8 papers
ActiveScope: Actively Seeking and Correcting Perception for MLLMs
Yajing Wang, Chao Bi, Junshu Sun +4
Multimodal Large Language Models (MLLMs) have demonstrated impressive vision-language understanding, yet still struggle with fine-grained perception in high-resolution images. Whil…
Adaptive Recurrent Message Passing for Test Time Computing on Graphs
Junshu Sun, Wanxing Chang, Qingming Huang +1
Pre-trained foundation models have demonstrated remarkable success in many domains, enabling a unified backbone to generalize across diverse downstream tasks. However, extending th…
Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
Junshu Sun, Wanxing Chang, Qingming Huang +1
Graph neural networks (GNNs) tightly couple their input-output parameters to dataset-specific feature spaces and target sets, exhibiting limited transferability across different da…
Enhancing Pre-trained Representation Classifiability can Boost its Interpretability
Shufan Shen, Zhaobo Qi, Junshu Sun +3
The visual representation of a pre-trained model prioritizes the classifiability on downstream tasks, while the widespread applications for pre-trained visual models have posed new…
Kernelized Sparse Fine-Tuning with Bi-level Parameter Competition for Vision Models
Shufan Shen, Junshu Sun, Shuhui Wang +1
Parameter-efficient fine-tuning (PEFT) aims to adapt pre-trained vision models to downstream tasks. Among PEFT paradigms, sparse tuning achieves remarkable performance by adjusting…
Edit Less, Achieve More: Dynamic Sparse Neuron Masking for Lifelong Knowledge Editing in LLMs
Jinzhe Liu, Junshu Sun, Shufan Shen +2
Lifelong knowledge editing enables continuous, precise updates to outdated knowledge in large language models (LLMs) without computationally expensive full retraining. However, exi…