Time-continuous emotion prediction has become an increasingly compelling task in machine learning. Considerable efforts have been made to advance the performance of these systems. Nonetheless, the main focus has been the development of more sophisticated models and the incorporation of different expressive modalities (e.g., speech, face, and physiology). In this paper, motivated by the benefit of difficulty awareness in a human learning procedure, we propose a novel machine learning framework, namely, dynamic difficulty awareness training (DDAT), which sheds fresh light on the research—directly exploiting the difficulties in learning to boost the machine learning process. The DDAT framework consists of two stages: information retrieval and information exploitation. In the first stage, we make use of the reconstruction error of input features or the annotation uncertainty to estimate the difficulty of learning specific information. The obtained difficulty level is then used in tandem with original features to update the model input in a second learning stage with the expectation that the model can learn to focus on high difficulty regions of the learning process. We perform extensive experiments on a benchmark database REmote COLlaborative and affective to evaluate the effectiveness of the proposed framework. The experimental results show that our approach outperforms related baselines as well as other well-established time-continuous emotion prediction systems, which suggests that dynamically integrating the difficulty information for neural networks can help enhance the learning process.
Moreover, DDAT relates to (MTL) as well @cite_31 @cite_0 @cite_20 @cite_49 . @cite_0 , Deng reconstructed the inputs with an AE as an auxiliary task for emotion prediction in a semi-supervised manner, and they demonstrated that the AE can distill representative high-level features from large-scale unlabelled data. @cite_20 , Han proposed utilisation of the PU as an auxiliary task for continuous and dimensional emotion prediction, and they found that this information helps improve performance. @cite_31 , Nicolaou introduced an output-associative framework to learn the correlations and patterns among different emotional dimensions ( arousal valence). In this framework, the arousal and valence predictions from independent models are fused together and fed into a consequential model for a final prediction ( arousal valence). The effectiveness of this approach has been replicated in @cite_51 @cite_62 .
@cite_62 @cite_0 @cite_49 @cite_31 @cite_51 @cite_20
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Automatic emotion recognition is a challenging task which can make great impact on improving natural human computer interactions. In this paper, we present our effort for the Affect Subtask in the Audio Visual Emotion Challenge (AVEC) 2017, which requires participants to perform continuous emotion prediction on three affective dimensions: Arousal, Valence and Likability based on the audiovisual signals. We highlight three aspects of our solutions: 1) we explore and fuse different hand-crafted and deep learned features from all available modalities including acoustic, visual, and textual modalities, and we further consider the interlocutor influence for the acoustic features; 2) we compare the effectiveness of non-temporal model SVR and temporal model LSTM-RNN and show that the LSTM-RNN can not only alleviate the feature engineering efforts such as construction of contextual features and feature delay, but also improve the recognition performance significantly; 3) we apply multi-task learning strategy for collaborative prediction of multiple emotion dimensions with shared representations according to the fact that different emotion dimensions are correlated with each other. Our solutions achieve the CCC of 0.675, 0.756 and 0.509 on arousal, valence, and likability respectively on the challenge testing set, which outperforms the baseline system with corresponding CCC of 0.375, 0.466, and 0.246 on arousal, valence, and likability. The continuous dimensional emotion can depict subtlety and complexity of emotional change, which is an inherently challenging problem with growing attention. This paper presents our automatic prediction of dimensional emotional state for Audio-Visual Emotion Challenge (AVEC 2017), which uses multi-features and fusion across all available modalities. Besides the baseline features provided by the organizers, we also extract other acoustic audio feature sets, appearance features and deep visual features as complementary features. Each type of feature is trained using Long Short-Term Memory Recurrent Neutral Network (LSTM-RNN) for every dimensional emotion prediction separately considering annotation delay and temporal pooling. To overcome overfitting problem, robust models are chosen carefully for individual model. Finally, multimodal emotion fusion is achieved by utilizing Support Vector Regression (SVR) with the estimates from different feature sets in decision level fusion. The experimental results indicate that our extracted features are beneficial to performance improvement and our system design achieves very promising results with Concordant Correlation Coefficient (CCC), which outperform the baseline system on the testing set for arousal of 0.599 vs 0.375 (baseline) and for valence of 0.721 vs 0.466 and for liking 0.295 vs 0.246. Emotions are often perceived by humans through a series of multimodal cues, such as verbal expressions, facial expressions and gestures. In order to recognise emotions automatically, reliable emotional labels are required to learn a mapping from human expressions to corresponding emotions. Dimensional emotion models have become popular and have been widely applied for annotating emotions continuously in the time domain. However, the statistical relationship between emotional dimensions is rarely studied. This paper provides a solution to automatic emotion recognition for the Audio Visual Emotion Challenge (AVEC) 2018. The objective is to find a robust way to detect emotions using more reliable emotion annotations in the valence and arousal dimensions. The two main contributions of this paper are: 1) the proposal of a new approach capable of generating more dependable emotional ratings for both arousal and valence from multiple annotators by extracting consistent annotation features; 2) the exploration of the valence and arousal distribution using outlier detection methods, which shows a specific oblique elliptic shape. With the learned distribution, we are able to detect the prediction outliers based on their local density deviations and correct them towards the learned distribution. The proposed method performance is evaluated on the RECOLA database containing audio, video and physiological recordings. Our results show that a moving average filter is sufficient to remove the incidental errors in annotations. The unsupervised dimensionality reduction approaches could be used to determine a gold standard annotations from multiple annotations. Compared with the baseline model of AVEC 2018, our approach improved the arousal and valence prediction of concordance correlation coefficient significantly to respectively 0.821 and 0.589. Automatic understanding of human affect using visual signals is of great importance in everyday human–machine interactions. Appraising human emotional states, behaviors and reactions displayed in real-world settings, can be accomplished using latent continuous dimensions (e.g., the circumplex model of affect). Valence (i.e., how positive or negative is an emotion) and arousal (i.e., power of the activation of the emotion) constitute popular and effective representations for affect. Nevertheless, the majority of collected datasets this far, although containing naturalistic emotional states, have been captured in highly controlled recording conditions. In this paper, we introduce the Aff-Wild benchmark for training and evaluating affect recognition algorithms. We also report on the results of the First Affect-in-the-wild Challenge (Aff-Wild Challenge) that was recently organized in conjunction with CVPR 2017 on the Aff-Wild database, and was the first ever challenge on the estimation of valence and arousal in-the-wild. Furthermore, we design and extensively train an end-to-end deep neural architecture which performs prediction of continuous emotion dimensions based on visual cues. The proposed deep learning architecture, AffWildNet, includes convolutional and recurrent neural network layers, exploiting the invariant properties of convolutional features, while also modeling temporal dynamics that arise in human behavior via the recurrent layers. The AffWildNet produced state-of-the-art results on the Aff-Wild Challenge. We then exploit the AffWild database for learning features, which can be used as priors for achieving best performances both for dimensional, as well as categorical emotion recognition, using the RECOLA, AFEW-VA and EmotiW 2017 datasets, compared to all other methods designed for the same goal. The database and emotion recognition models are available at http: ibug.doc.ic.ac.uk resources first-affect-wild-challenge.