260 research outputs found
Wavelet Integrated CNNs for Noise-Robust Image Classification
Convolutional Neural Networks (CNNs) are generally prone to noise
interruptions, i.e., small image noise can cause drastic changes in the output.
To suppress the noise effect to the final predication, we enhance CNNs by
replacing max-pooling, strided-convolution, and average-pooling with Discrete
Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers
applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and
design wavelet integrated CNNs (WaveCNets) using these layers for image
classification. In WaveCNets, feature maps are decomposed into the
low-frequency and high-frequency components during the down-sampling. The
low-frequency component stores main information including the basic object
structures, which is transmitted into the subsequent layers to extract robust
high-level features. The high-frequency components, containing most of the data
noise, are dropped during inference to improve the noise-robustness of the
WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy
version of ImageNet) show that WaveCNets, the wavelet integrated versions of
VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness
than their vanilla versions.Comment: CVPR accepted pape
Multi-wavelet residual dense convolutional neural network for image denoising
Networks with large receptive field (RF) have shown advanced fitting ability
in recent years. In this work, we utilize the short-term residual learning
method to improve the performance and robustness of networks for image
denoising tasks. Here, we choose a multi-wavelet convolutional neural network
(MWCNN), one of the state-of-art networks with large RF, as the backbone, and
insert residual dense blocks (RDBs) in its each layer. We call this scheme
multi-wavelet residual dense convolutional neural network (MWRDCNN). Compared
with other RDB-based networks, it can extract more features of the object from
adjacent layers, preserve the large RF, and boost the computing efficiency.
Meanwhile, this approach also provides a possibility of absorbing advantages of
multiple architectures in a single network without conflicts. The performance
of the proposed method has been demonstrated in extensive experiments with a
comparison with existing techniques.Comment: 9 pages, 9 figure
Top-down design of digital signal processing systems
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaves 45-46).by Amy M. Singer.M.Eng
The Importance of Anti-Aliasing in Tiny Object Detection
Tiny object detection has gained considerable attention in the research
community owing to the frequent occurrence of tiny objects in numerous critical
real-world scenarios. However, convolutional neural networks (CNNs) used as the
backbone for object detection architectures typically neglect Nyquist's
sampling theorem during down-sampling operations, resulting in aliasing and
degraded performance. This is likely to be a particular issue for tiny objects
that occupy very few pixels and therefore have high spatial frequency features.
This paper applied an existing approach WaveCNet for anti-aliasing to tiny
object detection. WaveCNet addresses aliasing by replacing standard
down-sampling processes in CNNs with Wavelet Pooling (WaveletPool) layers,
effectively suppressing aliasing. We modify the original WaveCNet to apply
WaveletPool in a consistent way in both pathways of the residual blocks in
ResNets. Additionally, we also propose a bottom-heavy version of the backbone,
which further improves the performance of tiny object detection while also
reducing the required number of parameters by almost half. Experimental results
on the TinyPerson, WiderFace, and DOTA datasets demonstrate the importance of
anti-aliasing in tiny object detection and the effectiveness of the proposed
method which achieves new state-of-the-art results on all three datasets. Codes
and experiment results are released at
https://github.com/freshn/Anti-aliasing-Tiny-Object-Detection.git
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