23 citations · 143 across the 50 of their papers we have counts for
12 papers · 1 filter
Discriminator-Cooperated Feature Map Distillation for GAN Compression
Tie Hu, Mingbao Lin, Lizhou You +2
Despite excellent performance in image generation, Generative Adversarial Networks (GANs) are notorious for its requirements of enormous storage and intensive computation. As an aw…
Exploring Content Relationships for Distilling Efficient GANs
Lizhou You, Mingbao Lin, Tie Hu +2
This paper proposes a content relationship distillation (CRD) to tackle the over-parameterized generative adversarial networks (GANs) for the serviceability in cutting-edge devices…
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…
Meta Architecture for Point Cloud Analysis
Haojia Lin, Xiawu Zheng, Lijiang Li +5
Recent advances in 3D point cloud analysis bring a diverse set of network architectures to the field. However, the lack of a unified framework to interpret those networks makes any…
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…
LAB-Net: LAB Color-Space Oriented Lightweight Network for Shadow Removal
Hong Yang, Gongrui Nan, Mingbao Lin +4
This paper focuses on the limitations of current over-parameterized shadow removal models. We present a novel lightweight deep neural network that processes shadow images in the LA…
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…
Shadow-Aware Dynamic Convolution for Shadow Removal
Yimin Xu, Mingbao Lin, Hong Yang +2
With a wide range of shadows in many collected images, shadow removal has aroused increasing attention since uncontaminated images are of vital importance for many downstream multi…
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks
Yunshan Zhong, Mingbao Lin, Xunchao Li +5
Light-weight super-resolution (SR) models have received considerable attention for their serviceability in mobile devices. Many efforts employ network quantization to compress SR m…
CF-ViT: A General Coarse-to-Fine Method for Vision Transformer
Mengzhao Chen, Mingbao Lin, Ke Li +4
Vision Transformers (ViT) have made many breakthroughs in computer vision tasks. However, considerable redundancy arises in the spatial dimension of an input image, leading to mass…
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…
Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization
Tianshuo Xu, Lijiang Li, Peng Mi +5
PSNR-oriented models are a critical class of super-resolution models with applications across various fields. However, these models tend to generate over-smoothed images, a problem…