#Visual recognition

Examine the evolution of Vision-Language Models in diverse visual tasks like image classification and object detection. This survey reviews the architectural frameworks, pre-training techniques, and datasets of these models, highlighting their role in zero-shot learning. It categorizes VLM methods into pre-training, transfer, and distillation, providing thorough analysis and future research avenues. Featured in TPAMI's Top 50, this publication is essential for understanding vision-language AI.

The project introduces an attention-based OCR model designed for image recognition, with tools for creating datasets and exporting trained models. Originating from the work of Qi Guo and Yuntian Deng, it employs CNN, LSTM, and attention mechanisms to enhance OCR accuracy. Installation is straightforward, accompanied by extensive training and testing features, including customizable dataset creation and model visualization. Export formats like SavedModel and frozen graph are supported. The model is suitable for scalable deployment via Tensorflow Serving and Google Cloud ML Engine, with flexible settings for diverse image processing applications.