论文检索

Yang Zhang, Songhe Feng

Generalized Zero-Shot Learning (GZSL) aims to recognize unseen classes, which are not observable during training, with auxiliary semantic information, e.g., attributes. Attributes play an important role in GZSL due to their ability to provide rich semantic information and visual guidance. In this paper, we will review the characteristics of attributes. First, the visual appearance of the same attribute varies greatly between different classes, which leads to the projection domain shift problem and inaccurate attribute localization. Second, the predefined semantic prototypes are not able to faithfully represent the credible visual information of each sample, which leads to suboptimal visual-semantic alignment. Therefore, we propose a novel framework called Enhancing doMain-invariant Parts (EMP) to solve above issues. To be specific, we mitigate the projection domain shift problem by the feature disentanglement technology in domain generalization, which can disentangle the attribute-based visual features into domain-invariant and domain-specific parts. So that the model can pay more attention to the essential parts of attributes rather than using all the information learned from the seen classes to identify the unseen classes. Then we achieve better visual-semantic alignment by refining the predefined semantic prototypes with the extracted credible visual information from corresponding sample. To make the extracted visual information be more in line with the image content rather than overfitting the semantic prototype, we draw on the idea of self-paced learning to help the model learn attributes from easy to complex. Experimental results show that our method achieves a new state-of-the-art performance on three generalized zero-shot learning benchmarks.

Yu Shang, Chen Gao 0001, Jiansheng Chen 0001, Depeng Jin, Huimin Ma 0001, Yong Li 0008

Recently multi-modal recommender systems have been widely applied in real scenarios such as e-commerce businesses. Existing multi-modal recommendation methods exploit the multi-modal content of items as auxiliary information and fuse them to boost performance. Despite the superior performance achieved by multi-modal recommendation models, there's currently no understanding of their robustness to adversarial attacks. In this work, we first identify the vulnerability of existing multi-modal recommendation models. Next, we show the key reason for such vulnerability is modality imbalance, i.e., the prediction score margin between positive and negative samples in the sensitive modality will drop dramatically facing adversarial attacks and fail to be compensated by other modalities. Finally, based on this finding we propose a novel defense method to enhance the robustness of multi-modal recommendation models through modality balancing. Specifically, we first adopt an embedding distillation to obtain a pair of content-similar but prediction-different item embeddings in the sensitive modality and calculate the score margin reflecting the modality vulnerability. Then we optimize the model to utilize the score margin between positive and negative samples in other modalities to compensate for the vulnerability. The proposed method can serve as a plug-and-play module and is flexible to be applied to a wide range of multi-modal recommendation models. Extensive experiments on two real-world datasets demonstrate that our method significantly improves the robustness of multi-modal recommendation models with nearly no performance degradation on clean data.

Wenhui Li 0001, Xinqi Su, Dan Song 0006, Lanjun Wang, Kun Zhang 0040, An-An Liu

Prior image-text matching methods have shown remarkable performance on many benchmark datasets, but most of them overlook the bias in the dataset, which exists in intra-modal and inter-modal, and tend to learn the spurious correlations that extremely degrade the generalization ability of the model. Furthermore, these methods often incorporate biased external knowledge from large-scale datasets as prior knowledge into image-text matching model, which is inevitable to force model further learn biased associations. To address above limitations, this paper firstly utilizes Structural Causal Models (SCMs) to illustrate how intra- and inter-modal confounders damage the image-text matching. Then, we employ backdoor adjustment to propose an innovative Deconfounded Causal Inference Network (DCIN) for image-text matching task. DCIN (1) decomposes the intra- and inter-modal confounders and incorporates them into the encoding stage of visual and textual features, effectively eliminating the spurious correlations during image-text matching, and (2) uses causal inference to mitigate biases of external knowledge. Consequently, the model can learn causality instead of spurious correlations caused by dataset bias. Extensive experiments on two well-known benchmark datasets, i.e., Flickr30K and MSCOCO, demonstrate the superiority of our proposed method.

