Search Results for author:
Found 22 papers, 8 papers with code
Inverse-like Antagonistic Scene Text Spotting via Reading-Order Estimation and Dynamic Sampling
Specifically, we propose an innovative reading-order estimation module (REM) that extracts reading-order information from the initial text boundary generated by an initial boundary module (IBM).
Regression-Oriented Knowledge Distillation for Lightweight Ship Orientation Angle Prediction with Optical Remote Sensing Images
Four tiny SOAP models are also created by replacing the convolutional blocks in Mobile-SOAP with four small-scale networks, respectively.
Scene Text Recognition with Single-Point Decoding Network
In this paper, we propose an efficient attention-free Single-Point Decoding Network (dubbed SPDN) for scene text recognition, which can replace the traditional attention-based decoding network.
SPR:Supervised Personalized Ranking Based on Prior Knowledge for Recommendation
Currently, two popular loss functions are widely used to optimize recommender systems: the pointwise and the pairwise.
Arbitrary Shape Text Detection via Boundary Transformer
In our method, we explicitly model the text boundary via an innovative iterative boundary transformer in a coarse-to-fine manner.
Open-set Text Recognition via Character-Context Decoupling
Contextual information can be decomposed into temporal information and linguistic information.
Channel Self-Supervision for Online Knowledge Distillation
Recently, researchers have shown an increased interest in the online knowledge distillation.
Kernel Proposal Network for Arbitrary Shape Text Detection
In this paper, we propose an innovative Kernel Proposal Network (dubbed KPN) for arbitrary shape text detection.
Towards Open-Set Text Recognition via Label-to-Prototype Learning
Scene text recognition is a popular topic and extensively used in the industry.
Supervised Contrastive Learning for Recommendation
In this work, we aim to consider the application of contrastive learning in the scenario of the recommendation system adequately, making it more suitable for recommendation task.
Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification
Learning a common latent embedding by aligning the latent spaces of cross-modal autoencoders is an effective strategy for Generalized Zero-Shot Classification (GZSC).
A Novel Deep Parallel Time-series Relation Network for Fault Diagnosis
Besides, BERT model applies absolute position embedding to introduce contextual information to the model, which would bring noise to the raw data and therefore cannot be applied to fault diagnosis directly.
Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection
In this work, we propose a novel adaptive boundary proposal network for arbitrary shape text detection, which can learn to directly produce accurate boundary for arbitrary shape text without any post-processing.
A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases
In this paper, a hierarchical deep learning framework is proposed to verify the land use information.
End-to-end trainable network for degraded license plate detection via vehicle-plate relation mining
Second, we propose to predict the quadrilateral bounding box in the local region by regressing the four corners of the license plate to robustly detect oblique license plates.
RUArt: A Novel Text-Centered Solution for Text-Based Visual Question Answering
Text-based visual question answering (VQA) requires to read and understand text in an image to correctly answer a given question.
Optical Character Recognition
Optical Character Recognition (OCR)
+2
IF-Net: An Illumination-invariant Feature Network
To show the practicality, we further evaluate IF-Net on the task of visual localization under large illumination changes scenes, and achieves the best localization accuracy.
Ranked #1 on
Image Stitching
on HPatches
Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection
In this paper, we propose a novel unified relational reasoning graph network for arbitrary shape text detection.
Semantic Bilinear Pooling for Fine-Grained Recognition
Specifically, we design a generalized cross-entropy loss for the training of the proposed framework to fully exploit the semantic priors via considering the relevance between adjacent levels and enlarge the distance between samples of different coarse classes.
Security Event Recognition for Visual Surveillance
The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.
AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition
Recognizing text in the wild is a really challenging task because of complex backgrounds, various illuminations and diverse distortions, even with deep neural networks (convolutional neural networks and recurrent neural networks).
Learning to Diversify via Weighted Kernels for Classifier Ensemble
In this paper, we argue that diversity, not direct diversity on samples but adaptive diversity with data, is highly correlated to ensemble accuracy, and we propose a novel technology for classifier ensemble, learning to diversify, which learns to adaptively combine classifiers by considering both accuracy and diversity.
