Controllable image synthesis models allow creation of diverse images based on text instructions or guidance from a reference image.
Permutations then serve as target generation orders for training an insertion-based Transformer language model.
Large-scale pretraining of visual representations has led to state-of-the-art performance on a range of benchmark computer vision tasks, yet the benefits of these techniques at extreme scale in complex production systems has been relatively unexplored.
One strategy to recover this information is to decode both the content and location of tokens.
To this end, reconstruction-based learning is often used in which the normality of an observation is expressed in how well it can be reconstructed.
The Vision Transformer was the first major attempt to apply a pure transformer model directly to images as input, demonstrating that as compared to convolutional networks, transformer-based architectures can achieve competitive results on benchmark classification tasks.
The solution we present not only allows us to train for multiple application objectives in a single deep neural network architecture, but takes advantage of correlated information in the combination of all training data from each application to generate a unified embedding that outperforms all specialized embeddings previously deployed for each product.
We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths.
We also introduce a multimodal methodology for generating visual and textual explanations simultaneously.
In contrast, humans can justify their decisions with natural language and point to the evidence in the visual world which led to their decisions.
Approaches to multimodal pooling include element-wise product or sum, as well as concatenation of the visual and textual representations.