Proposes DERNet with Decompose-Enhance-Reconstruct operator and three plug-and-play modules to shift small object detection from spatial to spectral feature processing, claiming better performance than YOLOv11 with 1/6 the parameters.
https: //arxiv.org/abs/1904.11486
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output. Commonly used downsampling methods, such as max-pooling, strided-convolution, and average-pooling, ignore the sampling theorem. The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling. However, simply inserting this module into deep networks degrades performance; as a result, it is seldomly used today. We show that when integrated correctly, it is compatible with existing architectural components, such as max-pooling and strided-convolution. We observe \textit{increased accuracy} in ImageNet classification, across several commonly-used architectures, such as ResNet, DenseNet, and MobileNet, indicating effective regularization. Furthermore, we observe \textit{better generalization}, in terms of stability and robustness to input corruptions. Our results demonstrate that this classical signal processing technique has been undeservingly overlooked in modern deep networks. Code and anti-aliased versions of popular networks are available at https://richzhang.github.io/antialiased-cnns/ .
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Strategic insertion of Global Average Pooling layers in VGG-16 reduces trainable parameters by 98%, maintains 66.4% ImageNet Top-1 accuracy, doubles translation robustness, and yields superior Spearman correlations in perceptual IQA tasks.
GeomPrompt learns a task-driven geometric prompt from RGB alone to substitute for missing or degraded depth in frozen RGB-D semantic segmentation models, yielding up to +6.1 mIoU gains on SUN RGB-D while being faster than monocular depth estimators.
citing papers explorer
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From Spatial to Spectral: An Efficient, Frequency-Guided Feature Representation Learner for Small Object Detection
Proposes DERNet with Decompose-Enhance-Reconstruct operator and three plug-and-play modules to shift small object detection from spatial to spectral feature processing, claiming better performance than YOLOv11 with 1/6 the parameters.
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Parameter-Efficient Architectural Modifications for Translation-Invariant CNNs
Strategic insertion of Global Average Pooling layers in VGG-16 reduces trainable parameters by 98%, maintains 66.4% ImageNet Top-1 accuracy, doubles translation robustness, and yields superior Spearman correlations in perceptual IQA tasks.
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GeomPrompt: Geometric Prompt Learning for RGB-D Semantic Segmentation Under Missing and Degraded Depth
GeomPrompt learns a task-driven geometric prompt from RGB alone to substitute for missing or degraded depth in frozen RGB-D semantic segmentation models, yielding up to +6.1 mIoU gains on SUN RGB-D while being faster than monocular depth estimators.