4 papers
A Lightweight Plastic-Memory Framework for Graph Few-Shot Class-Incremental Learning
Zihan Mei, Zhili Qin, Tongze Zhang +3
Graph Incremental Learning has garnered increasing attention as dynamic graph data continues to emerge across diverse fields. Conventional approaches primarily address catastrophic…
SPEAR: A System for Post-Quantization Error-Adaptive Recovery Enabling Efficient Low-Bit LLM Serving
Hongyuan Liu, Yawei Li, Zhiqiang Que +3
Efficient large language model (LLM) serving is increasingly constrained by deployment cost. Quantization is a key technique for reducing serving cost, yet even state-of-the-art 4-…
The Geometry of Compromise: Unlocking Generative Capabilities via Controllable Modality Alignment
Hongyuan Liu, Qinli Yang, Wen Li +6
Vision-Language Models (VLMs) such as CLIP learn a shared embedding space for images and text, yet their representations remain geometrically separated, a phenomenon known as the m…
Exploiting Fine-Grained Prototype Distribution for Boosting Unsupervised Class Incremental Learning
Jiaming Liu, Hongyuan Liu, Zhili Qin +4
The dynamic nature of open-world scenarios has attracted more attention to class incremental learning (CIL). However, existing CIL methods typically presume the availability of com…