Invertible Diffusion Models for Compressed Sensing
While deep neural networks (NNs) significantly advance image compressed sensing (CS) by improving reconstruction quality, the necessity of training current CS NNs from scratch …
Bin chen
Jian zhang
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1 min read
OSMamba: Omnidirectional Spectral Mamba with Dual-Domain Prior Generator for Exposure Correction
Exposure correction is a fundamental problem in computer vision and image processing. Recently, frequency domainbased methods have achieved impressive improvement, yet they still …
Gehui li
FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model
Recently, conditional diffusion models have gained popularity in numerous applications due to their exceptional generation ability. However, many existing methods are …
Jiwen yu
A Unified Continual Learning Framework with General Parameter-Efficient Tuning
The 'pre-training → downstream adaptation' presents both new opportunities and challenges for Continual Learning (CL). Although the recent state-of-the-art in CL is achieved …
Qiankun gao
Large-capacity and Flexible Video Steganography via Invertible Neural Network
Video steganography is the art of unobtrusively concealing secret data in a cover video and then recovering the secret data through a decoding protocol at the receiver end. …
Chong mou
R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning
Class-Incremental Learning(CIL) struggles with catastrophic forgetting when learning new knowledge, and Data-Free CIL (DFCIL) is even more challenging without access to the …
Qiankun gao
Optimization‑Inspired Compact Deep Compressive Sensing
Jian zhang
CREAM: CNN-REgularized ADMM framework for compressive-sensed image reconstruction
BoostNet: A Structured Deep Recursive Network to Boost Image Deblocking