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Emotional support conversation (ESC) helps reduce people&#39;s psychological stress and provide emotional value through interactive dialogues. Due to the high cost of crowdsourcing a large ESC corpus, recent attempts use large language models for dialogue augmentation. However, existing approaches largely overlook the social dynamics inherent in ESC, leading to less effective simulations. In this paper, we introduce SocialSim, a novel framework that simulates ESC by integrating key aspects of social interactions: social disclosure and social awareness. On the seeker side, we facilitate social disclosure by constructing a comprehensive persona bank that captures diverse and authentic help-seeking scenarios. On the supporter side, we enhance social awareness by eliciting cognitive reasoning to generate logical and supportive responses. Building upon SocialSim, we construct SSConv, a large-scale synthetic ESC corpus of which quality can even surpass crowdsourced ESC data, as validated by human evaluation. We further train a chatbot on SSConv and demonstrate its state-of-the-art performance in both automatic and human evaluations. We believe SocialSim offers a scalable way to synthesize ESC, making emotional care more accessible and practical. The material for replicating SocialSim has been uploaded in the supplementary Code&amp;Data Appendix.

AAAI 2025

SocialSim: Towards Socialized Simulation of Emotional Support Conversation

Emotional support conversation (ESC) helps reduce people's psychological stress and provide emotional value through interactive dialogues. Due to the high cost of crowdsourcing a large ESC corpus, recent attempts use large language models for dialogue augmentation. However, existing approaches largely overlook the social dynamics inherent in ESC, leading to less effective simulations. In this paper, we introduce SocialSim, a novel framework that simulates ESC by integrating key aspects of social interactions: social disclosure and social awareness. On the seeker side, we facilitate social disclosure by constructing a comprehensive persona bank that captures diverse and authentic help-seeking scenarios. On the supporter side, we enhance social awareness by eliciting cognitive reasoning to generate logical and supportive responses. Building upon SocialSim, we construct SSConv, a large-scale synthetic ESC corpus of which quality can even surpass crowdsourced ESC data, as validated by human evaluation. We further train a chatbot on SSConv and demonstrate its state-of-the-art performance in both automatic and human evaluations. We believe SocialSim offers a scalable way to synthesize ESC, making emotional care more accessible and practical. The material for replicating SocialSim has been uploaded in the supplementary Code&Data Appendix.

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Pretrained visual-language models such as CLIP have made significant advancements in multimodal tasks, including image-text retrieval. However, a major challenge in image-text matching lies in language bias, where models predominantly rely on language priors and neglect to adequately consider the visual content. We thus present Multimodal ASsociation Score (MASS), a training-free framework that reduces the reliance on language priors for better visual accuracy in image-text matching problems. Our method can be seamlessly incorporated into existing visual-language models without necessitating additional training. Our experiments have shown that MASS effectively reduces color, number, and gender biases. Also, it maintains linguistic understanding capability, as demonstrated in numerical results on image-text matching tests with linguistic complexities. Overall, MASS offers a promising solution for enhancing image-text matching performance in visual-language models.

MASS: Overcoming Language Bias in Image-Text Matching

Optical neural networks (ONNs) have attracted great attention due to their low power consumption and high-speed processing.
When training an ONN implemented on a chip with possible fabrication variations, the well-known backpropagation algorithm cannot be executed accurately because the perfect information inside the chip cannot be observed.
Instead, we employ a black-box optimization method such as zeroth-order (ZO) optimization.
In this paper, we first discuss how ONN parameters should be perturbed to search for better values in a black-box manner.
Conventionally, parameter perturbations are sampled from a normal distribution with an identity covariance matrix.
This is plausible if the parameters are not interrelated in a module, like a linear module of an ordinary neural network. 
However, this is not the best way for ONN modules with layered parameters, which are interrelated by optical paths.
We then propose to perturb the parameters by a normal distribution with a special covariance matrix computed by our novel method.
The covariance matrix is designed so that the perturbations appearing at the module output caused by the parameter perturbations become as isotropic as possible to uniformly search for better values.
Experimental results show that the proposed method using the special covariance matrix significantly outperformed conventional methods.

