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Found 12 papers, 4 papers with code
BiViT: Extremely Compressed Binary Vision Transformer
To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization.
Explore Faster Localization Learning For Scene Text Detection
Generally pre-training and long-time training computation are necessary for obtaining a good-performance text detector based on deep networks.
Binarizing by Classification: Is soft function really necessary?
Binary neural network leverages the $Sign$ function to binarize real values, and its non-derivative property inevitably brings huge gradient errors during backpropagation.
Ranked #1 on
Binarization
on ImageNet
(Top 1 Accuracy metric)
Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization
In this paper, we present a simple yet effective data-free quantization method with accurate activation clipping and adaptive batch normalization.
End-to-End Video Text Spotting with Transformer
Recent video text spotting methods usually require the three-staged pipeline, i. e., detecting text in individual images, recognizing localized text, tracking text streams with post-processing to generate final results.
Contrastive Learning of Semantic and Visual Representations for Text Tracking
Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video.
A Bilingual, OpenWorld Video Text Dataset and End-to-end Video Text Spotter with Transformer
Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data.
EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident Learning and Language Modeling
Extra rich non-paired single-modal text data is used for boosting the generalization of text branch.
Polygon-free: Unconstrained Scene Text Detection with Box Annotations
For example, without using polygon annotations, PSENet achieves an 80. 5% F-score on TotalText [3] (vs. 80. 9% of fully supervised counterpart), 31. 1% better than training directly with upright bounding box annotations, and saves 80%+ labeling costs.
Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the Wild
To address the severe domain distribution mismatch, we propose a synthetic-to-real domain adaptation method for scene text detection, which transfers knowledge from synthetic data (source domain) to real data (target domain).
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems
Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem.
Ranked #13 on
Paraphrase Identification
on Quora Question Pairs
(Accuracy metric)
TextCohesion: Detecting Text for Arbitrary Shapes
In this paper, we propose a pixel-wise method named TextCohesion for scene text detection, which splits a text instance into five key components: a Text Skeleton and four Directional Pixel Regions.
Ranked #1 on
Curved Text Detection
on SCUT-CTW1500
