3 citations · 4 across the 4 of their papers we have counts for
5 papers
Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection
Liangyu Teng, Hengsong Liu, Juncen Guo +4
The performance ceiling of an LLM team is constrained not only by individual model capabilities, but also by inter-member error resonance and predictive differences. Although heter…
Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
Jing Liu, Chenxuanyin Zou, Jiayang Ren +5
Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obsta…
Collaborative Adaptive Curriculum for Progressive Knowledge Distillation
Jing Liu, Zhenchao Ma, Han Yu +5
Recent advances in collaborative knowledge distillation have demonstrated cutting-edge performance for resource-constrained distributed multimedia learning scenarios. However, achi…
Adaptive Weighted Parameter Fusion with CLIP for Class-Incremental Learning
Juncen Guo, Xiaoguang Zhu, Liangyu Teng +4
Class-incremental Learning (CIL) enables the model to incrementally absorb knowledge from new classes and build a generic classifier across all previously encountered classes. When…
A Survey on Diffusion Models for Anomaly Detection
Jing Liu, Zhenchao Ma, Zepu Wang +7
Diffusion models (DMs) have emerged as a powerful class of generative AI models, showing remarkable potential in anomaly detection (AD) tasks across various domains, such as cybers…