-class methods are not suitable for the classification of movie-induced positive emotions. In this paper, we proposeTransEEG, a model for multi-label positive emotion classification from a viewer's brain activities when watching emotional movies. The key features ofTransEEGinclude (1) explicitly modeling the spatial correlation and temporal dependencies of multi-channel EEG signals using the Transformer structure based model, which effectively addresses long-distance dependencies, (2) exploiting the label-label correlations to guide the discriminative EEG representation learning, for that we design an Inter-Emotion Mask for guiding the Multi-Head Attention to learn the inter-emotion correlations, and (3) constructing an attention score vector from the representation-label correlation matrix to refine emotion-relevant EEG features. To evaluate the ability of our model for multi-label positive emotion classification, we demonstrate our model on a state-of-the-art positive emotion database CPED. Extensive experimental results show that our proposed method achieves superior performance over the competitive approaches.

Emotion analysis based on peripheral physiological signals has attracted increasing attention recently in affective computing. Previous works usually predict emotional states using a single emotion label for each discrete time. However, in real-world scenarios, it is not sufficient due to the fact that the real-world emotional state is usually a mixture of basic emotions. In this paper, we formulate the emotion analysis as an emotion distribution learning (EDL) problem and make two contributions. First, we establish a standardised dataset containing four negative emotions (anger, disgust, sadness, fear) and three positive emotions (tenderness, joy, amusement), which could be a useful benchmark for the EDL task. Second, we propose an emotion distribution prediction system which has the following distinct characteristics: (1) after processing raw peripheral physiological signals, we compute totally 89 representative features from four channels, i.e., GSR, SKT, ECG and HR, (2) an adaptive feature selection strategy based on recursive feature elimination (RFE) is used to select the most significant features in our EDL task, and (3) we design a dedicated EDL model based on convolution neural networks that takes information from both the feature correlation and the time domain into consideration. Experiments were conducted to validate our proposed system, and the results indicated that (1) the proposed feature selection strategy effectively selects significant features and improves algorithmic performance, and (2) the proposed EDL model can obtain good results in terms of six evaluation measures and outperform existing methods.

Positive emotions are of great significance to people's daily life, such as human-computer/robot interaction. However, the structure of extensive positive emotions is not clear yet and effective standardized inducing materials containing as many positive emotional categories as possible are lacking. Thus, this article aims to establish a Chinese positive emotion database (CPED) to (1) effectively elicit positive emotion categories as many as possible, (2) provide both the subjective feelings of different positive emotions and a corresponding peripheral physiological database, and (3) explore the structure and framework of positive emotion categories. 42 video clips of 16 positive emotion categories were screened from 1000+ online clips. Then a total of 312 participants watched and rated these video clips during which GSR and PPG signals were recorded. 34 video clips that met hit rate and intensity standards were systemically clustered into four emotion categories (empathy, fun, creativity and esteem). Eventually, 22 film clips of these four major categories formed the CPED database. A total of 84 features from GSR and PPG signals were extracted and entered into RF, SVM, DBN and LSTM classifiers that serves as baseline classification methods. A classification accuracy of 44.66 percent for four major categories of positive emotions was achieved.

Cartoon is a common form of art in our daily life and automatic generation of cartoon images from photos is highly desirable. However, state-of-the-art single-style methods can only generate one style of cartoon images from photos and existing multi-style image style transfer methods still struggle to produce high-quality cartoon images due to their highly simplified and abstract nature. In this article, we propose a novel multi-style generative adversarial network (GAN) architecture, called MS-CartoonGAN, which can transform photos into multiple cartoon styles. MS-CartoonGAN uses only unpaired photos and cartoon images of multiple styles for training. To achieve this, we propose to use (1) a hierarchical semantic loss with sparse regularization to retain semantic content and recover flat shading in different abstract levels, (2) a new edge-promoting adversarial loss for producing fine edges, and (3) a style loss to enhance the difference between output cartoon styles and make training process more stable. We also develop a multi-domain architecture, where the generator consists of a shared encoder and multiple decoders for different cartoon styles, along with multiple discriminators for individual styles. By observing that cartoon images drawn by different artists have their unique styles while sharing some common characteristics, our shared network architecture exploits the common characteristics of cartoon styles, achieving better cartoonization and being more efficient than single-style cartoonization. We show that our multi-domain architecture can theoretically guarantee to output desired multiple cartoon styles. Through extensive experiments including a user study, we demonstrate the superiority of the proposed method, outperforming state-of-the-art single-style and multi-style image style transfer methods.

In this work, we tackle the essential problem of scale inconsistency for self supervised joint depth-pose learning. Most existing methods assume that a consistent scale of depth and pose can be learned across all input samples, which makes the learning problem harder, resulting in degraded performance and limited generalization in indoor environments and long-sequence visual odometry application. To address this issue, we propose a novel system that explicitly disentangles scale from the network estimation. Instead of relying on PoseNet architecture, our method recovers relative pose by directly solving fundamental matrix from dense optical flow correspondence and makes use of a two-view triangulation module to recover an up-to-scale 3D structure. Then, we align the scale of the depth prediction with the triangulated point cloud and use the transformed depth map for depth error computation and dense reprojection check. Our whole system can be jointly trained end-to-end. Extensive experiments show that our system not only reaches state-of-the-art performance on KITTI depth and flow estimation, but also significantly improves the generalization ability of existing self-supervised depth-pose learning methods under a variety of challenging scenarios, and achieves state-of-the-art results among self-supervised learning-based methods on KITTI Odometry and NYUv2 dataset. Furthermore, we present some interesting findings on the limitation of PoseNet-based relative pose estimation methods in terms of generalization ability. Code is available at https://github.com/B1ueber2y/TrianFlow.

Recently, Image-to-Image Translation (IIT) has achieved great progress in image style transfer and semantic context manipulation for images. However, existing approaches require exhaustively labelling training data, which is labor demanding, difficult to scale up, and hard to adapt to a new domain. To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images in sparsely grouped datasets where only a few train samples are labelled. Using a one-input multi-output architecture, SG-GAN is well-suited for tackling multi-task learning and sparsely grouped learning tasks. The new model is able to translate images among multiple groups using only a single trained model. To experimentally validate the advantages of the new model, we apply the proposed method to tackle a series of attribute manipulation tasks for facial images as a case study. Experimental results show that SG-GAN can achieve comparable results with state-of-the-art methods on adequately labelled datasets while attaining a superior image translation quality on sparsely grouped datasets~\footnoteCode is available at https://github.com/zhangqianhui/SGGAN-tensorflow..