Richang Hong
Biographic Data
| ID | 10054109 |
|---|---|
| NAME | Richang Hong |
| GIVEN NAMES | Richang |
| FAMILY NAME | Hong |
| SIGNATURE | HONG R |
| AFFILIATIONS | Hefei University of Technology |
| ORCID | 0000-0001-5461-3986 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 13 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
CFLip
Lipreading refers to translating the lip movements observed in a video of a speaker into corresponding textual outputs, providing a visual alternative to auditory communication for individuals who are deaf or hard of hearing. Existing lipreading methods typically independently learn the lip movements of each speaker. This results in the model being highly sensitive to the individual visual features (lip color/shape) of the speakers in the trainin…
Semantic-Oriented Robust Sentence Level Text Watermark for Large Language Models
Subthreshold Depression Detection With Text-Guided Multimodal Learning
Depression, a widespread global mental health problem, affects millions of people annually, making early detection of subclinical depression crucial for timely intervention. Current automatic depression detection (ADD) methods, valuable for diagnosis, often neglect subthreshold populations and face difficulties in extracting diagnostic data from long sequences of multimodal information. These methods also inadequately leverage text modality, whic…
Explainable and Interactive LLMs-Augmented Depression Detection in Social Media
Depression detection based on social media content has received increasing attention in recent years, as it allows for early diagnosis before the user’s psychological state deteriorates. Although traditional methods of depression detection can provide a classification of whether the user is depressed or not, they cannot provide human-like explanations and interactions. In this article, we propose a next-generation paradigm for depression detectio…
Redgcn
Rating prediction is a challenging task in review-aware recommendation. Although current methods effectively combine collaborative signals with review data, they fail to differentiate user preferences across various ratings and overlook the independence between these ratings. In this article, we emphasize the importance of independence modeling among representations for different rating levels. To this end, we propose a rating-oriented explicit d…
Emotion Separation and Recognition From a Facial Expression by Generating the Poker Face With Vision Transformers
Representation learning and feature disentanglement have garnered significant research interest in the field of facial expression recognition (FER). The inherent ambiguity of emotion labels poses challenges for conventional supervised representation learning methods. Moreover, directly learning the mapping from a facial expression image to an emotion label lacks explicit supervision signals for capturing fine-grained facial features. In this arti…
Emcrl
As one representative framework of self-supervised learning (SSL), contrastive learning (CL) has drawn enormous attention in the representation learning area. By pulling together a “positive” example and an anchor, as well as pushing away many “negative” examples from the anchor, CL is able to generate high-quality representations for the data of different modalities. Therefore, the qualities of selected positive and negative examples are critica…
A Prompt-Based Topic-Modeling Method for Depression Detection on Low-Resource Data
Depression has a large impact on one’s personal life, especially during the COVID-19 pandemic. People have been trying to develop reliable methods for the depression detection task. Recently, methods based on deep learning have attracted much attention from the research community. However, they still face the challenge that data collection and annotation are difficult and expensive. In many real-world applications, only a small number of or even …
Multimodal Hierarchical Graph Collaborative Filtering for Multimedia-Based Recommendation
Multimedia-based recommendation (MMRec) is a challenging task, which goes beyond the collaborative filtering (CF) schema that only captures collaborative signals from interactions and explores multimodal user preference cues hidden in complex multimedia content. Despite the significant progress of current solutions for MMRec, we argue that they are limited by multimodal noise contamination. Specifically, a considerable amount of preference-irrele…
Dual Graph Neural Networks for Dynamic Users’ Behavior Prediction on Social Networking Services
Social network services (SNSs) provide platforms where users engage in social link behavior (e.g., predicting social relationships) and consumption behavior. Recent advancements in deep learning for recommendation and link prediction explore the symbiotic relationships between these behaviors, leveraging social influence theory and user homogeneity, i.e., users tend to accept recommendations from social friends and connect with like-minded users.…
Automatic Depression Detection via Learning and Fusing Features From Visual Cues
Depression is one of the most prevalent mental disorders, which seriously affects one’s life. Traditional depression diagnostics commonly depend on rating with scales, which can be labor-intensive and subjective. In this context, automatic depression detection (ADD), aiming to assist medical experts in their diagnosis and analysis, has been attracting more attention for its better objectivity and fewer laborious interventions. A typical ADD model…
Hierarchical Multifeature Fusion via Audio-Response-Level Modeling for Depression Detection
The clinical diagnosis of major depressive disorder (MDD) relying heavily on the subjective judgment assisted by questionnaires, could result in a low detection rate of MDD. Automatic depression detection (ADD) technology based on physiological and psychological information provides an objective and quantitative way for MDD detection. As a useful data modality, audio signals have attracted increasing interest in mental disorder detection. However…
Joint Multi-Grained Popularity-Aware Graph Convolution Collaborative Filtering for Recommendation
Graph convolution networks (GCNs), with their efficient ability to capture high-order connectivity in graphs, have been widely applied in recommender systems. Stacking multiple neighbor aggregation is the major operation in GCNs. It implicitly captures popularity features because the number of neighbor nodes reflects the popularity of a node. However, existing GCN-based methods ignore a universal problem: users’ sensitivity to item popularity is …
No prominent works on this page.
