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Nikolas Markou - Artificial Intelligence for Vision: A walkthrough of recent breakthroughs
Explore the latest breakthroughs in computer vision, including the emergence of transformers, multi-scale vision transformers, and innovative models that can recognize objects in images, videos, and 3D data.
- Computer vision is the field of AI that helps machines interpret and understand visual information.
- The recent breakthroughs in computer vision are due to the emergence of transformers, which have enabled the creation of larger and more powerful models.
- Visual transformers treat images as sequences of patches employing transformer encoding, similar to language models.
- The multi-scale vision transformer is a recent innovation that has achieved state-of-the-art results in image recognition and object detection tasks.
- The vision transformer has integrated images as a kind of language, allowing the model to understand and recognize objects in images.
- The transformer architecture with its novel attention mechanism has changed the field of computer vision.
- Computer vision is no longer limited to static images, but can now handle videos and 3D data.
- The field of computer vision is evolving rapidly, with new breakthroughs and innovations being developed continuously.
- The most commonly used models for object detection are YOLO versions 8 and 5, which have dominated the field due to their speed and accuracy.
- The ConvNext family of models, especially ConvNext V1 and V2, are good alternatives to traditional CNN-based models.
- The number of parameters in a model has a significant impact on its performance, with larger models generally performing better.
- The activation functions used in the model can also impact its performance, with Swish being the most recent and popular activation function.
- Data augmentation techniques are essential for improving the performance of computer vision models.
- The future of computer vision is likely to involve the development of larger and more powerful models that can handle complex tasks such as scene understanding and object tracking.
- The rise of transformers in computer vision has enabled the creation of models that can handle multiple modalities, including images, text, and speech.