7 accepted papers
Model ensembling is a widely used technique that enhances performance in image-text matching tasks by combining multiple models, each trained with different initializations. However, the inefficiencies associated with training several models and generating outputs from them constrain their practical…
This paper introduces a novel exemplar-based framework for reading Chinese texts in natural scene or document images. We present the Deep Exemplar-based Chinese Text Recognizer, which is structured to first identify candidate characters as exemplars from each text-line, and subsequently recognize th…
Image-text matching is a crucial task that bridges visual and linguistic modalities. Recent research typically formulates it into the problem of maximizing the margin with the truly hardest negatives to enhance the learning efficiency and avoid the poor local optima. We argue that such formulation c…
Owing to powerful generative priors, Text-to-Image (T2I) diffusion models have achieved promising results in image colorization task. However, recent advanced methods primarily integrate global semantics. Such practice neglects local semantics, yielding suboptimal colorization performance. In this p…
Recent data-driven image colorization methods have leveraged pre-trained Text-to-Image (T2I) diffusion models as generative prior, while still suffering from unsatisfactory and inaccurate semantic-level color control. To address these issues, we propose a Semantic Adaptation method (SeAda) that enha
Scene text image super-resolution has significantly improved the accuracy of scene text recognition. However, many existing methods emphasize performance over efficiency and ignore the practical need for lightweight solutions in deployment scenarios. Faced with the issues, our work proposes an effic…
Texts from scene images typically consist of several characters and exhibit a characteristic sequence structure. Existing methods capture the structure with the sequence-to-sequence models by an encoder to have the visual representations and then a decoder to translate the features into the label se…