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Found 11 papers, 3 papers with code
ZiNet: Linking Chinese Characters Spanning Three Thousand Years
In addition, powered by the knowledge of radical systems in ZiNet, this paper introduces glyph similarity measurement between ancient Chinese characters, which could capture similar glyph pairs that are potentially related in origins or semantics.
Toward Zero-shot Character Recognition: A Gold Standard Dataset with Radical-level Annotations
To increase the adaptability of ACCID, we propose a splicing-based synthetic character algorithm to augment the training samples and apply an image denoising method to improve the image quality.
A semantics-driven methodology for high-quality image annotation
Recent work in Machine Learning and Computer Vision has highlighted the presence of various types of systematic flaws inside ground truth object recognition benchmark datasets.
Incremental Image Labeling via Iterative Refinement
Data quality is critical for multimedia tasks, while various types of systematic flaws are found in image benchmark datasets, as discussed in recent work.
FCC: Feature Clusters Compression for Long-Tailed Visual Recognition
Through representation learning, DNNs can map BFs into dense clusters in feature space, while the features of minority classes often show sparse clusters.
Aligning Visual and Lexical Semantics
We discuss two kinds of semantics relevant to Computer Vision (CV) systems - Visual Semantics and Lexical Semantics.
CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising
Degraded images commonly exist in the general sources of character images, leading to unsatisfactory character recognition results.
RCRN: Real-world Character Image Restoration Network via Skeleton Extraction
SENet aims to preserve the structural consistency of the character and normalize complex noise.
RZCR: Zero-shot Character Recognition via Radical-based Reasoning
The long-tail effect is a common issue that limits the performance of deep learning models on real-world datasets.
Building a visual semantics aware object hierarchy
To address this problem, we propose a novel unsupervised method to build visual semantics aware object hierarchy, aiming to get a classification model by learning from pure-visual information and to dissipate the bias of linguistic representations caused by SGP.
Visual Ground Truth Construction as Faceted Classification
Recent work in Machine Learning and Computer Vision has provided evidence of systematic design flaws in the development of major object recognition benchmark datasets.
