BigdataAIML-Transformer-CV-Comparing ViT, DETR, BLIP, ViLT-Part.IV Takeaway Message

Takeaway Message

We introduced the evolution from traditional CNN-based approaches to transformer architectures, comparing vision models with language models and multimodal models. We also explored 4 fundamental computer vision tasks and their corresponding techniques, providing a practical Streamlit implementation guide to building your own computer vision web applications for further explorations.

The fundamental CV(Computer Vision) tasks and models include:

• Image Classification:
  Analyze images and assign them to one or more predefined categories or classes, utilizing model architectures like ViT (Vision Transformer).
• Image Segmentation:
  Classify image pixels into specific categories, creating detailed masks that outline object boundaries, including DETR and Mask2Former model architectures.
• Image Captioning:
  Generates descriptive text for images, demonstrating models like visual encoder-decoder and BLIP that combine visual encoding with language generation capabilities.
• VQA(Visual Question Answering):
  Process both image and text queries to answer open-ended questions based on image content, comparing architectures like ViLT (Vision Language Transformer) with its token-based outputs and BLIP with more coherent responses.