The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown
great potential in various downstream tasks via leveraging the pretrained vision and …

Due to the flexible representation of arbitrary-shaped scene text and simple pipeline, bottom-
up segmentation-based methods begin to be mainstream in real-time scene text detection …

We exploit the potential of the large-scale Contrastive Language-Image Pretraining (CLIP)
model to enhance scene text detection and spotting tasks, transforming it into a robust …

Reading text in real-world scenarios often requires understanding the context surrounding it,
especially when dealing with poor-quality text. However, current scene text recognizers are …

Ambiguous scene text detection is an extremely challenging task. Existing text detectors that
rely solely on visual cues often suffer from confusion due to being evenly distributed in …

In resource-limited keyword spotting scenarios, the scarcity of annotated corpora hinders
deep learning's ability to develop robust models for representing acoustic features. Recent …

The CLIP (Contrastive Language-Image Pre-training) model and its variants are becoming
the de facto backbone in many applications. However, training a CLIP model from hundreds …

Table detection, a pivotal task in document analysis, aims to precisely recognize and locate
tables within document images. Although deep learning has shown remarkable progress in …

Inspired by the great success of language model (LM)-based pre-training, recent studies in
visual document understanding have explored LM-based pre-training methods for modeling …

Proper interaction between visual and semantic features is crucial to obtain a powerful
feature representation for scene text recognition (STR). The existing interaction methods …