Design a solution for achieving zero-shot learning in object recognition problems, such as recognizing objects from text descriptions. Explain how you would address the key challenge of separating factors of variation corresponding to invariances from other factors in the learned representation space.

To achieve zero-shot learning, we would use a multi-modal approach that combines natural language processing and computer vision techniques. We would first pre-process the text descriptions using techniques such as entity recognition and dependency parsing to extract relevant features. Then, we would train a neural network model on a large dataset of images with corresponding text descriptions. The model would learn to separate factors of variation corresponding to invariances from other factors in the learned representation space, allowing it to generalize to new object categories without requiring explicit labeled examples.