Layered-Parameter Perturbation for Zeroth-Order Optimization of Optical Neural Networks

3D Human Pose Estimation (HPE) is a one-to-many problem by nature, making it challenging to estimate an accurate 3D pose from a single 2D pose. Some prior works have attempted to tackle this problem by using a conditional generative network. They generate 3D poses from a given 2D pose with noises from a standard Gaussian distribution, while the depth distribution is dependent on each posture and more complex than the standard Gaussian distribution. This may lead to inaccurate distribution learning. In this paper, we propose a probabilistic framework called ProPose to address this issue. ProPose employs Pose Instance-Level Gaussian Distribution (PILGD) derived from 3D pose-based self-representation learning to obtain reliable distribution which is able to address pose-dependent depth distribution. To access this PILGD, we utilize normalizing flow, which learns a mapping function between the PILGD and a 2D Pose-Adaptive Gaussian Distribution (PAGD). This converts the problem of directly estimating 3D poses from 2D poses to a mapping problem between PILGD and PAGD using a normalizing flow. Extensive experiments show the advantages of utilizing the PILGD and PAGD. ProPose achieves comparable performances to previous state-of-the-art methods in a multi-hypothesis setting. Notably, ProPose in single-hypothesis setting demonstrates comparable generalization ability to existing state-of-the-art deterministic methods for the first time.

ProPose: Probabilistic 3D Human Pose Estimation with Instance-Level Distribution and Normalizing Flow

Benefiting from large-scale pre-training of text-video pairs, current text-to-video (T2V) diffusion models can generate high-quality videos from the text description. Besides, given some reference images or videos, the parameter-efficient fine-tuning method, i.e. LoRA, can generate high-quality customized concepts, e.g., the specific subject or the motions from a reference video. However, combining the trained multiple concepts from different references into a single network shows obvious artifacts. To this end, we propose CustomTTT, where we can joint custom the appearance and the motion of the given video easily. In detail, we first analyze the prompt influence in the current video diffusion model and find the LoRAs are only needed for the specific layers for appearance and motion customization. Besides, since each LoRA is trained individually, we propose a novel test-time training technique to update parameters after combination utilizing the trained customized models. We conduct detailed experiments to verify the effectiveness of the proposed methods. Our method outperforms several state-of-the-art works in both qualitative and quantitative evaluations. The code will be available.

CustomTTT: Motion and Appearance Customized Video Generation via Test-Time Training

Monitoring a large population of dynamic processes with limited resources presents a significant challenge across various industrial sectors. This is due to 1) the inherent disparity between the available monitoring resources and the extensive number of processes to be monitored and 2) the unpredictable and heterogeneous dynamics inherent in the progression of these processes. Online learning approaches, commonly referred to as bandit methods, have demonstrated notable potential in addressing this issue by dynamically allocating resources and effectively balancing the exploitation of high-reward processes and the exploration of uncertain ones. However, most online learning algorithms are designed for 1) a centralized setting that requires data sharing across processes for accurate predictions or 2) a homogeneity assumption that estimates a single global model from decentralized data. To overcome these limitations and enable online learning in a heterogeneous population under a decentralized setting, we propose a federated collaborative online monitoring method. Our approach utilizes representation learning to capture the latent representative models within the population and introduces a novel federated collaborative UCB algorithm to estimate these models from sequentially observed decentralized data. This strategy facilitates informed monitoring resource allocation. The efficacy of our method is demonstrated through theoretical analysis, simulation studies, and its application to decentralized cognitive degradation monitoring in Alzheimer’s disease.

FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning

Recent advances in diffusion models focus on efficiently handling conditional generative tasks without extra training. The process involves decomposing the result into two components: 1. unconditional sample $u\_{t}$, generated in the absence of conditions. 2. condition correction $\phi$, adjusting $u\_{t}$ to include the guidance image $I\_c$. This adjustment is quantified by the pixel-level measure $\\| \mathcal{A}(\mathcal{D}(\mathfrak{C}'\_{\phi}(u\_t))) - I\_c \\|\_2$, where $\mathcal{D}(z\_t)$ decodes the latent code $z\_{t} = \mathfrak{C}'\_{\phi}(u\_t) = u\_t + \phi $, and the forward operator $\mathcal{A}( x\_t)$ translates the noisy image $ x\_t=\mathcal{D}(z\_t)$ into the guidance domain for comparison with the guidance image $I\_c$. To enhance the fidelity of $\phi$, we propose a learnable latent forward operator $\mathfrak{A}\_{ \theta}(z\_t, t)$, focusing on latent-space consistency  $\\| \mathfrak{A}\_{\theta}(\mathfrak{C}'\_{\phi}(u\_t), t) - \mathcal{E}(I\_c) \\|\_2$ with the expectation that this latent-space consistency approximates the pixel-level fidelity measure. Here, $\mathcal{E}(I\_c)$ acts as the encoder, mapping the guidance image into the latent space. Furthermore a correctional operator $\mathfrak{C}''\_{\psi}(z\_t) = z\_t + \psi $ is proposed to rectify model mismatching in the latent guidance model, thereby refining the consistency constraint to $\\| \mathfrak{A}\_{\theta}(\mathfrak{C}''\_{\psi}(\mathfrak{C}'\_{\phi}(u\_t)), t) - \mathcal{E}(I\_c) \\|\_2$. The determination of the condition term $\phi$ and the correction estimation $\psi$ is akin to solving a blind inverse problem. Our EMControl employs the Expectation-Maximization (EM) algorithm to solve the blind inverse problem during the reverse sampling process. This technique ensures that samples, once consistent with the guidance, are accurately mapped back onto the noisy data manifold, adhering to the data's inherent distribution. The EMControl has proven its effectiveness by delivering superior performance in conditional diffusion generation tasks compared to previous approaches. Moreover, its application to multiple-condition scenarios underscores its versatility and robustness across a range of generative tasks.

EMControl: Adding Conditional Control to Text-to-Image Diffusion Models Via Expectation-Maximization

Speech-driven 3D facial animation has garnered lots of attention thanks to its broad range of applications. Despite recent advancements in achieving realistic lip motion, current methods fail to capture the nuanced emotional undertones conveyed through speech and produce monotonous facial motion. These limitations result in blunt and repetitive facial animations, reducing user engagement and hindering their applicability. To address these challenges, we introduce DEEPTalk, a novel approach that generates diverse and emotionally rich 3D facial expressions directly from speech inputs. To achieve this, we first train DEE (\textbf{D}ynamic \textbf{E}motion \textbf{E}mbedding), which employs probabilistic contrastive learning to forge a joint emotion embedding space for both speech and facial motion. This probabilistic framework captures the uncertainty in interpreting emotions from speech and facial motion, enabling the derivation of emotion vectors from its multifaceted space. Moreover, to generate dynamic facial motion, we design TH-VQVAE (\textbf{T}emporally \textbf{H}ierarchical VQ-VAE) as an expressive and robust motion prior overcoming limitations of VAEs and VQ-VAEs. Utilizing these strong priors, we develop DEEPTalk, A talking head generator that non-autoregressively predicts codebook indices to create dynamic facial motion, incorporating a novel emotion consistency loss. Extensive experiments on various datasets demonstrate the effectiveness of our approach in creating diverse, emotionally expressive talking faces that maintain accurate lip-sync. Source code will be made publicly available soon.

