Conversational Speech Synthesis (CSS) aims to produce speech appropriate for oral communication. However, the complexity of context dependency modeling poses significant challenges in the field of CSS, especially the mutual psychological influence between interlocutors. Previous studies have verified that prior commonsense knowledge helps machines understand subtle psychological information (e.g., feelings and intentions) in spontaneous oral dialogues. Therefore, to enhance context understanding and improve the naturalness of synthesized speech, we propose a novel conversational speech synthesis system (CMCU-CSS) that incorporates the Commonsense-based Multi-modal Context Understanding (CMCU) module to model the dynamic emotional interaction among interlocutors. Specifically, we first utilize three implicit states (intent state, internal state and external state) in CMCU to model the context dependency between inter/intra speakers with the help of commonsense knowledge. Furthermore, we infer emotion vectors from the fusion of these implicit states and multi-modal features to enhance the emotion discriminability of synthesized speech. This is the first attempt to combine commonsense knowledge with conversational speech synthesis, and its effect in terms of emotion discriminability of synthetic speech is evaluated by emotion recognition in conversation task. The results of subjective and objective evaluations demonstrate that the CMCU-CSS model achieves more natural speech with context-appropriate emotion and is equipped with the best emotion discriminability, surpassing that of other conversational speech synthesis models.
论文检索
输入标题、作者或关键词，从 1,014 篇学术成果中精准定位
Predicting individual aesthetic preferences holds significant practical applications and academic implications for human society. However, existing studies mainly focus on learning and predicting the commonality of facial attractiveness, with little attention given to Personalized Facial Beauty Prediction (PFBP). PFBP aims to develop a machine that can adapt to individual aesthetic preferences with only a few images rated by each user. In this paper, we formulate this task from a meta-learning perspective that each user corresponds to a meta-task. To address such PFBP task, we draw inspiration from the human aesthetic mechanism that visual aesthetics in society follows a Gaussian distribution, which motivates us to disentangle user preferences into a commonality and an individuality part. To this end, we propose a novel MetaFBP framework, in which we devise a universal feature extractor to capture the aesthetic commonality and then optimize to adapt the aesthetic individuality by shifting the decision boundary of the predictor via a meta-learning mechanism. Unlike conventional meta-learning methods that may struggle with slow adaptation or overfitting to tiny support sets, we propose a novel approach that optimizes a high-order predictor for fast adaptation. In order to validate the performance of the proposed method, we build several PFBP benchmarks by using existing facial beauty prediction datasets rated by numerous users. Extensive experiments on these benchmarks demonstrate the effectiveness of the proposed MetaFBP method.
Towards Adaptable Graph Representation Learning: An Adaptive Multi-Graph Contrastive Transformer
PDF ↗Significant progress has been made in graph representation learning in recent years. However, most of these methods model spatial relationships via predefined graphs or decouple spatial-temporal representations, which limits the generalization and effectiveness of the model. To address these issues, we introduce an adaptive multi-graph contrastive transformer (AMGCT) for general spatial-temporal graph representation learning. Specifically, we first propose adaptive multi-graph contrastive learning (AMGCL). Without any expert knowledge, AMGCL can gradually generate adaptive spatial graphs with different topologies to learn spatial representations from different views. Cross-graph contrastive learning further explores potential correlations between different views, making each view's features more discriminative. In addition, to avoid insufficient interaction caused by decoupling spatial-temporal information in existing methods, we design a coupled graph transformer (CGT) to consider spatial relationships at each stage of temporal modeling, explore complementary information between spatial and temporal domains, and obtain more compact spatial-temporal representations. Experimental results on two different spatial-temporal graph datasets and tasks demonstrate that the proposed method achieves excellent performance.
Although empathic interaction between counselor and client is fundamental to success in the psychotherapeutic process, there are currently few datasets to aid a computational approach to empathy understanding. In this paper, we construct a multimodal empathy dataset collected from face-to-face psychological counseling sessions. The dataset consists of 771 video clips. We also propose three labels (i.e., expression of experience, emotional reaction, and cognitive reaction) to describe the degree of empathy between counselors and their clients. Expression of experience describes whether the client has expressed experiences that can trigger empathy, and emotional and cognitive reactions indicate the counselor's empathic reactions. As an elementary assessment of the usability of the constructed multimodal empathy dataset, an interrater reliability analysis of annotators' subjective evaluations for video clips is conducted using the intraclass correlation coefficient and Fleiss' Kappa. Results prove that our data annotation is reliable. Furthermore, we conduct empathy prediction using three typical methods, including the tensor fusion network, the sentimental words aware fusion network, and a simple concatenation model. The experimental results show that empathy can be well predicted on our dataset. Our dataset is available for research purposes.
