Chenyou Fan

The prevalence of social media platforms has accelerated the propagation of fake news, with carefully crafted multimodal fake news often gaining public trust and causing severe impacts. Methods based on multimodal small language models (MSLMs) struggle in scenarios with scarce labeled data. Large vision-language model (LVLM)-based approaches, though alleviating this limitation, often face two major problems: They rely too much on text-image consistency when predicting the authenticity of news. They judge which samples need to introduce external knowledge based on LVLMs’ direct outputs, which is not accurate because of LVLMs’ overconfidence. This article proposes a novel LVLM-based multiview multimodal fake news detection (MV-MFND) framework to address these problems. MV-MFND leverages LVLMs to analyze text, image, and text-image pairs separately, thus predicting authenticity from multiple perspectives. MV-MFND determines which samples need to introduce external knowledge by analyzing the softmax probability of specific tokens output by the LVLM. Our framework MV-MFND achieves SOTA performance on three real-world datasets Fakeddit, Twitter, and MR2-en on Qwen.

Out-of-distribution (OOD) intent detection aims to identify user queries that exceed predefined intent categories and is a crucial technology for ensuring interaction reliability and user experience in practical applications such as dialogue systems and intelligent customer service. Although large language models (LLMs) perform excellently in natural language processing tasks, they encounter difficulties with zero-shot OOD intent detection. This article reveals the dual biases of LLMs in zero-shot OOD intent detection: 1) LLMs tend to prefer the intents that are presented earlier in the intent list; and 2) LLMs frequently misclassify unknown intent as in-distribution (ID) intent. These biases severely limit the deployment of LLMs in intent-recognition systems that require high reliability. To address this problem, we propose a novel semantics-based sorting-filtering and querying-confirming (SF-QC) method: the semantic sorting-filtering (SF) module dynamically adjusts the intent list to mitigate position preference, and then the querying-confirming (QC) module deeply validates the initial ID response to reduce OOD misclassification, starting from the root cause of biases to guide the LLM to make unbiased judgments. Experiments on two mainstream intent datasets show that our proposed SF-QC approach has up to 7.82% (Macro-F1) and 9.76% (ACC) improvement in overall performance, and up to 14.9% (Macro-F1) and 20.66% (ACC) improvement in OOD performance over the chain-of-thought (CoT) method. The excellent detection performance of SF-QC provides key technical support for the robust deployment of LLMs in practical applications such as dialogue systems, helping to reduce system risks and enhance user experience.

Machine learning (ML) model trading, known for its role in protecting data privacy, faces a major challenge: information asymmetry. This issue can lead to model deception, a problem that current literature has not fully solved, where the seller misrepresents model performance to earn more. We propose a game-theoretic approach, adding a verification step in the ML model market that lets buyers check model quality before buying. However, this method can be expensive and offers imperfect information, making it harder for buyers to decide. Our analysis reveals that a seller might probabilistically conduct model deception considering the chance of model verification. This deception probability decreases with the verification accuracy and increases with the verification cost. To maximize seller payoff, we further design optimal pricing schemes accounting for heterogeneous buyers' strategic behaviors. Interestingly, we find that reducing information asymmetry benefits both the seller and buyer. Meanwhile, protecting buyer order information doesn't improve the payoff for the buyer or the seller. These findings highlight the importance of reducing information asymmetry in ML model trading and open new directions for future research.

Classical Federated Learning (FL) encounters significant challenges when deploying large models on power-constrained clients. To tackle this, we propose an asymmetric FL mechanism that enables the aggregation of compact client models into a comprehensive server model. We design the server model as a Mixture-of-Experts (MoE), where each expert has the same architecture as each client model. This uniformity allows for efficient fusion of the most pertinent client models to update each server expert, based on the measured relevance between each client and server expert. To address the Non-IID data issue, we further optimize the server-side MoE architecture by incorporating a main expert that always activates alongside a set of selectively activated routed experts. This configuration ensures a balance between learning general knowledge and specific data distribution. Our Fed-MoE framework is model-agnostic and has demonstrated notable improvements on vision FL tasks with million-scale ResNet backbones, and language tasks with billion-scale BERT and GPT-2 backbones.

Recently, learning-based stereo matching networks have advanced significantly. However, they often lack robustness and struggle to achieve impressive cross-domain performance due to domain shifts and imbalanced disparity distributions among diverse datasets. Leveraging Vision Foundation Models (VFMs) can intuitively enhance the model's robustness, but integrating such a model into stereo matching cost-effectively to fully realize their robustness remains a key challenge. To address this, we propose SMoEStereo, a novel framework that adapts VFMs for stereo matching through a tailored, scene-specific fusion of Low-Rank Adaptation (LoRA) and Mixture-of-Experts (MoE) modules. SMoEStereo introduces MoE-LoRA with adaptive ranks and MoE-Adapter with adaptive kernel sizes. The former dynamically selects optimal experts within MoE to adapt varying scenes across domains, while the latter injects inductive bias into frozen VFMs to improve geometric feature extraction. Importantly, to mitigate computational overhead, we further propose a lightweight decision network that selectively activates MoE modules based on input complexity, balancing efficiency with accuracy. Extensive experiments demonstrate that our method exhibits state-of-the-art cross-domain and joint generalization across multiple benchmarks without dataset-specific adaptation. The code is available at \textcolor{red}{https://github.com/cocowy1/SMoE-Stereo}.

Machine learning (ML) model trading, known for its role in protecting data privacy, faces a major challenge: information asymmetry. This issue can lead to model deception, a problem that current literature has not fully solved, where the seller misrepresents model performance to earn more. We propose a game-theoretic approach, adding a verification step in the ML model market that lets buyers check model quality before buying. However, this method can be expensive and offers imperfect information, making it harder for buyers to decide. Our analysis reveals that a seller might probabilistically conduct model deception considering the chance of model verification. This deception probability decreases with the verification accuracy and increases with the verification cost. To maximize seller payoff, we further design optimal pricing schemes accounting for heterogeneous buyers’ strategic behaviors. Interestingly, we find that reducing information asymmetry benefits both the seller and buyer. Meanwhile, protecting buyer order information doesn’t improve the payoff for the buyer or the seller. These findings highlight the importance of reducing information asymmetry in ML model trading and open new directions for future research.