1 citations · 2 across the 3 of their papers we have counts for
4 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…
Robust Embodied Perception in Dynamic Environments via Disentangled Weight Fusion
Juncen Guo, Xiaoguang Zhu, Jingyi Wu +4
Embodied perception systems face severe challenges of dynamic environment distribution drift when they continuously interact in open physical spaces. However, the existing domain i…
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…
CalFuse: Multi-Modal Continual Learning via Feature Calibration and Parameter Fusion
Juncen Guo, Siao Liu, Xiaoguang Zhu +8
With the proliferation of multi-modal data in large-scale visual recognition systems, enabling models to continuously acquire knowledge from evolving data streams while preserving…