Multimodal sentiment analysis has gained significant attention due to the proliferation of multimodal content on social media. However, existing studies in this area rely heavily on large-scale supervised data, which is time-consuming and labor-intensive to collect. Thus, there is a need to address the challenge of few-shot multimodal sentiment analysis. To tackle this problem, we propose a novel method called Multimodal Probabilistic Fusion Prompts (MultiPoint) that leverages diverse cues from different modalities for multimodal sentiment detection in the few-shot scenario. Specifically, we start by introducing a Consistently Distributed Sampling approach called CDS, which ensures that the few-shot dataset has the same category distribution as the full dataset. Unlike previous approaches primarily using prompts based on the text modality, we design unified multimodal prompts to reduce discrepancies between different modalities and dynamically incorporate multimodal demonstrations into the context of each multimodal instance. To enhance the model's robustness, we introduce a probabilistic fusion method to fuse output predictions from multiple diverse prompts for each input. Our extensive experiments on six datasets demonstrate the effectiveness of our approach. First, our method outperforms strong baselines in the multimodal few-shot setting. Furthermore, under the same amount of data (1% of the full dataset), our CDS-based experimental results significantly outperform those based on previously sampled datasets constructed from the same number of instances of each class.
Most existing methods for image emotion classification extract features directly from images supervised by a single emotional label. However, this approach has a limitation known as the affective gap which restricts the capability of these features as they do not always align with the emotions perceived by users. To effectively bridge the affective gap, this paper proposes a visual content understanding network inspired by the human staged emotion perception process. The proposed network is comprised of three perception modules designed to extract multi-level information. Firstly, an entity perception module extracts entities from images. Secondly, an attribute perception module extracts the attribute content of each entity. Thirdly, an emotion perception module extracts emotion features based on both the entity and attribute information. We generate pseudo-labels of entities and attributes through image segmentation and vision-language models to provide auxiliary guidance for network learning. The progressive entity and attribute understanding enable the network to hierarchically extract semantic-level features for emotion analysis. Extensive experiments demonstrate that our progressive learning network achieves superior performance on various benchmark datasets for image emotion classification.
The human brain can easily focus on one speaker and suppress others in scenarios such as a cocktail party. Recently, researchers found that auditory attention can be decoded from the electroencephalogram (EEG) data. However, most existing deep learning methods are difficult to use prior knowledge of different views (that is attended speech and EEG are task-related views) and extract an unsatisfactory representation. Inspired by Broadbent's filter model, we decode auditory attention in a multi-view paradigm and extract the most relevant and important information utilizing the missing view. Specifically, we propose an auditory attention decoding (AAD) method based on multi-view VAE with task-related multi-view contrastive (TMC) learning. Employing TMC learning in multi-view VAE can utilize the missing view to accumulate prior knowledge of different views into the fusion of representation, and extract the approximate task-related representation. We examine our method on two popular AAD datasets, and demonstrate the superiority of our method by comparing it to the state-of-the-art method.
Learning Shared Semantic Information from Multimodal Bio-signals for Brain-Muscle Modulation Analysis
PDF ↗This paper presents a novel learning-based algorithm to investigate the high-level shared semantic information between electroencephalography (EEG) and electromyography (EMG) signals, for understanding brain-muscle modulation during movement execution. The proposed algorithm incorporates a spatial encoder that condenses spatial information obtained from EEG/EMG signals into unified temporal tokens using a learnable correlation matrix. These tokens are then encoded and decoded via a siamese temporal encoder and classification head to extract joint semantic information presented in cross-modal signals. Additionally, an analysis pipeline is designed to examine brain-muscle modulation based on the proposed algorithm. Experimental results from a self-collected multimodal bio-signals dataset validate the efficacy of the proposed algorithm in extracting and analyzing high-level latent semantic information shared in EEG and EMG signals, outperforming the state-of-the-art model by 5.35% in accuracy, 4.69% in precision, and 8.65% in recall. Notably, the designed analysis pipeline can also reveal low-level relationships, such as those related to time and space, between multimodal bio-signals. This research provides neuroscientists with a valuable tool for obtaining enhanced insights into brain-muscle modulation.
