2,817 research outputs found
xUnit: Learning a Spatial Activation Function for Efficient Image Restoration
In recent years, deep neural networks (DNNs) achieved unprecedented
performance in many low-level vision tasks. However, state-of-the-art results
are typically achieved by very deep networks, which can reach tens of layers
with tens of millions of parameters. To make DNNs implementable on platforms
with limited resources, it is necessary to weaken the tradeoff between
performance and efficiency. In this paper, we propose a new activation unit,
which is particularly suitable for image restoration problems. In contrast to
the widespread per-pixel activation units, like ReLUs and sigmoids, our unit
implements a learnable nonlinear function with spatial connections. This
enables the net to capture much more complex features, thus requiring a
significantly smaller number of layers in order to reach the same performance.
We illustrate the effectiveness of our units through experiments with
state-of-the-art nets for denoising, de-raining, and super resolution, which
are already considered to be very small. With our approach, we are able to
further reduce these models by nearly 50% without incurring any degradation in
performance.Comment: Conference on Computer Vision and Pattern Recognition (CVPR), 201
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI
Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging
techniques available for clinical applications. However, the rather slow speed
of MRI acquisitions limits the patient throughput and potential indi cations.
Compressive Sensing (CS) has proven to be an efficient technique for
accelerating MRI acquisition. The most widely used CS-MRI model, founded on the
premise of reconstructing an image from an incompletely filled k-space, leads
to an ill-posed inverse problem. In the past years, lots of efforts have been
made to efficiently optimize the CS-MRI model. Inspired by deep learning
techniques, some preliminary works have tried to incorporate deep architectures
into CS-MRI process. Unfortunately, the convergence issues (due to the
experience-based networks) and the robustness (i.e., lack real-world noise
modeling) of these deeply trained optimization methods are still missing. In
this work, we develop a new paradigm to integrate designed numerical solvers
and the data-driven architectures for CS-MRI. By introducing an optimal
condition checking mechanism, we can successfully prove the convergence of our
established deep CS-MRI optimization scheme. Furthermore, we explicitly
formulate the Rician noise distributions within our framework and obtain an
extended CS-MRI network to handle the real-world nosies in the MRI process.
Extensive experimental results verify that the proposed paradigm outperforms
the existing state-of-the-art techniques both in reconstruction accuracy and
efficiency as well as robustness to noises in real scene
A Survey on Bayesian Deep Learning
A comprehensive artificial intelligence system needs to not only perceive the
environment with different `senses' (e.g., seeing and hearing) but also infer
the world's conditional (or even causal) relations and corresponding
uncertainty. The past decade has seen major advances in many perception tasks
such as visual object recognition and speech recognition using deep learning
models. For higher-level inference, however, probabilistic graphical models
with their Bayesian nature are still more powerful and flexible. In recent
years, Bayesian deep learning has emerged as a unified probabilistic framework
to tightly integrate deep learning and Bayesian models. In this general
framework, the perception of text or images using deep learning can boost the
performance of higher-level inference and in turn, the feedback from the
inference process is able to enhance the perception of text or images. This
survey provides a comprehensive introduction to Bayesian deep learning and
reviews its recent applications on recommender systems, topic models, control,
etc. Besides, we also discuss the relationship and differences between Bayesian
deep learning and other related topics such as Bayesian treatment of neural
networks.Comment: To appear in ACM Computing Surveys (CSUR) 202
Hybrid Spectral Denoising Transformer with Guided Attention
In this paper, we present a Hybrid Spectral Denoising Transformer (HSDT) for
hyperspectral image denoising. Challenges in adapting transformer for HSI arise
from the capabilities to tackle existing limitations of CNN-based methods in
capturing the global and local spatial-spectral correlations while maintaining
efficiency and flexibility. To address these issues, we introduce a hybrid
approach that combines the advantages of both models with a Spatial-Spectral
Separable Convolution (S3Conv), Guided Spectral Self-Attention (GSSA), and
Self-Modulated Feed-Forward Network (SM-FFN). Our S3Conv works as a lightweight
alternative to 3D convolution, which extracts more spatial-spectral correlated
features while keeping the flexibility to tackle HSIs with an arbitrary number
of bands. These features are then adaptively processed by GSSA which per-forms
3D self-attention across the spectral bands, guided by a set of learnable
queries that encode the spectral signatures. This not only enriches our model
with powerful capabilities for identifying global spectral correlations but
also maintains linear complexity. Moreover, our SM-FFN proposes the
self-modulation that intensifies the activations of more informative regions,
which further strengthens the aggregated features. Extensive experiments are
conducted on various datasets under both simulated and real-world noise, and it
shows that our HSDT significantly outperforms the existing state-of-the-art
methods while maintaining low computational overhead. Code is at https:
//github.com/Zeqiang-Lai/HSDT.Comment: ICCV 202
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