Zexian Yang, Dayan Wu, Wanqian Zhang, Bo Li 0063, Weiping Wang 0005

Incremental learning for person re-identification (ReID) aims to develop models that can be trained with a continuous data stream, which is a more practical setting for real-world applications. However, the existing incremental ReID methods make two strong assumptions that the cameras are fixed and the new-emerging data is class-disjoint from previous classes. This is unrealistic as previously observed pedestrians may re-appear and be captured again by new cameras. In this paper, we investigate person ReID in an unexplored scenario named Camera Incremental Person ReID (CIPR), which advances existing lifelong person ReID by taking into account the class overlap issue. Specifically, new data collected from new cameras may probably contain an unknown proportion of identities seen before. This subsequently leads to the lack of cross-camera annotations for new data due to privacy concerns. To address these challenges, we propose a novel framework ExtendOVA. First, to handle the class overlap issue, we introduce an instance-wise seen-class identification module to discover previously seen identities at the instance level. Then, we propose a criterion for selecting confident ID-wise candidates and also devise an early learning regularization term to correct noise issues in pseudo labels. Furthermore, to compensate for the lack of previous data, we resort prototypical memory bank to create surrogate features, along with a cross-camera distillation loss to further retain the inter-camera relationship. The comprehensive experimental results on multiple benchmarks show that ExtendOVA significantly outperforms the state-of-the-arts with remarkable advantages.

Weiming Liu 0005, Xiaolin Zheng, Chaochao Chen 0001, Mengling Hu, Xinting Liao, Fan Wang 0020, Yanchao Tan, Dan Meng 0001, Jun Wang 0020

Cross-Domain Recommendation (CDR) has been popularly studied for solving the data sparsity problem via leveraging rich knowledge from the auxiliary domain. Most of the current CDR models assume that user-item ratings/reviews are both accessible during the training procedure. However, it is impractical nowadays due to the strict data privacy protection policy. In this paper, we focus on the Privacy-Preserving Cross-Domain Recommendation problem (PPCDR). Although some previous approaches have investigated the problems, they always fail to effectively utilize the rating and review for user preference modeling. What is worse, they separate the processes of privacy data modeling and user-item collaborative filtering, leading to suboptimal solutions. To fill this gap, we propose the Differentially Private Sparse Mapping Recommendation model (DPSMRec), an end-to-end cross-domain recommendation framework for solving the PPCDR problem. DPSMRec includes three main modules, i.e., source embedding generation module, target rating prediction module, and differentially private sparse mapping module. Specifically, the source embedding generation module and target rating prediction module are set to exploit the ratings and review in each domain. The differentially private sparse mapping module aims to transfer knowledge among the overlapped users via Wasserstein distance privately. To obtain more accurate mapping solutions, we further propose differentially private enhanced sparse optimal transport via fused Gromov-Wasserstein distance which can consider both structure and semantic information among the users across domains. Our empirical study on Amazon and Douban datasets demonstrates that DPSMRec significantly outperforms the state-of-the-art models under the PPCDR setting.

Zhenghong Lin, Yanchao Tan, Yunfei Zhan, Weiming Liu 0005, Fan Wang 0020, Chaochao Chen 0001, Shiping Wang, Carl Yang 0001

With the rapid growth of multimedia-sharing platforms (e.g. Twitter and TikTok), multimedia recommender systems have become fundamental for helping users alleviate information overload and discover items of interest. Existing multimedia recommendation methods often incorporate various auxiliary modalities (e.g., visual, textual, and acoustic) to describe item characteristics and improve task performance. However, these methods usually assume that each item is associated with complete modalities, ignoring the prevalence of missing modality issues in real-world scenarios. To deal with the challenge of missing modalities, in this paper, we propose a novel framework of Contrastive Intra- and Inter-Modality Generation (CI2MG) for enhancing incomplete multimedia recommendation. We first develop a contrastive intra- and inter-modality generation module for the missing modalities, where the intra-modality representation is updated through clustering-based hypergraph convolution and inter-modality representation is obtained by optimal transport between different modalities. To tackle the challenge of insufficient and incomplete supervision labels during intra- and inter-modality generation, a modality-aware contrastive learning paradigm is introduced based on an augmentation between the intra-modality view and inter-modality view. Furthermore, to learn task-related representations from the generative modalities and further improve the performance of recommendation, we design an enhanced multimedia recommendation module to alleviate the influences driven by task-irrelevant noise. Extensive experiments on real-world datasets show the superiority of our proposed CI2MG framework in offering great potential for personalized multimedia recommendation over the state-of-the-art baselines regarding Recall, NDCG, and Precision metrics.

