31 accepted papers
Autoregressive (AR) frameworks have recently achieved remarkable progress in zero-shot text-to-speech (TTS) by leveraging discrete speech tokens and large language model techniques. Despite their success, existing AR-based zero-shot TTS systems face two critical limitations: (i) an inherent speed-qu…
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
With the rapid development of multimodal large language models (MLLMs), deploying them on low-resource devices remains challenging. Beyond the model size, long multimodal inputs cause substantial memory overhead in the KV cache, making efficient cache management critical. In this paper, we propose D
Large Language Model (LLM)-based Text-to-Speech (TTS) models have already reached a high degree of naturalness. However, the precision control of TTS inference is still challenging. Although instruction-based Text-to-Speech (Instruct-TTS) models are proposed, these models still lack fine-grained con…
Current fMRI decoders face a performance-fidelity trade-off where efficient ID encoders outperform geometrically-aligned surface-based models. We argue this is an artifact of inefficient surface tokenization and the failure to use anatomy as a predictive signal. We present **NeurIPS**, a framework t…
Open vocabulary Human-Object Interaction (HOI) detection is a challenging task that detects all <human, verb, object> triplets of interest in an image, even those that are not pre-defined in the training set. Existing approaches typically rely on output features generated by large Vision-Language Mo…
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…
Text-to-image person re-identification (ReID) aims to retrieve the images of an interested person based on textual descriptions. One main challenge for this task is the high cost in manually annotating large-scale databases, which affects the generalization ability of ReID models. Recent works handl…
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
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…
The integration of humanoid and animal-shaped robots into specialized domains, such as healthcare, multiterrain operations, and psychotherapy, necessitates a deep understanding of proxemics-the study of spatial behavior that governs effective human-robot interactions. Unlike traditional robots in ma
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…
We introduce HARPER, a novel dataset for 3D body pose estimation and forecasting in dyadic interactions between users and Spot, the quadruped robot manufactured by Boston Dynamics. The key-novelty of HARPER is its focus on the robot’s perspective, i.e., on the data captured by the robot’s sensors. T…
Image-based virtual try-on is an increasingly important task for online shopping. It aims to synthesize images of a specific person wearing a specified garment. Diffusion model-based approaches have recently become popular as they are excellent at image synthesis tasks. However these approaches usua…
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…
Most existing methods realize 3D instance segmentation by extending those models used for 3D object detection or 3D semantic segmentation. However, these non-straightforward methods suffer from two drawbacks: 1) Imprecise bounding boxes or unsatisfactory semantic predictions limit the performance of…
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 challenging research topic that plays a critical role in human-computer interaction. Multimodal inputs further improve the performance as more emotional information is used. However, existing studies learn all the information in the sample while only a small portion o…
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…
In order to enhance the security of automatic speaker verification (ASV) systems, automatic spoofing attack detection, which discriminates the fake audio recordings from genuine human speech, has gain much attention recently. Among various ways of spoofing attacks, replay attacks are one of the most…
Deep learning methods achieve great success recently on many computer vision problems. In spite of these practical successes, optimization of deep networks remains an active topic in deep learning research. In this work, we focus on investigation of the network solution properties that can potential…