Socially intelligent systems such as home robots should be able to perceive emotions and social behaviors. Affect recognition datasets have limited labeled data, and existing large unlabeled datasets, e.g., VoxCeleb2, suitable for pre-training, mostly contain neutral expressions, limiting their application to affective downstream tasks. We introduce a novel Semi-supervised Affective Adaptation framework via Metric Learning (SAAML) to adapt pre-trained audiovisual models (e.g., AV-HuBERT) to expressive behaviors associated with emotions and social communication. The proposed framework automatically retrieves a large number of emotional excerpts (>100 hours) from the VoxCeleb2 dataset via metric learning from two emotion recognition datasets (MSP-IMPROV and CREMA-D), and learns domain-invariant emotion-aware representations. Experimental results show that fine-tuning the proposed affect-aware AV-HuBERT (AW-HuBERT) improves the emotion recognition accuracy by 3-6% compared to fine-tuning the original pre-trained models. We further validate the effectiveness of the AW-HuBERT on human-centered visual understanding tasks, namely, facial expression recognition, video highlight detection, and continuous emotion recognition. The proposed approach consistently outperforms AV-HuBERT and delivers competitive performance compared to the existing methods. With this work, we demonstrate the effectiveness of adaptive pre-training for existing models on domain-specific data to enhance their performance for human-centered tasks.
Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation
PDF ↗Emotion Recognition in Conversation (ERC) plays an important role in driving the development of human-machine interaction. Emotions can exist in multiple modalities, and multimodal ERC mainly faces two problems: (1) the noise problem in the cross-modal information fusion process, and (2) the prediction problem of less sample emotion labels that are semantically similar but different categories. To address these issues and fully utilize the features of each modality, we adopted the following strategies: first, deep emotion cues extraction was performed on modalities with strong representation ability, and feature filters were designed as multimodal prompt information for modalities with weak representation ability. Then, we designed a Multimodal Prompt Transformer (MPT) to perform cross-modal information fusion. MPT embeds multimodal fusion information into each attention layer of the Transformer, allowing prompt information to participate in encoding textual features and being fused with multi-level textual information to obtain better multimodal fusion features. Finally, we used the Hybrid Contrastive Learning (HCL) strategy to optimize the model's ability to handle labels with few samples. This strategy uses unsupervised contrastive learning to improve the representation ability of multimodal fusion and supervised contrastive learning to mine the information of labels with few samples. Experimental results show that our proposed model outperforms state-of-the-art models in ERC on two benchmark datasets.
Multimodal emotion recognition based on electroencephalogram (EEG) and compensating physiological signals (e.g., eye tracking) has shown potential in the diagnosis and rehabilitation tracking of depression. Since the multi-channel EEG signals are generally processed as one-dimensional (1-D) graph-like features, existing approaches can only adopt underdeveloped shallow models to recognize emotions. However, these simple models have difficulty decoupling complex emotion patterns due to their limited representation capacity. To address this problem, we propose the graph-to-grid (G2G), a concise and plug-and-play module that transforms the 1-D graph-like data into the two-dimensional (2-D) grid-like data via the numerical relation coding. After that, the well developed deep models, e.g., ResNet can be used to downstream tasks. In addition, G2G simplifies the previous complex multimodal fusion into an input matrix augmentation operation, which greatly reduces the difficulty of model design and parameter tuning. Extensive results on three public datasets (SEED, SEED5 and MPED) indicate that the proposed approach achieves state-of-the-art emotion recognition accuracy in both unimodal and multimodal settings, with good cross-session generalization ability. G2G enables the development of more appropriate multimodal emotion recognition algorithms for follow-up studies. Our code is publicly available at https://github.com/Jinminbox/G2G.
Emotion recognition from physiological signals is a topic of widespread interest, and researchers continue to develop novel techniques for perceiving emotions. However, the emergence of deep learning has highlighted the need for high-quality emotional datasets to accurately decode human emotions. In this study, we present a novel multimodal emotion dataset that incorporates electroencephalography (EEG) and eye movement signals to systematically explore human emotions. Seven basic emotions (happy, sad, fear, disgust, surprise, anger, and neutral) are elicited by a large number of 80 videos and fully investigated with continuous labels that indicate the intensity of the corresponding emotions. Additionally, we propose a novel Multimodal Adaptive Emotion Transformer (MAET), that can flexibly process both unimodal and multimodal inputs. Adversarial training is utilized in MAET to mitigate subject discrepancy, which enhances domain generalization. Our extensive experiments, encompassing both subject-dependent and cross-subject conditions, demonstrate MAET's superior performance in handling various inputs. The filtering of data for high emotional evocation using continuous labels proved to be effective in the experiments. Furthermore, the complementary properties between EEG and eye movements are observed. Our code is available at https://github.com/935963004/MAET.
