1 citations · 1 across the 9 of their papers we have counts for
9 papers
Fine-Tuning of Transformer models with Frames
Harshavardhan Adepu, Li Zhang, Sanjiv Kumar +1
Parameter-Efficient Fine-Tuning (PEFT) strategies such as Low-Rank Adaptation (LoRA) are effective solutions for fine-tuning large-scale pre-trained models; however, their memory r…
DNA: Uncovering Universal Latent Forgery Knowledge
Jingtong Dou, Chuancheng Shi, Yemin Wang +6
As generative AI achieves hyper-realism, superficial artifact detection has become obsolete. While prevailing methods rely on resource-intensive fine-tuning of black-box backbones,…
Transferable Adversarial Attacks on Black-Box Vision-Language Models
Kai Hu, Weichen Yu, Li Zhang +5
Vision Large Language Models (VLLMs) are increasingly deployed to offer advanced capabilities on inputs comprising both text and images. While prior research has shown that adversa…
Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement
Haofan Wu, Yin Huang, Yuqing Wu +12
High-quality fundus images provide essential anatomical information for clinical screening and ophthalmic disease diagnosis. Yet, due to hardware limitations, operational variabili…
DiscQuant: A Quantization Method for Neural Networks Inspired by Discrepancy Theory
Jerry Chee, Arturs Backurs, Rainie Heck +4
Quantizing the weights of a neural network has two steps: (1) Finding a good low bit-complexity representation for weights (which we call the quantization grid) and (2) Rounding th…
Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition
Zheda Mai, Ping Zhang, Cheng-Hao Tu +3
Parameter-efficient fine-tuning (PEFT) has attracted significant attention due to the growth of pre-trained model sizes and the need to fine-tune (FT) them for superior downstream…
Robust Training of Neural Networks at Arbitrary Precision and Sparsity
Chengxi Ye, Grace Chu, Yanfeng Liu +5
The discontinuous operations inherent in quantization and sparsification introduce a long-standing obstacle to backpropagation, particularly in ultra-low precision and sparse regim…
FrameQuant: Flexible Low-Bit Quantization for Transformers
Harshavardhan Adepu, Zhanpeng Zeng, Li Zhang +1
Transformers are the backbone of powerful foundation models for many Vision and Natural Language Processing tasks. But their compute and memory/storage footprint is large, and so,…
UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer
Ji Liu, Dehua Tang, Yuanxian Huang +9
Traditional channel-wise pruning methods by reducing network channels struggle to effectively prune efficient CNN models with depth-wise convolutional layers and certain efficient…