4 citations · 4 across the 12 of their papers we have counts for
8 papers · 1 filter
Immunizing 3D Gaussian Generative Models Against Unauthorized Fine-Tuning via Attribute-Space Traps
Jianwei Zhang, Sihan Cao, Chaoning Zhang +7
Recent large-scale generative models enable high-quality 3D synthesis. However, the public accessibility of pre-trained weights introduces a critical vulnerability. Adversaries can…
RCP: Representation Consistency Pruner for Mitigating Distribution Shift in Large Vision-Language Models
Jianwei Zhang, Chaoning Zhang, Sihan Cao +7
Large Vision-Language Models (LVLMs) suffer from prohibitive inference costs due to the massive number of visual tokens processed by the language decoder. Existing pruning methods…
Language-Guided Token Compression with Reinforcement Learning in Large Vision-Language Models
Sihan Cao, Jianwei Zhang, Pengcheng Zheng +7
Large Vision-Language Models (LVLMs) incur substantial inference costs due to the processing of a vast number of visual tokens. Existing methods typically struggle to model progres…
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy
Yiting Yang, Hao Luo, Yuan Sun +7
A prevalent approach in Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViT) involves freezing the majority of the backbone parameters and solely learnin…
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation
Wei Dong, Yuan Sun, Yiting Yang +7
A common strategy for Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViTs) involves adapting the model to downstream tasks by learning a low-rank adapta…
TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings
Dawei Yan, Pengcheng Li, Yang Li +7
Currently, inspired by the success of vision-language models (VLMs), an increasing number of researchers are focusing on improving VLMs and have achieved promising results. However…
Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach
Wei Dong, Xing Zhang, Bihui Chen +5
Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while…
Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing
Wei Dong, Dawei Yan, Zhijun Lin +1
The advent of high-capacity pre-trained models has revolutionized problem-solving in computer vision, shifting the focus from training task-specific models to adapting pre-trained…