Hongru Liang, Jingyao Liu, Yuanxin Xiang, Jiachen Du, Lanjun Zhou, Shushen Pan, Wenqiang Lei

Towards sufficient music searching, it is vital to form a complete set of labels for each song. However, current solutions fail to resolve it as they cannot produce diverse enough mappings to make up for the information missed by the gold labels. Based on the observation that such missing information may already be presented in user comments, we propose to study the automated music labeling in an essential but under-explored setting, where the model is required to harvest more diverse and valid labels from the users' comments given limited gold labels. To this end, we design an iterative framework (DiVa) to harvest more Diverse and Valid labels from user comments for music. The framework makes a classifier able to form complete sets of labels for songs via pseudo-labels inferred from pre-trained classifiers and a novel joint score function. The experiment on a densely annotated testing set reveals the superiority of the DiVa over state-of-the-art solutions in producing more diverse labels missed by the gold labels. We hope our work can inspire future research on automated music labeling.

Huafeng Liu 0001, Mingjie Zhou, Liping Jing, Michael K. Ng 0001

The cold-start recommendation has been one of the most central problems in online platforms where new users or items arrive continuously. Although existing meta-learning based models with globally sharing knowledge show good performance in most cold-start scenarios, the ability to handle challenges on intention heterogeneity and prediction uncertainty is missing, and these two challenges are particularly evident in cold-start scenarios with fewer interaction data. To tackle the above challenges, in this paper, we present an uncertainty-aware Stochastic Meta Process with Doubly Intention learning (DISMP) for the cold-start recommendation, which has promising properties in uncertainty quantification. With the aid of the meta-learning stochastic process, DISMP can store general knowledge by capturing the relevance of different user-item pairs in terms of intentions and concepts, which is capable of rapid adaptation to new users and items. Furthermore, intentions with general and specific levels are extracted by doubly distinguishing the role of latent variables, which is able to capture the dependencies across different types of intentions and concepts. Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines on cold-start recommendations with different perspectives.

Shuanglin Yan, Neng Dong, Jun Liu 0036, Liyan Zhang 0002, Jinhui Tang 0001

Text-to-image person re-identification (TIReID) retrieves pedestrian images of the same identity based on a query text. However, existing methods typically treat it as a one-to-one image-text matching problem, only focusing on the relationship between image-text pairs within a view. The many-to-many matching between image-text pairs across views under the same identity is not taken into account, which is one of the main reasons for the poor performance of existing methods. To this end, we propose a simple yet effective framework, called LCR2S, for modeling many-to-many correspondences of the same identity by learning comprehensive representations for both modalities from a novel perspective. We construct a support set for each image (text) by using other images (texts) under the same identity and design a multi-head attentional fusion module to fuse the image (text) and its support set. The resulting enriched image and text features are aligned to train a "richer" TIReID model with many-to-many correspondences. Since the support set is unavailable during inference, we propose to distill the knowledge learned by the "richer" model into a lightweight model for inference with a single image/text as input. The lightweight model focus on semantic association and reasoning of multi-view information, which can generate a comprehensive representation containing multi-view information with only a single-view input to perform accurate text-to-image retrieval during inference. In particular, we use the intra-modal features and inter-modal semantic relations of the "richer" model to supervise the lightweight model to inherit its powerful capability. Extensive experiments demonstrate the effectiveness of LCR2S, and it also achieves new state-of-the-art performance on three popular TIReID datasets.

Zhongxuan Han, Chaochao Chen 0001, Xiaolin Zheng, Weiming Liu 0005, Jun Wang 0020, Wenjie Cheng, Yuyuan Li 0001

Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. The existing research on UOF is limited and fails to deal with the root cause of the UOF issue: the learning process between advantaged and disadvantaged users is unfair. To tackle this issue, we propose an In-processing User Constrained Dominant Sets (In-UCDS) framework, which is a general framework that can be applied to any backbone recommendation model to achieve user-oriented fairness. We split In-UCDS into two stages, i.e., the UCDS modeling stage and the in-processing training stage. In the In-UCDS modeling stage, for each disadvantaged user, we extract a constrained dominant set (a user cluster) containing some advantaged users that are similar to it. In the in-processing training stage, we move the representations of disadvantaged users closer to their corresponding cluster by calculating a fairness loss. By combining the fairness loss with the original backbone model loss, we address the UOF issue and maintain the overall recommendation performance simultaneously. Comprehensive experiments on three real-world datasets demonstrate that In-UCDS outperforms the state-of-the-art methods, leading to a fairer model with better overall recommendation performance.

