Turning a CLIP Model Into a Scene Text Spotter.

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 backbone, FastTCM-CR50. This backbone utilizes visual prompt learning and cross-attention in CLIP to extract image and text-based prior knowledge. Using predefined and learnable prompts, FastTCM-CR50 introduces an instance-language matching process to enhance the synergy between image and text embeddings, thereby refining text regions.

The Impact of Illegitimate Tasks on Volunteer Participation: The Perspective of Psychological Capital.

How do illegitimate tasks in volunteering affect volunteer participation? Previous research has focused only on the unidimensional effects of illegitimate tasks on volunteer participation. This study used the Job Demands-Resources model to investigate the multidimensional effects of illegitimate tasks on volunteer participation and the potential mechanisms of the effects. Based on three waves of survey data from 1768 Chinese volunteers, we found that illegitimate tasks negatively affect volunteer attitudes and volunteer outcomes by reducing volunteers' psychological capital.