Conversational Text-to-speech Synthesis (TTS) aims to generate speech with proper style in the user-agent conversation scenario. Although previous works have explored modeling the context in the dialogue history to provide style information for the agent, there are still deficiencies in modeling the role-aware multi-modal context. Moreover, previous works ignore the emotional dependencies between the user and the agent, which includes: 1) agent understands emotional states of users, and 2) agent expresses proper emotion in the generated speech. In this work, we propose an Emotionally Situated Text-to-speech Synthesis (EmoSit-TTS) framework to understand users' semantics and subtle emotional states, and generate speech with proper speaking style and emotional expression in the user-agent conversation. Experiments on the DailyTalk dataset show the superiority of our proposed framework for the user-agent conversational TTS, especially in terms of emotion-aware expressiveness, which outperforms other state-of-the-art methods by 0.69 on MOS. Demos of our proposed framework are available at https://anonydemo.github.io.
Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition
PDF ↗Cross-corpus speech emotion recognition (SER) seeks to generalize the ability of inferring speech emotion from a well-labeled corpus to an unlabeled one, which is a rather challenging task due to the significant discrepancy between two corpora. Existing methods, typically based on unsupervised domain adaptation (UDA), struggle to learn corpus-invariant features by global distribution alignment, but unfortunately, the resulting features are mixed with corpus-specific features or not class-discriminative. To tackle these challenges, we propose a novel Emotion Decoupling aNd Alignment learning framework (EMO-DNA) for cross-corpus SER, a novel UDA method to learn emotion-relevant corpus-invariant features. The novelties of EMO-DNA are two-fold: contrastive emotion decoupling and dual-level emotion alignment. On one hand, our contrastive emotion decoupling achieves decoupling learning via a contrastive decoupling loss to strengthen the separability of emotion-relevant features from corpus-specific ones. On the other hand, our dual-level emotion alignment introduces an adaptive threshold pseudo-labeling to select confident target samples for class-level alignment, and performs corpus-level alignment to jointly guide model for learning class-discriminative corpus-invariant features across corpora. Extensive experimental results demonstrate the superior performance of EMO-DNA over the state-of-the-art methods in several cross-corpus scenarios. Source code is available at https://github.com/Jiaxin-Ye/Emo-DNA.
Emotion recognition in conversation aims to identify the emotions underlying each utterance, and it has great potential in various domains. Human perception of emotions relies on multiple modalities, such as language, vocal tonality, and facial expressions. While many studies have incorporated multimodal information to enhance emotion recognition, the performance of multimodal models often plateaus when additional modalities are added. We demonstrate through experiments that the main reason for this plateau is an imbalanced assignment of gradients across modalities. To address this issue, we propose fine-grained adaptive gradient modulation, a plug-in approach to rebalance the gradients of modalities. Experimental results show that our method improves the performance of all baseline models and outperforms existing plug-in methods.
Affective and psychological perception are pivotal in human-machine interaction and essential domains within artificial intelligence. Existing physiological signal-based affective and psychological datasets primarily rely on contact-based sensors, potentially introducing extraneous affectives during the measurement process. Consequently, creating accurate non-contact affective and psychological perception datasets is crucial for overcoming these limitations and advancing affective intelligence. In this paper, we introduce the Remote Multimodal Affective and Psychological (ReMAP) dataset, for the first time, apply head micro-tremor (HMT) signals for affective and psychological perception. ReMAP features 68 participants and comprises two sub-datasets. The stimuli videos utilized for affective perception undergo rigorous screening to ensure the efficacy and universality of affective elicitation. Additionally, we propose a novel remote affective and psychological perception framework, leveraging multimodal complementarity and interrelationships to enhance affective and psychological perception capabilities. Extensive experiments demonstrate HMT as a "small yet powerful" physiological signal in psychological perception. Our method outperforms existing state-of-the-art approaches in remote affective recognition and psychological perception. The ReMAP dataset is publicly accessible at https://remap-dataset.github.io/ReMAP.
Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition
PDF ↗It has been a hot research topic to enable machines to understand human emotions in multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion analysis in conversation (MM-ERC). MM-ERC has received consistent attention in recent years, where a diverse range of methods has been proposed for securing better task performance. Most existing works treat MM-ERC as a standard multimodal classification problem and perform multimodal feature disentanglement and fusion for maximizing feature utility. Yet after revisiting the characteristic of MM-ERC, we argue that both the feature multimodality and conversational contextualization should be properly modeled simultaneously during the feature disentanglement and fusion steps. In this work, we target further pushing the task performance by taking full consideration of the above insights. On the one hand, during feature disentanglement, based on the contrastive learning technique, we devise a Dual-level Disentanglement Mechanism (DDM) to decouple the features into both the modality space and utterance space. On the other hand, during the feature fusion stage, we propose a Contribution-aware Fusion Mechanism (CFM) and a Context Refusion Mechanism (CRM) for multimodal and context integration, respectively. They together schedule the proper integrations of multimodal and context features. Specifically, CFM explicitly manages the multimodal feature contributions dynamically, while CRM flexibly coordinates the introduction of dialogue contexts. On two public MM-ERC datasets, our system achieves new state-of-the-art performance consistently. Further analyses demonstrate that all our proposed mechanisms greatly facilitate the MM-ERC task by making full use of the multimodal and context features adaptively. Note that our proposed methods have the great potential to facilitate a broader range of other conversational multimodal tasks.
Pretrained Implicit-Ensemble Transformer for Open-Set Authentication on Multimodal Mobile Biometrics
Smartphones have become indispensable in our lives, even for security-critical tasks. Traditional security measures such as PINs provide only one-time authentication, while biometrics enable continuous authentication in mobile devices. This paper introduces a simple, lightweight, pretrained Transformer dubbed PIEformer for open-set authentication (OSA) of multimodal touchstrokes and gait biometrics. Compared to conventional mobile closed-set authentication, OSA enables more secure and practical authentication, with genuine and impostor users disjoint from the training set. PIEFormer incorporates a novel implicit ensembling mechanism for extracting discriminative embeddings within an open-set environment and enhancing generalization performance. This approach learns multiple diverse sub-embeddings, capturing complementary aspects of biometrics data with minimal computational overhead, allowing Transformers to exhibit robust capabilities in OSA. Our proposed methods demonstrate state-of-the-art results on HMOG and BBMAS datasets, particularly in open-set scenarios compared to closed-set literature, thus bringing mobile biometric authentication closer to real-world applications.
MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion
PDF ↗Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task. Especially, the achievement of precise and controllable image reconstruction bears great significance in propelling the progress and utilization of brain-computer interfaces. Despite the advancements in complex image reconstruction techniques, the challenge persists in achieving a cohesive alignment of both semantic (concepts and objects) and structure (position, orientation, and size) with the image stimuli. To address the aforementioned issue, we propose a two-stage image reconstruction model called MindDiffuser1. In Stage 1, the VQ-VAE latent representations and the CLIP text embeddings decoded from fMRI are put into Stable Diffusion, which yields a preliminary image that contains semantic information. In Stage 2, we utilize the CLIP visual feature decoded from fMRI as supervisory information, and continually adjust the two feature vectors decoded in Stage 1 through backpropagation to align the structural information. The results of both qualitative and quantitative analyses demonstrate that our model has surpassed the current state-of-the-art models on Natural Scenes Dataset (NSD). The subsequent experimental findings corroborate the neurobiological plausibility of the model, as evidenced by the interpretability of the multimodal feature employed, which align with the corresponding brain responses.
Domain adaptation has driven the progress of Facial Expression Recognition (FER). Existing cross-domain FER methods focus only on the domain alignment of a single source domain to the target domain, ignoring the importance of multisource domains that contain richer knowledge. However, Cross-Multidomain FER (CMFER)needs to combat the domain conflicts caused by the uncertainty of intradomain annotations and the inconsistency of interdomain distributions. To this end, this paper proposes a Domain-Uncertain Mutual Learning (DUML) method to deal with the more challenging CMFER problem. Specifically, we consider a domain-specific global perspective for domain-invariance representation and domain fusion for facial generic detail representation to mitigate cross-domain distribution differences. Further, we develop Intra-Domain Uncertainty (Intra-DU) and Inter-Domain Uncertainty (Inter-DU) to combat the large dataset shifts caused by annotation uncertainty. Finally, extensive experimental results on multiple benchmark across multidomain FER datasets demonstrate the remarkable effectiveness of DUML against CMFER uncertainty. All codes and training logs are publicly available at https://github.com/liuhw01/DUML.