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FCOS: Fully Convolutional One-Stage Object Detection
TLDR
For the first time, a much simpler and flexible detection framework achieving improved detection accuracy is demonstrated, and it is hoped that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks.
Detecting Text in Natural Image with Connectionist Text Proposal Network
TLDR
A novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image and develops a vertical anchor mechanism that jointly predicts location and text/non-text score of each fixed-width proposal, considerably improving localization accuracy.
ResNeSt: Split-Attention Networks
TLDR
A simple and modular Split-Attention block that enables attention across feature-map groups ResNet-style is presented that preserves the overall ResNet structure to be used in downstream tasks straightforwardly without introducing additional computational costs.
Bag of Tricks for Image Classification with Convolutional Neural Networks
TLDR
This paper examines a collection of training procedure refinements and empirically evaluates their impact on the final model accuracy through ablation study, and shows that by combining these refinements together, they are able to improve various CNN models significantly.
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.
TLDR
DGL distills the computational patterns of GNNs into a few generalized sparse tensor operations suitable for extensive parallelization and allows users to easily port and leverage the existing components across multiple deep learning frameworks.
Single Shot Text Detector with Regional Attention
TLDR
A novel single-shot text detector that directly outputs word-level bounding boxes in a natural image and develops a hierarchical inception module which efficiently aggregates multi-scale inception features.
An End-to-End TextSpotter with Explicit Alignment and Attention
TLDR
A novel text-alignment layer is proposed that allows it to precisely compute convolutional features of a text instance in arbitrary orientation, which is the key to boost the performance of the model on the ICDAR 2015.
Text-Attentional Convolutional Neural Network for Scene Text Detection
TLDR
A new system for scene text detection by proposing a novel text-attentional convolutional neural network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components and a powerful low-level detector called contrast-enhancement maximally stable extremal regions (MSERs) is developed.
ABCNet: Real-Time Scene Text Spotting With Adaptive Bezier-Curve Network
TLDR
For the first time, a novel BezierAlign layer is designed for extracting accurate convolution features of a text instance with arbitrary shapes, significantly improving the precision compared with previous methods and introducing negligible computation overhead.
Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation
TLDR
This work proposes a data-dependent upsampling (DUpsampling) to replace bilinear, which takes advantages of the redundancy in the label space of semantic segmentation and is able to recover the pixel-wise prediction from low-resolution outputs of CNNs.
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