Xiyue Gao, Zhuoqi Ma, Jiangtao Cui, Xiaofang Xia, Cai Xu

Temporal recommendation systems aim to suggest items to users at the optimal time. However, the significant imbalance of items in the training data poses a major challenge to predictive accuracy. Existing approaches attempt to alleviate this issue by modifying the loss function or utilizing resampling techniques, but such approaches may inadvertently amplify the specificity of certain behaviors. To address this problem, we propose a novel temporal recommendation algorithm called HCRec. The algorithm adopts a transformer-like architecture, focuses on decoupling variant and invariant information according to hierarchical categories, and then uses invariant prototypes to mitigate the impact of special behaviors. Although category-enhanced recommendation is not a new concept, current approaches have considerable limitations. They usually incorporate the category information into a black-box model as another input sequence, which can easily lead to overfitting. In contrast, HCRec creates a hierarchical relationship tree of items and categories, and establishes mapping rules to classify nodes as variant or invariant. Then, an attention model is used to extract potential variant node features from historical behaviors and enhance them with external knowledge. The invariant node features are fused based on the relationship tree, and finally used for recommendation. On top of this, two time kernel functions are designed to simulate short-term and long-period time effects. Experiments on real-world datasets show that hierarchical category-enhanced prototype learning helps alleviate the imbalance problem, enabling HCRec to outperform all baseline methods.

Tianyu Chang, Xun Yang 0001, Xin Luo 0006, Wei Ji 0008, Meng Wang 0001

Visual Instance Retrieval (VIR) is a hot research topic for its wide application in real world, such as object re-identification in smart city scenarios. However, due to the limited style diversity in source training data, most existing VIR models always fail to generalize well to unseen domain. How to improve the generalizability of VIR models has received increasing attention in most recent years. In this paper, we pay attention to the Single Domain Generalization (SDG) based VIR task, a more challenging but practical problem, where the model is only trained on single domain data and directly evaluated on unseen target domain without any fine-tuning or adaptations. In this case, the limited style variance in training data may cause the model learning incorrect reliance on the superficial style feature and reduce the generalizability of the model. To address this issue, we propose a novel Style-Invariant robust Representation Learning (SIRL) method for the challenging task, which mainly aims to first diversify the training data with style augmentation, and then enforce the model to learn style-invariant features. Specifically, we first design an adversarial style synthesis module which learns to synthesize diverse augmented samples with adversarially learned styles. Then, we devise an invariant feature learning module to minimize cross-domain feature inconsistency between source images and style-augmented images for capturing domain-invariant instance features. In this way, we can prevent the model from over-exploiting semantic content-independent cues (e.g., color) as shortcut features, thereby estimating the pairwise instance similarity more robustly. We integrate our SIRL method with SOTA VIR networks and evaluate its effectiveness on several public benchmark datasets. Extensive experiments clearly show that the SIRL method can substantially improve the generalizability of existing VIR networks in the challenging SDG-VIR setting.

Ravikiran Parameshwara, Ibrahim Radwan, Akshay Asthana, Iman Abbasnejad, Ramanathan Subramanian, Roland Goecke

Whilst deep learning techniques have achieved excellent emotion prediction, they still require large amounts of labelled training data, which are (a) onerous and tedious to compile, and (b) prone to errors and biases. We propose Multi-Task Contrastive Learning for Affect Representation (MT-CLAR) for few-shot affect inference. MT-CLAR combines multi-task learning with a Siamese network trained via contrastive learning to infer from a pair of expressive facial images (a) the (dis)similarity between the facial expressions, and (b) the difference in valence and arousal levels of the two faces. We further extend the image-based MT-CLAR framework for automated video labelling where, given one or a few labelled video frames (termed support-set), MT-CLAR labels the remainder of the video for valence and arousal. Experiments are performed on the AFEW-VA dataset with multiple support-set configurations; moreover, supervised learning on representations learnt via MT-CLAR are used for valence, arousal and categorical emotion prediction on the AffectNet and AFEW-VA datasets. The results show that valence and arousal predictions via MT-CLAR are very comparable to the state-of-the-art (SOTA), and we significantly outperform SOTA with a support-set ≈6% the size of the video dataset.

