Most current methods for image emotion analysis suffer from the affective gap, in which features directly extracted from images are supervised by a single emotional label, which may not align with users' perceived emotions. To effectively address this limitation, this paper introduces a novel multi-stage perception approach inspired by the human staged emotion perception process. The proposed approach comprises three perception modules: entity perception, attribute perception, and emotion perception. The entity perception module identifies entities in images, while the attribute perception module captures the attribute content associated with each entity. Finally, the emotion perception module combines entity and attribute information to extract emotion features. Pseudo-labels of entities and attributes are generated through image segmentation and vision-language models to provide auxiliary guidance for network learning. A progressive understanding of entities and attributes allows the network to hierarchically extract semantic-level features for emotion analysis. Comprehensive experiments on image emotion classification, regression, and distribution learning demonstrate the superior performance of our multi-stage perception network.

Although video affective content analysis has great potential in many applications, it has not been thoroughly studied due to limited datasets. In this paper, we construct a large-scale video affective dataset with danmu (VAD). It consists of 19,267 elaborately segmented video clips from user-generated videos. The VAD dataset is annotated by the crowdsourcing platform with discrete valence, arousal, and primary emotions, as well as the comparison of valence and arousal between two consecutive video clips. Unlike previous datasets, including only video clips, our proposed dataset also provides danmu, which is the real-time comment from users as they watch a video. Danmu provides extra information for video affective content analysis. As a preliminary assessment of the usability of our dataset, an analysis of inter-annotator consistency for each label is conducted using weighted Fleiss' Kappa, regular Fleiss' Kappa, intraclass correlation coefficient, and percent consensus. Besides, we also perform a statistical analysis of labels and danmu. Finally, video affective content analysis is conducted on our dataset and three typical methods (i.e., TFN, MulT, and MISA) are leveraged to provide benchmarks. We also demonstrate that danmu can significantly improve the performance of the video affective content analysis task on some labels. Our dataset is available for research purposes.

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.

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.

Although temporal patterns inherent in visual and audio signals are crucial for affective video content analysis, they have not been thoroughly explored yet. In this paper, we propose a novel Temporal-Aware Multimodal (TAM) method to fully capture the temporal information. Specifically, we design a cross-temporal multimodal fusion module that applies attention-based fusion to different modalities within and across video segments. As a result, it fully captures the temporal relations between different modalities. Furthermore, a single emotion label lacks supervision for learning representation of each segment, making temporal pattern mining difficult. We leverage time-synchronized comments (TSCs) as auxiliary supervision, since these comments are easily accessible and contain rich emotional cues. Two TSC-based self-supervised tasks are designed: the first aims to predict the emotional words in a TSC from video representation and TSC contextual semantics, and the second predicts the segment in which the TSC appears by calculating the correlation between video representation and TSC embedding. These self-supervised tasks are used to pre-train the cross-temporal multimodal fusion module on a large-scale video-TSC dataset, which is crawled from the web without labeling costs. These self-supervised pre-training tasks prompt the fusion module to perform representation learning on segments including TSC, thus capturing more temporal affective patterns. Experimental results on three benchmark datasets show that the proposed fusion module achieves state-of-the-art results in affective video content analysis. Ablation studies verify that after TSC-based pre-training, the fusion module learns more segments' affective patterns and achieves better performance.