23 accepted papers
Facial expression recognition (FER) in the wild is severely hampered by label noise and annotation ambiguity. Existing methods, including sample selection, label ensembling, and consistency regularization, primarily rely on ordinary label supervision and offer limited control over non-target predict
Mainstream Automatic Speech Recognition (ASR) systems excel at transcribing lexical content, but largely fail to recognize nonverbal vocalizations (NVs) embedded in speech, such as sighs, laughs, and coughs. This capability is important for a comprehensive understanding of human communication, as NV…
Multimodal large language models (MLLMs) have advanced rapidly in recent years. However, existing approaches for vision tasks often rely on indirect representations, such as generating coordinates as text for detection, which limits performance and prevents dense prediction tasks like segmentation.…
Recently, advancements in AI counseling based on large language models have shown significant progress. However, existing studies employ a one-time generation approach to synthesize multi-turn dialogue samples, resulting in low therapy fidelity and failing to capture the decision-making rationale be
Studies of speech representation enhance zero-shot Text-to-Speech by mapping text to intermediate representations before generating speech. However, using representations often struggles to balance linguistic, para-linguistic, and non-linguistic information in speech during the synthesis phase. Addi…
Diffeomorphic-based cortical surface reconstruction typically involves a series of deformation processes to extract the cerebral cortex from brain magnetic resonance images (MRI). While most methods are designed for adult brains using Neural Ordinary Differential Equations (NODE) with fixed step siz…
Open-domain dialogue systems aim to generate natural and engaging conversations, providing significant practical value in real applications such as social robotics and personal assistants. The advent of large language models (LLMs) has greatly advanced this field by improving context understanding a
Multimodal Emotion and Intent Joint Understanding (MEIJU) aims to decode the semantic information expressed in the multimodal dialogues while inferring the emotions and intents, providing users with a more humanized human-machine interaction experience. However, challenges such as difficulties in da…
Currently, large language models (LLMs) have made significant progress in the field of psychological counseling. However, existing mental health LLMs overlook a critical issue where they do not consider the fact that different psychological counselors exhibit different personal styles, including lin…
In this paper, our objective is to develop a multi-agent financial system that incorporates simulated trading , a technique extensively utilized by financial professionals. While current LLM-based agent models demonstrate competitive performance, they still exhibit significant deviations from real-w
Traditional Retrieval-Augmented Generation (RAG) frameworks often segment documents into larger chunks to preserve contextual coherence, inadvertently introducing redundant noise. Recent advanced RAG frameworks have shifted toward finer-grained chunking to improve precision. However, in long-documen
In conventional deep speaker embedding frameworks, the pooling layer aggregates all frame-level features over time and computes their mean and standard deviation statistics as inputs to subsequent segment-level layers. Such statistics pooling strategy produces fixed-length representations from varia…
Recent advancements of general domain oriented Role-playing Agents (RPAs) have enabled the agents to maintain character properties in a wide spectrum of daily tasks beyond mere scenario based chit-chatting. Nonetheless, current works lacks consideration of replicating internal properties of characte
When confronting long document information retrieval for Query-Focused Summarization(QFS), Traditional Retrieval-Augmented Generation(RAG) frameworks struggle to retrieve all relevant knowledge points, and the chunking and retrieve strategies of existing frameworks may disrupt the connections betwee…
Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder worldwide, characterized by progressive motor and non-motor symptoms. Unfortunately, there are no definitive PD modifying therapies, so accurate course prediction in advance and appropriate medical adjustment are essent…
Enabled by multi-head self-attention, Transformer has exhibited remarkable results in speech emotion recognition (SER). Compared to the original full attention mechanism, window-based attention is more effective in learning fine-grained features while greatly reducing model redundancy. However, emot…
Speech emotion recognition is crucial to human-computer interaction. The temporal regions that represent different emotions scatter in different parts of the speech locally. Moreover, the temporal scales of important information may vary over a large range within and across speech segments. Although…
Multi-modal emotion recognition is crucial for human-computer interaction. Many existing algorithms attempt to achieve multi-modal interactions through a cross-attention mechanism. Due to the problems of noise introduction and heavy computation in the original attention mechanism, window attention h…
Large language models (LLMs) have been widely applied in various fields due to their excellent capability for memorizing knowledge and chain of thought (CoT). When these language models are applied in the field of psychological counseling, they often rush to provide universal advice. However, when u…
Personality recognition is one of the core technologies in human-machine interaction, which has received increasing attention. Previous works mainly focus on essays or monologues, while personality traits reveal more in the interactions with others. Due to the lack of appropriate datasets, a few app…
Facial expression recognition (FER) has recently attracted attention in computer vision. However, existing methods mostly focus on the explicit performance and overlook their computational resources and memory consumption. Hence, achieving promising performance while maintaining the efficiency of mo…
Speech emotion recognition is a vital contributor to the next generation of human-computer interaction (HCI). However, current existing small-scale databases have limited the development of related research. In this paper, we present LSSED, a challenging large-scale english speech emotion dataset, w…
Siamese series of tracking networks have shown great potentials in achieving balanced accuracy and beyond real-time speed. However, most of existing siamese trackers only consider appearance features of first frame, and hardly benefit from interframe information. The lack of latest temporal transfor…