Hao Yu 0014, Danielle A. Allessio, Will Lee 0002, William Rebelsky, Frank Sylvia, Tom Murray 0001, John J. Magee, Ivon Arroyo, Beverly P. Woolf, Sarah Adel Bargal 等

A key to personalized online learning is presenting content at an appropriate difficulty level; content that is too difficult can cause frustration and content that is too easy may result in boredom. Appropriate content can improve students' engagement and learning outcome. In this research, we propose a computer vision enhanced problem selector (COVES), a deep learning model to select a personalized difficulty level for each student. A combination of visual information and traditional log data is used to predict student-problem interactions, which are then used to guide problem difficulty selection in real time. COVES was trained on a dataset of fifty-one sixth-grade students interacting with the online math tutor MathSpring. Once COVES was integrated into the tutor, its effectiveness was tested with twenty-two seventh-grade students in controlled experiments. Students who received problems at an appropriate difficulty level, based on real-time predictions of their performance, demonstrated improved engagement with the math tutor. Results indicate that COVES leads to higher mastery of math concepts, better timing, and higher scores, thus providing a positive learning experience for the participants.

Yi Wu 0019, Shangfei Wang, Yanan Chang

Existing methods for facial expression recognition (FER) lack the utilization of prior facial knowledge, primarily focusing on expression-related regions while disregarding explicitly processing expression-independent information. This paper proposes a patch-aware FER method that incorporates facial keypoints to guide the model and learns precise representations through two collaborative streams, addressing these issues. First, facial keypoints are detected using a facial landmark detection algorithm, and the facial image is divided into equal-sized patches using the Patch Embedding Module. Then, a correlation is established between the keypoints and patches using a simplified conversion relationship. Two collaborative streams are introduced, each corresponding to a specific mask strategy. The first stream masks patches corresponding to the keypoints, excluding those along the facial contour, with a certain probability. The resulting image embedding is input into the Encoder to obtain expression-related features. The features are passed through the Decoder and Classifier to reconstruct the masked patches and recognize the expression, respectively. The second stream masks patches corresponding to all the above keypoints. The resulting image embedding is input into the Encoder and Classifier successively, with the resulting logit approximating a uniform distribution. Through the first stream, the Encoder learns features in the regions related to expression, while the second stream enables the Encoder to better ignore expression-independent information, such as the background, facial contours, and hair. Experiments on two benchmark datasets demonstrate that the proposed method outperforms state-of-the-art methods.

Zaijing Li, Ting-En Lin, Yuchuan Wu, Meng Liu 0006, Fengxiao Tang, Ming Zhao 0007, Yongbin Li

Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information. It consists of several subtasks, such as emotion recognition in conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis presents numerous challenges, including modality alignment, unified input/output forms, and dataset bias. To address these challenges, we propose a Task-Specific Prompt method to jointly model subtasks and introduce a multimodal generative framework called UniSA. Additionally, we organize the benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation benchmark, SAEval. We design novel pre-training tasks and training methods to enable the model to learn generic sentiment knowledge among subtasks to improve the model's multimodal sentiment perception ability. Our experimental results show that UniSA performs comparably to the state-of-the-art on all subtasks and generalizes well to various subtasks in sentiment analysis.

Yucheng Liu, Ziyu Jia, Haichao Wang

Emotion recognition using multi-modal physiological signals is an emerging field in affective computing that significantly improves performance compared to unimodal approaches. The combination of Electroencephalogram(EEG) and Galvanic Skin Response(GSR) signals are particularly effective for objective and complementary emotion recognition. However, the high cost and inconvenience of EEG signal acquisition severely hinder the popularity of multi-modal emotion recognition in real-world scenarios, while GSR signals are easier to obtain. To address this challenge, we propose EmotionKD, a framework for cross-modal knowledge distillation that simultaneously models the heterogeneity and interactivity of GSR and EEG signals under a unified framework. By using knowledge distillation, fully fused multi-modal features can be transferred to an unimodal GSR model to improve performance. Additionally, an adaptive feedback mechanism is proposed to enable the multi-modal model to dynamically adjust according to the performance of the unimodal model during knowledge distillation, which guides the unimodal model to enhance its performance in emotion recognition. Our experiment results demonstrate that the proposed model achieves state-of-the-art performance on two public datasets. Furthermore, our approach has the potential to reduce reliance on multi-modal data with lower sacrificed performance, making emotion recognition more applicable and feasible. The source code is available at https://github.com/YuchengLiu-Alex/EmotionKD

