Yiwen Guo

Yuanze Li, Haolin Wang, Shihao Yuan et al.

Due to the training configuration, traditional industrial anomaly detection (IAD) methods have to train a specific model for each deployment scenario, which is insufficient to meet the requirements of modern design and manufacturing. On the contrary, large multimodal models~(LMMs) have shown eminent generalization ability on various vision tasks, and their perception and comprehension capabilities imply the potential of applying LMMs on IAD tasks. However, we observe that even though the LMMs have abundant knowledge about industrial anomaly detection in the textual domain, the LMMs are unable to leverage the knowledge due to the modality gap between textual and visual domains. To stimulate the relevant knowledge in LMMs and adapt the LMMs towards anomaly detection tasks, we introduce existing IAD methods as vision experts and present a novel large multimodal model applying vision experts for industrial anomaly detection~(abbreviated to {Myriad}). Specifically, we utilize the anomaly map generated by the vision experts as guidance for LMMs, such that the vision model is guided to pay more attention to anomalous regions. Then, the visual features are modulated via an adapter to fit the anomaly detection tasks, which are fed into the language model together with the vision expert guidance and human instructions to generate the final outputs. Extensive experiments are applied on MVTec-AD, VisA, and PCB Bank benchmarks demonstrate that our proposed method not only performs favorably against state-of-the-art methods, but also inherits the flexibility and instruction-following ability of LMMs in the field of IAD. Source code and pre-trained models are publicly available at \url{https://github.com/tzjtatata/Myriad}.

Wenyu Li, Xiaoqi Jiao, Yi Chang et al.

The creation of high-quality multimodal datasets remains fundamental for advancing role-playing capabilities in large language models (LLMs). While existing works predominantly focus on text-based persona simulation, Audio Role-Playing (ARP) presents unique challenges due to the need for synchronized alignment of semantic content and vocal characteristics. To address this gap, we propose AudioRole, a meticulously curated dataset from 13 TV series spanning 1K+ hours with 1M+ character-grounded dialogues, providing synchronized audio-text pairs annotated with speaker identities and contextual metadata. In addition, to demonstrate the effectiveness of the dataset, we introduced ARP-Eval, a dual-aspect evaluation framework that assesses both response quality and role fidelity. Empirical validation showing GLM-4-Voice trained on AudioRole (which we called ARP-Model) achieve an average Acoustic Personalization score of 0.31, significantly outperforming the original GLM-4-voice and the more powerful model MiniCPM-O-2.6, which specifically supports role-playing in one-shot scenarios. The ARP-Model also achieves a Content Personalization score of 0.36, surpassing the untrained original model by about 38% and maintaining the same level as MiniCPM-O-2.6. AudioRole features dialogues from over 115 main characters, 6 trained ARP-Models that role-play different characters, and evaluation protocols. Together, they provide an essential resource for advancing audio-grounded role-playing research.

Haoyu Wang, Guangyan Zhang, Jiale Chen et al.

With the development of speech large language models (speech LLMs), users can now interact directly with assistants via speech. However, most existing models only convert response content into speech without fully capturing the rich emotional cues in user queries, where the same sentence may convey different meanings depending on the expression. Emotional understanding is thus essential for improving human-machine interaction. Most empathetic speech LLMs rely on massive datasets, demanding high computational cost. A key challenge is to build models that generate empathetic responses with limited data and without large-scale training. To this end, we propose Emotion Omni, a model that understands emotional content in user speech and generates empathetic responses. We further developed a data pipeline to construct a 200k emotional dialogue dataset supporting empathetic speech assistants. Experiments show that Emotion Omni achieves comparable instruction-following ability without large-scale pretraining, while surpassing existing models in speech quality (UTMOS:4.41) and empathy (Emotion GPT Score: 3.97). These results confirm its improvements in both speech fidelity and emotional expressiveness. Demos are available at https://w311411.github.io/omni_demo/.