167 citations · 914 across the 103 of their papers we have counts for
13 papers · 1 filter
Efficient Stitchable Task Adaptation
Haoyu He, Zizheng Pan, Jing Liu +2
The paradigm of pre-training and fine-tuning has laid the foundation for deploying deep learning models. However, most fine-tuning methods are designed to meet a specific resource…
Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction
Zeshuai Deng, Zhuokun Chen, Shuaicheng Niu +3
Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images. Conventional SR methods typically gather th…
Mask Propagation for Efficient Video Semantic Segmentation
Yuetian Weng, Mingfei Han, Haoyu He +4
Video Semantic Segmentation (VSS) involves assigning a semantic label to each pixel in a video sequence. Prior work in this field has demonstrated promising results by extending im…
Object-aware Inversion and Reassembly for Image Editing
Zhen Yang, Ganggui Ding, Wen Wang +3
By comparing the original and target prompts, we can obtain numerous editing pairs, each comprising an object and its corresponding editing target. To allow editability while maint…
QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models
Jing Liu, Ruihao Gong, Xiuying Wei +3
Large Language Models (LLMs) excel in NLP, but their demands hinder their widespread deployment. While Quantization-Aware Training (QAT) offers a solution, its extensive training c…
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models
Yefei He, Jing Liu, Weijia Wu +2
Diffusion models have demonstrated remarkable capabilities in image synthesis and related generative tasks. Nevertheless, their practicality for real-world applications is constrai…
SwitchGPT: Adapting Large Language Models for Non-Text Outputs
Xinyu Wang, Bohan Zhuang, Qi Wu
Large Language Models (LLMs), primarily trained on text-based datasets, exhibit exceptional proficiencies in understanding and executing complex linguistic instructions via text ou…
Stitched ViTs are Flexible Vision Backbones
Zizheng Pan, Jing Liu, Haoyu He +2
Large pretrained plain vision Transformers (ViTs) have been the workhorse for many downstream tasks. However, existing works utilizing off-the-shelf ViTs are inefficient in terms o…
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning
Mingyang Zhang, Hao Chen, Chunhua Shen +4
Large Language Models (LLMs), such as LLaMA and T5, have shown exceptional performance across various tasks through fine-tuning. Although low-rank adaption (LoRA) has emerged to ch…
PTQD: Accurate Post-Training Quantization for Diffusion Models
Yefei He, Luping Liu, Jing Liu +3
Diffusion models have recently dominated image synthesis tasks. However, the iterative denoising process is expensive in computations at inference time, making diffusion models les…
Stitchable Neural Networks
Zizheng Pan, Jianfei Cai, Bohan Zhuang
The public model zoo containing enormous powerful pretrained model families (e.g., ResNet/DeiT) has reached an unprecedented scope than ever, which significantly contributes to the…
Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning
Haoyu He, Jianfei Cai, Jing Zhang +2
Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative for full fine-tuning so as to adapt pre-trained vision models to downstream tasks, which only tunes…
A Survey on Efficient Training of Transformers
Bohan Zhuang, Jing Liu, Zizheng Pan +3
Recent advances in Transformers have come with a huge requirement on computing resources, highlighting the importance of developing efficient training techniques to make Transforme…