7 accepted papers
Handwritten text generation (HTG) is a challenging task due to the vast diversity of handwriting styles. In addition, even the same writer can show subtle differences when writing the same character, further aggravating the difficulties of HTG. In this paper, we propose a novel Adaptive Style-aware…
Handwritten text generation (HTG) aims to synthesize handwritten samples by imitating a specific writer, which has a wide range of applications and thus has significant research value. However, current studies on HTG are confronted with a main bottleneck: dominant models lack the ability to perceive…
Seismic phase picking is one of the critical challenges in seismic data processing. With the advancement of deep learning, numerous neural network architectures have been employed to explore the correlations between seismic waveforms and the underlying information. However, existing methods predomin…
Constructing a highly accurate handwritten OCR system requires large amounts of high-quality training data, yet data collection is labor-intensive and costly. With the advance of generative models, high-quality synthetic images have been applied to enhance handwritten text recognition (HTR) models,…
In this paper, we leverage Contrastive Language-Image Pre-training (CLIP) for Historical Document Image Retrieval (HDIR). We are largely inspired by recent advances on CLIP and its exceptional generalization capabilities, but for the first time, we tailor it to benefit HDIR. We put forward a dual-br…
In the domain of historical Mongolian document image retrieval (HMDIR), word spotting poses a inherent challenge due to the frequent appearance of out-of-vocabulary (OOV) words. Existing methods have mainly focused on query-by-example (QBE), neglecting the query-by-string (QBS) approach. Meanwhile,…
The deep image hashing aims to map the input image into simply binary hash codes via deep neural networks. Motivated by the recent advancements of Vision Transformers (ViT), many deep hashing methods based on ViT have been proposed. Nevertheless, the ViT has enormous number of model parameters and h…