Licai Sun, Zheng Lian 0004, Bin Liu 0041, Jianhua Tao 0001

Dynamic facial expression recognition (DFER) is essential to the development of intelligent and empathetic machines. Prior efforts in this field mainly fall into supervised learning paradigm, which is severely restricted by the limited labeled data in existing datasets. Inspired by recent unprecedented success of masked autoencoders (e.g., VideoMAE), this paper proposes MAE-DFER, a novel self-supervised method which leverages large-scale self-supervised pre-training on abundant unlabeled data to largely advance the development of DFER. Since the vanilla Vision Transformer (ViT) employed in VideoMAE requires substantial computation during fine-tuning, MAE-DFER develops an efficient local-global interaction Transformer (LGI-Former) as the encoder. Moreover, in addition to the standalone appearance content reconstruction in VideoMAE, MAE-DFER also introduces explicit temporal facial motion modeling to encourage LGI-Former to excavate both static appearance and dynamic motion information. Extensive experiments on six datasets show that MAE-DFER consistently outperforms state-of-the-art supervised methods by significant margins (e.g., +6.30% UAR on DFEW and +8.34% UAR on MAFW), verifying that it can learn powerful dynamic facial representations via large-scale self-supervised pre-training. Besides, it has comparable or even better performance than VideoMAE, while largely reducing the computational cost (about 38% FLOPs). We believe MAE-DFER has paved a new way for the advancement of DFER and can inspire more relevant research in this field and even other related tasks. Codes and models are publicly available at https://github.com/sunlicai/MAE-DFER.

Zhihe Zhao, Dongdong Weng, Hanzhi Guo, Jing Hou, Jixiang Zhou

This paper proposes a framework that utilizes skinning decomposition to automatically generate facial rigging from 4D expressions. The framework inputs a predefined rigging template and an actor's 4D facial expressions, including a neutral expression, as well as a group of arbitrary expressions. The output includes not only the linear blend skinning weights and joint positions of the actor's head mesh but also other facial components such as teeth and eyes. Compared to traditional methods, this paper applies a soft constraint to optimize joint positions and imposes a fixed sparsity distribution constraint to improve weight distribution. To further enhance rigging efficiency, this paper leverages GPU expression-parallel and CPU vertex-parallel strategies for joint transformation and weight updates, respectively. The experiments show that the proposed method generates high-fidelity facial rigging that outperforms existing solutions in terms of computational speed, joint position correctness, weight distribution correctness, or computational cost.

Sidharth Anand, Naresh Kumar Devulapally, Sreyasee Das Bhattacharjee, Junsong Yuan 0001

Evaluating speaker emotion in conversations is crucial for various applications requiring human-computer interaction. However, co-occurrences of multiple emotional states (e.g. 'anger' and 'frustration' may occur together or one may influence the occurrence of the other) and their dynamic evolution may vary dramatically due to the speaker's internal (e.g., influence of their personalized socio-cultural-educational and demographic backgrounds) and external contexts. Thus far, the previous focus has been on evaluating only the dominant emotion observed in a speaker at a given time, which is susceptible to producing misleading classification decisions for difficult multi-labels during testing. In this work, we present Self-supervised Multi- Label Peer Collaborative Distillation (SeMuL-PCD) Learning via an efficient Multimodal Transformer Network, in which complementary feedback from multiple mode-specific peer networks (e.g.transcript, audio, visual) are distilled into a single mode-ensembled fusion network for estimating multiple emotions simultaneously. The proposed Multimodal Distillation Loss calibrates the fusion network by minimizing the Kullback-Leibler divergence with the peer networks. Additionally, each peer network is conditioned using a self-supervised contrastive objective to improve the generalization across diverse socio-demographic speaker backgrounds. By enabling peer collaborative learning that allows each network to independently learn their mode-specific discriminative patterns,SeMUL-PCD is effective across different conversation environments. In particular, the model not only outperforms the current state-of-the-art models on several large-scale public datasets (e.g., MOSEI, EmoReact and ElderReact), but with around 17% improved weighted F1-score in the cross-dataset experimental settings. The model also demonstrates an impressive generalization ability across age and demography-diverse populations.