153 research outputs found
CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution
Medical image arbitrary-scale super-resolution (MIASSR) has recently gained
widespread attention, aiming to super sample medical volumes at arbitrary
scales via a single model. However, existing MIASSR methods face two major
limitations: (i) reliance on high-resolution (HR) volumes and (ii) limited
generalization ability, which restricts their application in various scenarios.
To overcome these limitations, we propose Cube-based Neural Radiance Field
(CuNeRF), a zero-shot MIASSR framework that can yield medical images at
arbitrary scales and viewpoints in a continuous domain. Unlike existing MIASSR
methods that fit the mapping between low-resolution (LR) and HR volumes, CuNeRF
focuses on building a coordinate-intensity continuous representation from LR
volumes without the need for HR references. This is achieved by the proposed
differentiable modules: including cube-based sampling, isotropic volume
rendering, and cube-based hierarchical rendering. Through extensive experiments
on magnetic resource imaging (MRI) and computed tomography (CT) modalities, we
demonstrate that CuNeRF outperforms state-of-the-art MIASSR methods. CuNeRF
yields better visual verisimilitude and reduces aliasing artifacts at various
upsampling factors. Moreover, our CuNeRF does not need any LR-HR training
pairs, which is more flexible and easier to be used than others. Our code will
be publicly available soon
Hard Nominal Example-aware Template Mutual Matching for Industrial Anomaly Detection
Anomaly detectors are widely used in industrial production to detect and
localize unknown defects in query images. These detectors are trained on
nominal images and have shown success in distinguishing anomalies from most
normal samples. However, hard-nominal examples are scattered and far apart from
most normalities, they are often mistaken for anomalies by existing anomaly
detectors. To address this problem, we propose a simple yet efficient method:
\textbf{H}ard Nominal \textbf{E}xample-aware \textbf{T}emplate \textbf{M}utual
\textbf{M}atching (HETMM). Specifically, \textit{HETMM} aims to construct a
robust prototype-based decision boundary, which can precisely distinguish
between hard-nominal examples and anomalies, yielding fewer false-positive and
missed-detection rates. Moreover, \textit{HETMM} mutually explores the
anomalies in two directions between queries and the template set, and thus it
is capable to capture the logical anomalies. This is a significant advantage
over most anomaly detectors that frequently fail to detect logical anomalies.
Additionally, to meet the speed-accuracy demands, we further propose
\textbf{P}ixel-level \textbf{T}emplate \textbf{S}election (PTS) to streamline
the original template set. \textit{PTS} selects cluster centres and
hard-nominal examples to form a tiny set, maintaining the original decision
boundaries. Comprehensive experiments on five real-world datasets demonstrate
that our methods yield outperformance than existing advances under the
real-time inference speed. Furthermore, \textit{HETMM} can be hot-updated by
inserting novel samples, which may promptly address some incremental learning
issues
The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training
Although current deep learning techniques have yielded superior performance
on various computer vision tasks, yet they are still vulnerable to adversarial
examples. Adversarial training and its variants have been shown to be the most
effective approaches to defend against adversarial examples. These methods
usually regularize the difference between output probabilities for an
adversarial and its corresponding natural example. However, it may have a
negative impact if the model misclassifies a natural example. To circumvent
this issue, we propose a novel adversarial training scheme that encourages the
model to produce similar outputs for an adversarial example and its ``inverse
adversarial'' counterpart. These samples are generated to maximize the
likelihood in the neighborhood of natural examples. Extensive experiments on
various vision datasets and architectures demonstrate that our training method
achieves state-of-the-art robustness as well as natural accuracy. Furthermore,
using a universal version of inverse adversarial examples, we improve the
performance of single-step adversarial training techniques at a low
computational cost
Face hallucination under an image decomposition perspective
In this paper we propose to convert the task of face hallucination into an image decomposition problem, and thenuse the morphological component analysis (MCA) for hallucinating a single face image, based on a novel three-stepframework. Firstly, a low-resolution input image is up-sampled by interpolation. Then, the MCA is employed to decompose the interpolated image into a high-resolution image and an unsharp masking, as MCA can properly decompose a signal into special parts according to typical dictionaries. Finally, a residue compensation, which is based on the neighbour reconstruction of patches, is performed to enhance the facial details. The proposed method can effectively exploit the facial properties for face hallucination under the image decomposition perspective. Experimental results demonstrate the effectiveness of our method, in terms of the visual quality of the hallucinated face images
SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks
Spiking neural networks are efficient computation models for low-power
environments. Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions
are successful techniques for SNN training. Nevertheless, the spike-base BP
training is slow and requires large memory costs. Though ANN2NN provides a
low-cost way to train SNNs, it requires many inference steps to mimic the
well-trained ANN for good performance. In this paper, we propose a SNN-to-ANN
(SNN2ANN) framework to train the SNN in a fast and memory-efficient way. The
SNN2ANN consists of 2 components: a) a weight sharing architecture between ANN
and SNN and b) spiking mapping units. Firstly, the architecture trains the
weight-sharing parameters on the ANN branch, resulting in fast training and low
memory costs for SNN. Secondly, the spiking mapping units ensure that the
activation values of the ANN are the spiking features. As a result, the
classification error of the SNN can be optimized by training the ANN branch.
Besides, we design an adaptive threshold adjustment (ATA) algorithm to address
the noisy spike problem. Experiment results show that our SNN2ANN-based models
perform well on the benchmark datasets (CIFAR10, CIFAR100, and Tiny-ImageNet).
Moreover, the SNN2ANN can achieve comparable accuracy under 0.625x time steps,
0.377x training time, 0.27x GPU memory costs, and 0.33x spike activities of the
Spike-based BP model
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