activity
20212024
most citedELMformer: Efficient Raw Image Restoration with a Locally Multiplicative Transformer

10 citations · 21 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20242 cited

A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification

Quanwei Liu, Yanni Dong, Tao Huang +2

Hyperspectral image (HSI) classification techniques have been intensively studied and a variety of models have been developed. However, these HSI classification models are confined…

cs.LG2023

Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants

Yibo Yang, Haobo Yuan, Xiangtai Li +6

How to enable learnability for new classes while keeping the capability well on old classes has been a crucial challenge for class incremental learning. Beyond the normal case, lon…

cs.CV20233 cited

DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration

Yuchun Miao, Lefei Zhang, Liangpei Zhang +1

Diffusion models have recently received a surge of interest due to their impressive performance for image restoration, especially in terms of noise robustness. However, existing di…

cs.CV20236 cited

DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense Prediction

Yangyang Xu, Yibo Yang, Lefei Zhang

Convolution neural networks (CNNs) and Transformers have their own advantages and both have been widely used for dense prediction in multi-task learning (MTL). Most of the current…

cs.CV202210 cited

ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative Transformer

Jiaqi Ma, Shengyuan Yan, Lefei Zhang +2

In order to get raw images of high quality for downstream Image Signal Process (ISP), in this paper we present an Efficient Locally Multiplicative Transformer called ELMformer for…

cs.CV2021

Siamese Network with Interactive Transformer for Video Object Segmentation

Meng Lan, Jing Zhang, Fengxiang He +1

Semi-supervised video object segmentation (VOS) refers to segmenting the target object in remaining frames given its annotation in the first frame, which has been actively studied…