Automatic Depression Detection via Learning and Fusing Features From Visual Cues
Depression is one of the most prevalent mental disorders, which seriously affects one’s life. Traditional depression diagnostics commonly depend on rating with scales, which can be labor-intensive and subjective. In this context, automatic depression detection (ADD), aiming to assist medical experts in their diagnosis and analysis, has been attracting more attention for its better objectivity and fewer laborious interventions. A typical ADD model…
Hierarchical Multifeature Fusion via Audio-Response-Level Modeling for Depression Detection
The clinical diagnosis of major depressive disorder (MDD) relying heavily on the subjective judgment assisted by questionnaires, could result in a low detection rate of MDD. Automatic depression detection (ADD) technology based on physiological and psychological information provides an objective and quantitative way for MDD detection. As a useful data modality, audio signals have attracted increasing interest in mental disorder detection. However…
Joint Multi-Grained Popularity-Aware Graph Convolution Collaborative Filtering for Recommendation
Graph convolution networks (GCNs), with their efficient ability to capture high-order connectivity in graphs, have been widely applied in recommender systems. Stacking multiple neighbor aggregation is the major operation in GCNs. It implicitly captures popularity features because the number of neighbor nodes reflects the popularity of a node. However, existing GCN-based methods ignore a universal problem: users’ sensitivity to item popularity is …
A Prompt-Based Topic-Modeling Method for Depression Detection on Low-Resource Data
Depression has a large impact on one’s personal life, especially during the COVID-19 pandemic. People have been trying to develop reliable methods for the depression detection task. Recently, methods based on deep learning have attracted much attention from the research community. However, they still face the challenge that data collection and annotation are difficult and expensive. In many real-world applications, only a small number of or even …
Multimodal Hierarchical Graph Collaborative Filtering for Multimedia-Based Recommendation
Multimedia-based recommendation (MMRec) is a challenging task, which goes beyond the collaborative filtering (CF) schema that only captures collaborative signals from interactions and explores multimodal user preference cues hidden in complex multimedia content. Despite the significant progress of current solutions for MMRec, we argue that they are limited by multimodal noise contamination. Specifically, a considerable amount of preference-irrele…
Dual Graph Neural Networks for Dynamic Users’ Behavior Prediction on Social Networking Services
Social network services (SNSs) provide platforms where users engage in social link behavior (e.g., predicting social relationships) and consumption behavior. Recent advancements in deep learning for recommendation and link prediction explore the symbiotic relationships between these behaviors, leveraging social influence theory and user homogeneity, i.e., users tend to accept recommendations from social friends and connect with like-minded users.…
Redgcn
Rating prediction is a challenging task in review-aware recommendation. Although current methods effectively combine collaborative signals with review data, they fail to differentiate user preferences across various ratings and overlook the independence between these ratings. In this article, we emphasize the importance of independence modeling among representations for different rating levels. To this end, we propose a rating-oriented explicit d…
Emotion Separation and Recognition From a Facial Expression by Generating the Poker Face With Vision Transformers
Representation learning and feature disentanglement have garnered significant research interest in the field of facial expression recognition (FER). The inherent ambiguity of emotion labels poses challenges for conventional supervised representation learning methods. Moreover, directly learning the mapping from a facial expression image to an emotion label lacks explicit supervision signals for capturing fine-grained facial features. In this arti…
Emcrl
As one representative framework of self-supervised learning (SSL), contrastive learning (CL) has drawn enormous attention in the representation learning area. By pulling together a “positive” example and an anchor, as well as pushing away many “negative” examples from the anchor, CL is able to generate high-quality representations for the data of different modalities. Therefore, the qualities of selected positive and negative examples are critica…
CFLip
Lipreading refers to translating the lip movements observed in a video of a speaker into corresponding textual outputs, providing a visual alternative to auditory communication for individuals who are deaf or hard of hearing. Existing lipreading methods typically independently learn the lip movements of each speaker. This results in the model being highly sensitive to the individual visual features (lip color/shape) of the speakers in the trainin…
Semantic-Oriented Robust Sentence Level Text Watermark for Large Language Models
Subthreshold Depression Detection With Text-Guided Multimodal Learning
Depression, a widespread global mental health problem, affects millions of people annually, making early detection of subclinical depression crucial for timely intervention. Current automatic depression detection (ADD) methods, valuable for diagnosis, often neglect subthreshold populations and face difficulties in extracting diagnostic data from long sequences of multimodal information. These methods also inadequately leverage text modality, whic…
Explainable and Interactive LLMs-Augmented Depression Detection in Social Media
Depression detection based on social media content has received increasing attention in recent years, as it allows for early diagnosis before the user’s psychological state deteriorates. Although traditional methods of depression detection can provide a classification of whether the user is depressed or not, they cannot provide human-like explanations and interactions. In this article, we propose a next-generation paradigm for depression detectio…
Computer Science (12 works) · Artificial Intelligence (11 works) · Machine learning (5 works) · Advanced Graph Neural Networks (4 works) · Graph (4 works) · Mental Health via Writing (4 works) · Sentiment Analysis and Opinion Mining (4 works) · Theoretical Computer Science (4 works) · Emotion and Mood Recognition (3 works) · Natural language processing (3 works)