209 citations · 292 across the 26 of their papers we have counts for
26 papers
UniPTS: A Unified Framework for Proficient Post-Training Sparsity
Jingjing Xie, Yuxin Zhang, Mingbao Lin +3
Post-training Sparsity (PTS) is a recently emerged avenue that chases efficient network sparsity with limited data in need. Existing PTS methods, however, undergo significant perfo…
Learning Image Demoireing from Unpaired Real Data
Yunshan Zhong, Yuyao Zhou, Yuxin Zhang +2
This paper focuses on addressing the issue of image demoireing. Unlike the large volume of existing studies that rely on learning from paired real data, we attempt to learn a demoi…
Boosting the Cross-Architecture Generalization of Dataset Distillation through an Empirical Study
Lirui Zhao, Yuxin Zhang, Fei Chao +1
The poor cross-architecture generalization of dataset distillation greatly weakens its practical significance. This paper attempts to mitigate this issue through an empirical study…
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs
Yuxin Zhang, Lirui Zhao, Mingbao Lin +6
The ever-increasing large language models (LLMs), though opening a potential path for the upcoming artificial general intelligence, sadly drops a daunting obstacle on the way towar…
Ultrasound Image Reconstruction with Denoising Diffusion Restoration Models
Yuxin Zhang, Clément Huneau, Jérôme Idier +1
Ultrasound image reconstruction can be approximately cast as a linear inverse problem that has traditionally been solved with penalized optimization using the or norm,…
Spatial Re-parameterization for N:M Sparsity
Yuxin Zhang, Mingbao Lin, Mingliang Xu +2
This paper presents a Spatial Re-parameterization (SpRe) method for the N:M sparsity. SpRe stems from an observation regarding the restricted variety in spatial sparsity of convolu…
Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution Networks
Yunshan Zhong, Mingbao Lin, Jingjing Xie +3
This paper introduces Distribution-Flexible Subset Quantization (DFSQ), a post-training quantization method for super-resolution networks. Our motivation for developing DFSQ is bas…
Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer
Nisha Huang, Yuxin Zhang, Weiming Dong
Large-scale text-to-video diffusion models have demonstrated an exceptional ability to synthesize diverse videos. However, due to the lack of extensive text-to-video datasets and t…
ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
Yuxin Zhang, Weiming Dong, Fan Tang +6
Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textu…
MBQuant: A Novel Multi-Branch Topology Method for Arbitrary Bit-width Network Quantization
Yunshan Zhong, Yuyao Zhou, Fei Chao +1
Arbitrary bit-width network quantization has received significant attention due to its high adaptability to various bit-width requirements during runtime. However, in this paper, w…
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning
Yuxin Zhang, Fan Tang, Weiming Dong +4
We present Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, which can fit in most existing arbitrary image style…
Bi-directional Masks for Efficient N:M Sparse Training
Yuxin Zhang, Yiting Luo, Mingbao Lin +4
We focus on addressing the dense backward propagation issue for training efficiency of N:M fine-grained sparsity that preserves at most N out of M consecutive weights and achieves…
Real-Time Image Demoireing on Mobile Devices
Yuxin Zhang, Mingbao Lin, Xunchao Li +7
Moire patterns appear frequently when taking photos of digital screens, drastically degrading the image quality. Despite the advance of CNNs in image demoireing, existing networks…
SMMix: Self-Motivated Image Mixing for Vision Transformers
Mengzhao Chen, Mingbao Lin, ZhiHang Lin +3
CutMix is a vital augmentation strategy that determines the performance and generalization ability of vision transformers (ViTs). However, the inconsistency between the mixed image…
Shadow Removal by High-Quality Shadow Synthesis
Yunshan Zhong, Lizhou You, Yuxin Zhang +3
Most shadow removal methods rely on the invasion of training images associated with laborious and lavish shadow region annotations, leading to the increasing popularity of shadow i…
Inversion-Based Style Transfer with Diffusion Models
Yuxin Zhang, Nisha Huang, Fan Tang +4
The artistic style within a painting is the means of expression, which includes not only the painting material, colors, and brushstrokes, but also the high-level attributes includi…
Exploiting the Partly Scratch-off Lottery Ticket for Quantization-Aware Training
Yunshan Zhong, Gongrui Nan, Yuxin Zhang +2
Quantization-aware training (QAT) receives extensive popularity as it well retains the performance of quantized networks. In QAT, the contemporary experience is that all quantized…
Learning Best Combination for Efficient N:M Sparsity
Yuxin Zhang, Mingbao Lin, Zhihang Lin +5
By forcing at most N out of M consecutive weights to be non-zero, the recent N:M network sparsity has received increasing attention for its two attractive advantages: 1) Promising…
Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning
Yuxin Zhang, Fan Tang, Weiming Dong +4
In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a…
A Survey of Deep Learning Models for Structural Code Understanding
Ruoting Wu, Yuxin Zhang, Qibiao Peng +2
In recent years, the rise of deep learning and automation requirements in the software industry has elevated Intelligent Software Engineering to new heights. The number of approach…
Super Vision Transformer
Mingbao Lin, Mengzhao Chen, Yuxin Zhang +3
We attempt to reduce the computational costs in vision transformers (ViTs), which increase quadratically in the token number. We present a novel training paradigm that trains only…
Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters
Mingbao Lin, Liujuan Cao, Yuxin Zhang +3
This paper focuses on filter-level network pruning. A novel pruning method, termed CLR-RNF, is proposed. We first reveal a "long-tail" long-tail pruning problem in magnitude-based…
OptG: Optimizing Gradient-driven Criteria in Network Sparsity
Yuxin Zhang, Mingbao Lin, Mengzhao Chen +2
Network sparsity receives popularity mostly due to its capability to reduce the network complexity. Extensive studies excavate gradient-driven sparsity. Typically, these methods ar…
Carrying out CNN Channel Pruning in a White Box
Yuxin Zhang, Mingbao Lin, Chia-Wen Lin +5
Channel Pruning has been long studied to compress CNNs, which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which…