12 research outputs found
Learning the Degradation Distribution for Blind Image Super-Resolution
Synthetic high-resolution (HR) \& low-resolution (LR) pairs are widely used
in existing super-resolution (SR) methods. To avoid the domain gap between
synthetic and test images, most previous methods try to adaptively learn the
synthesizing (degrading) process via a deterministic model. However, some
degradations in real scenarios are stochastic and cannot be determined by the
content of the image. These deterministic models may fail to model the random
factors and content-independent parts of degradations, which will limit the
performance of the following SR models. In this paper, we propose a
probabilistic degradation model (PDM), which studies the degradation
as a random variable, and learns its distribution by modeling the
mapping from a priori random variable to . Compared
with previous deterministic degradation models, PDM could model more diverse
degradations and generate HR-LR pairs that may better cover the various
degradations of test images, and thus prevent the SR model from over-fitting to
specific ones. Extensive experiments have demonstrated that our degradation
model can help the SR model achieve better performance on different datasets.
The source codes are released at \url{[email protected]:greatlog/UnpairedSR.git}.Comment: Accepted to CVRP202
End-to-end Alternating Optimization for Real-World Blind Super Resolution
Blind Super-Resolution (SR) usually involves two sub-problems: 1) estimating
the degradation of the given low-resolution (LR) image; 2) super-resolving the
LR image to its high-resolution (HR) counterpart. Both problems are ill-posed
due to the information loss in the degrading process. Most previous methods try
to solve the two problems independently, but often fall into a dilemma: a good
super-resolved HR result requires an accurate degradation estimation, which
however, is difficult to be obtained without the help of original HR
information. To address this issue, instead of considering these two problems
independently, we adopt an alternating optimization algorithm, which can
estimate the degradation and restore the SR image in a single model.
Specifically, we design two convolutional neural modules, namely
\textit{Restorer} and \textit{Estimator}. \textit{Restorer} restores the SR
image based on the estimated degradation, and \textit{Estimator} estimates the
degradation with the help of the restored SR image. We alternate these two
modules repeatedly and unfold this process to form an end-to-end trainable
network. In this way, both \textit{Restorer} and \textit{Estimator} could get
benefited from the intermediate results of each other, and make each
sub-problem easier. Moreover, \textit{Restorer} and \textit{Estimator} are
optimized in an end-to-end manner, thus they could get more tolerant of the
estimation deviations of each other and cooperate better to achieve more robust
and accurate final results. Extensive experiments on both synthetic datasets
and real-world images show that the proposed method can largely outperform
state-of-the-art methods and produce more visually favorable results. The codes
are rleased at \url{https://github.com/greatlog/RealDAN.git}.Comment: Extension of our previous NeurIPS paper. Accepted to IJC
VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
A diffusion probabilistic model (DPM), which constructs a forward diffusion
process by gradually adding noise to data points and learns the reverse
denoising process to generate new samples, has been shown to handle complex
data distribution. Despite its recent success in image synthesis, applying DPMs
to video generation is still challenging due to high-dimensional data spaces.
Previous methods usually adopt a standard diffusion process, where frames in
the same video clip are destroyed with independent noises, ignoring the content
redundancy and temporal correlation. This work presents a decomposed diffusion
process via resolving the per-frame noise into a base noise that is shared
among all frames and a residual noise that varies along the time axis. The
denoising pipeline employs two jointly-learned networks to match the noise
decomposition accordingly. Experiments on various datasets confirm that our
approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based
alternatives in high-quality video generation. We further show that our
decomposed formulation can benefit from pre-trained image diffusion models and
well-support text-conditioned video creation.Comment: Accepted to CVPR202
Fast Meta Failure Recovery for Federated Meta-Learning
In recent years, the field of distributed deep learning within the Internet of Things (IoT) or the edge has experienced exponential growth. Federated meta-learning has emerged as a significant advancement, enabling collaborative learning among source nodes to establish a global model initialization. This approach allows for optimal performance while necessitating minimal data samples for updating model parameters at the target node. Federated meta-learning has gained increased attention due to its capacity to provide real-time edge intelligence. However, a critical aspect that remains inadequately explored is the recovery of interim meta knowledge\u27s failure, which constitutes a pivotal key for adapting to new tasks. In this paper, we introduce FMRec, a novel platform designed to offer a fast and flexible recovery mechanism for failed interim meta knowledge in various federated meta-learning scenarios. FMRec serves as a complementary system compatible with different types of federated models and is adaptable to diverse tasks. We present a demonstration of its design and assess its efficiency and reliability through real-world applications
Strain Rate Effect on the Thermomechanical Behavior of NiTi Shape Memory Alloys: A Literature Review
A review of experiments and models for the strain rate effect of NiTi Shape Memory Alloys (SMAs) is presented in this paper. Experimental observations on the rate-dependent properties, such as stress responses, temperature evolutions, and phase nucleation and propagation, under uniaxial loads are classified and summarized based on the strain rate values. The strain rates are divided into five ranges and in each range the deformation mechanism is unique. For comparison, results under other loading modes are also reviewed; however, these are shorter in length due to a limited number of experiments. A brief discussion on the influences of the microstructure on the strain-rate responses is followed. Modeling the rate-dependent behaviors of NiTi SMAs focuses on incorporating the physical origins in the constitutive relationship. Thermal source models are the key rate-dependent constitutive models under quasi-static loading to account for the self-heating mechanism. Thermal kinetic models, evolving from thermal source models, address the kinetic relationship in dynamic deformation