DEEPTalk: Dynamic Emotion Embedding for Probabilistic Speech-Driven 3D Face Animation

Given the large volume of side information from different modalities, multimodal recommender systems have become increasingly vital, as they exploit richer semantic information beyond user-item interactions. Recent works highlight that leveraging Graph Convolutional Networks (GCNs) to explicitly model multimodal item-item relations can significantly enhance recommendation performance. However, due to the inherent over-smoothing issue of GCNs, existing models benefit only from shallow GCNs with limited representation power. This drawback is especially pronounced when facing complex and high-dimensional patterns such as multimodal data, as it requires large-capacity models to accommodate complicated correlations. To this end, in this paper, we investigate bypassing GCNs when modeling multimodal item-item relationship. More specifically, we propose a Topology-aware Multi-Layer Perceptron (TMLP), which uses MLPs instead of GCNs to model the relationships between items. 
TMLP enhances MLPs with topological pruning to denoise item-item relations and intra (inter)-modality learning to integrate higher-order modality correlations.
Extensive experiments on three real-world datasets verify TMLP's superiority over nine baselines. We also find that by discarding the internal message passing in GCNs, which is sensitive to node connections, TMLP achieves significant improvements in both training efficiency and robustness against existing models.

Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs

With the successful transition of Transformers from NLP to CV domains, Vision-Transformers(ViTs) have achieved state-of-the-art performance in many CV tasks. However, backdoor attacks, a significant threat in deep learning, also pose a risk to the security of ViT models. Recently, several backdoor attack methods targeting the patch-level self-attention mechanism in ViTs have been proposed, but they are relatively naive in terms of stealthiness and robustness against defensive measures, lacking in-depth investigation. In this paper, we explore the crucial role of attention-level imperceptibility in backdoor attacks for ViTs and propose an Attention-Imperceptible Backdoor Attacks on Vision Transformers(AIBA). In AIBA, a constrained adversarial perturbation is used as the trigger to achieve visual imperceptibility. Additionally, the trigger is designed to seamlessly implant into the focal areas of the image, ensuring that the trigger receives enough attention from the model without causing anomalies at the attention level. During the backdoor learning process, we designed an efficient constrained bi-level optimization training strategy to implant an effective backdoor in the victim model using the imperceptible trigger. We evaluated the effectiveness of the proposed AIBA across multiple datasets and ViT benchmarks and explored the robustness of AIBA against current ViT-specific defense methods. The experimental results demonstrate that our backdoor attack method can successfully implant a powerful and stealthy backdoor into ViTs.

Attention-Imperceptible Backdoor Attacks on Vision Transformers

In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in many graph mining tasks.
However, scaling them to large graphs is challenging due to the high computational and storage costs of repeated feature propagation and non-linear transformation during training.
One commonly employed approach to address this challenge is model-simplification, which only executes the $\textbf{P}$ropagation ($\textbf{P}$) once in the pre-processing, and  
$\textbf{C}$ombine ($\textbf{C}$) these receptive fields in different ways and then feed them into a simple model for better performance.
Despite their high predictive performance and scalability, these methods still face two limitations.
First, existing approaches mainly focus on exploring different $\textbf{C}$ methods from the model perspective, neglecting the crucial problem of performance degradation with increasing $\textbf{P}$ depth from the data-centric perspective, known as the over-smoothing problem.
Second, pre-processing overhead takes up most of the end-to-end processing time, especially for large-scale graphs.
To address these limitations, we present random walk with noise masking (RMask), a plug-and-play module compatible with the existing model-simplification works. 
This module enables the exploration of deeper GNNs while preserving their scalability.
Unlike the previous model-simplification works, we focus on continuous $\textbf{P}$ and found that the noise existing inside each $\textbf{P}$ is the cause of the over-smoothing issue, and use the efficient masking mechanism to eliminate them.
Experimental results on six real-world datasets demonstrate that model-simplification works equipped with RMask yield superior performance compared to their original version and can make a good trade-off between accuracy and efficiency.

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