**SpineNet** is a convolutional neural network backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by [Neural Architecture Search](https://paperswithcode.com/method/neural-architecture-search).

**Visformer**, or **Vision-friendly Transformer**, is an architecture that combines [Transformer](https://paperswithcode.com/methods/category/transformers)-based architectural features with those from [convolutional neural network](https://paperswithcode.com/methods/category/convolutional-neural-networks) architectures. Visformer adopts the stage-wise design for higher base performance. But [self-attentions](https://paperswithcode.com/method/multi-head-attention) are only utilized in the last two stages, considering that self-attention in the high-resolution stage is relatively inefficient even when the FLOPs are balanced. Visformer employs [bottleneck blocks](https://paperswithcode.com/method/bottleneck-residual-block) in the first stage and utilizes [group 3 × 3 convolutions](https://paperswithcode.com/method/grouped-convolution) in bottleneck blocks inspired by [ResNeXt](https://paperswithcode.com/method/resnext). It also introduces [BatchNorm](https://paperswithcode.com/method/batch-normalization) to patch embedding modules as in CNNs.

Visformer

Visformer: The Vision-friendly Transformer

SpineNet

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

**Feedback Memory** is a type of attention module used in the [Feedback Transformer](https://paperswithcode.com/method/feedback-transformer) architecture. It allows a [transformer](https://paperswithcode.com/method/transformer) to to use the most abstract representations from the past directly as inputs for the current timestep. This means that the model does not form its representation in parallel, but sequentially token by token. More precisely, we replace the context inputs to attention modules with memory vectors that are computed over the past, i.e.:

$$ \mathbf{z}^{l}\_{t} = \text{Attn}\left(\mathbf{x}^{l}\_{t},  \left[\mathbf{m}\_{t−\tau}, \dots, \mathbf{m}\_{t−1}\right]\right) $$

where a memory vector $\mathbf{m}\_{t}$ is computed by summing the representations of each layer at the $t$-th time step:

$$ \mathbf{m}\_{t} = \sum^{L}\_{l=0}\text{Softmax}\left(w^{l}\right)\mathbf{x}\_{t}^{l} $$

where $w^{l}$ are learnable scalar parameters. Here $l = 0$ corresponds to token embeddings. The weighting of different layers by a [softmax](https://paperswithcode.com/method/softmax) output gives the model more flexibility as it can average them or select one of them. This modification of the self-attention input adapts the computation of the Transformer from parallel to sequential, summarized in the Figure. Indeed, it gives the ability to formulate the representation $\mathbf{x}^{l}\_{t+1}$ based on past representations from any layer $l'$, while in a standard Transformer this is only true for $l > l'$. This change can be viewed as exposing all previous computations to all future computations, providing better representations of the input. Such capacity would allow much shallower models to capture the same level of abstraction as a deeper architecture.

Source	SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com