They propose to treat Image Generation as an autoregressive problem where each new pixel is generated by only taking int

a function that adds plans to an object that does not currently have a nested property of tuesday.location. We have also used the nullish operators to provide default values. This function will accept falsely values like ‘0’ as valid parameters. Which means our budget can be set to zero without any errors.

Since Transformers need to learn the inductive biases for the task they are being trained for, it is always beneficial to help that learning process by all means. Any inductive bias that we can include in the inputs of the model will facilitate its learning and improve the results.

The encoder uses multiple self-attention blocks to combine the information between the different embeddings. The processed embeddings are passed to a decoder module that, using learnable embeddings as queries (object queries) that are able to attend to all the computed visual features, generates an embedding. In that embedding, all the information needed to perform the object detection is encoded. Each output is fed into a fully connected layer that will output a five-dimensional tensor with elements c and b where c will represent the predicted class for that element and b the coordinates of the bounding box (1D and 4D respectively). The value of c can be assigned to a “no object” token, that will represent an object query that did not find any meaningful detection and hence the coordinates will not be taken into account.

The input sequence consists of a flattened vector of pixel values extracted from a patch of size PxP. Each flattened element is fed into a linear projection layer that will produce what they call the “patch embeddings”. An extra learnable embedding is attached to the beginning of the sequence. This embedding, after being updated by self-attention, will be used to predict the class of the input image. A learnable positional embedding is also added to each of these embeddings.

Here, q represents the pixel embedding to be updated. It gets multiplied with all the other embeddings from pixels in memory (represented as m) using query and key matrices (Wq and Wk) generating a score that is then softmaxed and used as weights for the sum of the value vector obtained with the matrix Wv. The resulting embedding is added to the original q embedding, this way obtaining the final result. In this figure, p represent the positional encodings added to each input embedding. This encoding is generated from the coordinates of each pixel.

A hybrid architecture is also presented in this work. Instead of using projected image patches as input to the transformer, they use feature maps from the early stages of a ResNet. By training Transformers and this CNN backbone end-to-end, they achieve their best performances.

In order for these pixel values to be suitable as input for self-attention layers, each RGB value is converted into a tensor of d dimensions using 1D convolutions and the m features of the context patch are flattened to be 1 dimensional.

It uses self-attention with visual features extracted from a convolutional backbone. The feature maps computed in the backbone module are flattened over their spatial dimensions i.e., if the feature map has shape (h x w x d) the flattened result will have shape (hw x d). A learnable positional encoding is added to each dimension and the resulting sequence is fed into the encoder.

In Computer Vision, these embeddings can represent either the position of a feature in a 1 dimensional flattened sequence or they can represent a the 2 dimensional position of a feature.

When updating features with transformers, the order of the input sequence is lost. This order will be difficult or even impossible to learn by the Transformer itself, so what it is done is to aggregate a positional representation to the input embedding of the model. This positional encoding can be learned or it can be sampled from a fixed function, and the position where it is aggregated can vary , although it is usually done just at the input embeddings, right before being fed into the model.

This model is able to compute multiple detections for a single image in parallel. The number of objects that it can detect, though, is limited to the number of object queries used.

In this field, relative positional encodings have been found to work really well. They consist of learnable embeddings that learn to encode relative distances between features instead of encoding their global positions.

Note that by using self-attention, multiple pixel values can be predicted at once and in parallel (since we already know the original pixel values of the input image), and the patch used to compute self-attention can handle a higher receptive field than a convolutional layer. In evaluation though, image generation depends on each pixel having the values of their neighbors available, so it can only be performed one step at a time.

The authors of the paper claim that the model outperforms SOTA models in images with large objects. They hypothesize that this is due to the higher receptive field that self-attention provides to the model.

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