Presents an end-to-end multitask CNN with FPN, dynamic RoI pooling, and convolutional attention for simultaneous lexicon-free text localization and recognition in complex images.
Spatial Transformer Networks
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations.
verdicts
UNVERDICTED 4representative citing papers
ADIS-GAN disentangles affine transformations in a GAN to achieve over 98% classification accuracy on MNIST within 30 degrees rotation and over 90% under FGSM and PGD attacks while generating rotation and scaling factors.
Online RNNs (RTRL, SnAp-1) beat linear filters and transformers at medium-to-long horizon forecasting of PCA respiratory motion weights in two cine-MRI datasets, yielding sub-1.4 mm and sub-2.8 mm geometric errors.
SANet adds a re-sampling-based scale-aware module to semantic segmentation networks to better handle inconsistent object scales in aerial images.
citing papers explorer
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A Multitask Network for Localization and Recognition of Text in Images
Presents an end-to-end multitask CNN with FPN, dynamic RoI pooling, and convolutional attention for simultaneous lexicon-free text localization and recognition in complex images.
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Affine Disentangled GAN for Interpretable and Robust AV Perception
ADIS-GAN disentangles affine transformations in a GAN to achieve over 98% classification accuracy on MNIST within 30 degrees rotation and over 90% under FGSM and PGD attacks while generating rotation and scaling factors.
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Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers
Online RNNs (RTRL, SnAp-1) beat linear filters and transformers at medium-to-long horizon forecasting of PCA respiratory motion weights in two cine-MRI datasets, yielding sub-1.4 mm and sub-2.8 mm geometric errors.
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SAN: Scale-Aware Network for Semantic Segmentation of High-Resolution Aerial Images
SANet adds a re-sampling-based scale-aware module to semantic segmentation networks to better handle inconsistent object scales in aerial images.