Scene recognition is a crucial task in many image processing and computer vision applications - from search by image or automatic image annotation to self-driving cars, drones and surveillance systems. State-of-the-art machine learning algorithms can solve such tasks. Yet, TensorFlow framework makes implementation of these algorithms much easier and faster.
The goal was to build a graph of objects and relationships from the pixels of the given image. The task envisaged locating objects, recognizing them, finding and classifying relationships between the objects.
We have trained an end-to-end model, which can build an object-relationship graph from the image pixel data, without using any intermediate models.
End-to-end learning is a state-of-the-art approach for image analysis and other machine learning tasks. Instead of stacking several models learned independently of each other, end-to-end learning provides a faster way to do all the optimizations by a single training run. Our model had several outputs: objects' positions, their classes, relationships between objects, and types of relationships. We trained and tested the model on the VisualGenome dataset.
Probabilistic graphical models
unsupervised sound segmentation
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