A **Depthwise Fire Module** is a modification of a [Fire Module](https://paperswithcode.com/method/fire-module) with depthwise separable convolutions to improve the inference time performance. It is used in the [CornerNet](https://paperswithcode.com/method/cornernet)-Lite architecture for object detection.

** LayoutReader** is a sequence-to-sequence model for reading order detection that uses both textual and layout information, where the layout-aware language model [LayoutLM](https://paperswithcode.com/method/layoutlmv2) is leveraged as an encoder. The generation step in the encoder-decoder structure tis modified to generate the reading order sequence.

In the encoding stage, LayoutReader packs the pair of source and target segments into a contiguous input sequence of LayoutLM and carefully designs the [self-attention mask](https://paperswithcode.com/methods/category/factorized-attention) to control the visibility between tokens. As shown in the Figure, LayoutReader allows the tokens in the source segment to attend to each other while preventing the tokens in the target segment from attending to the rightward context. If 1 means allowing and 0 means preventing, the detail of the mask $M$ is as follows:

$$ M\_{i, j}= \begin{cases}1, & \text { if } i<j \text { or } i, j \in \operatorname{src} \\ 0, & \text { otherwise }\end{cases} $$

where $i, j$ are the indices in the packed input sequence, so they may be from source or target segments; $i, j \in$ src means both tokens are from source segment.

In the decoding stage, since the source and target are reordered sequences, the prediction candidates can be constrained to the source segment. Therefore, we ask the model to predict the indices in the source sequence. The probability is calculated as follows:

$$
\mathcal{P}\left(x_{k}=i \mid x_{<k}\right)=\frac{\exp \left(e_{i}^{T} h\_{k}+b\_{k}\right)}{\sum_{j} \exp \left(e\_{j}^{T} h_{k}+b\_{k}\right)}
$$

where $i$ is an index in the source segment; $e\_{i}$ and $e\_{j}$ are the $\mathrm{i}$-th and $\mathrm{j}$-th input embeddings of the source segment; $h\_{k}$ is the hidden states at the $\mathrm{k}$-th time step; $b\_{k}$ is the bias at the $\mathrm{k}$-th time step.

LayoutReader

LayoutReader: Pre-training of Text and Layout for Reading Order Detection

Depthwise Fire Module

CornerNet-Lite: Efficient Keypoint Based Object Detection

A **Reversible Residual Network**, or **RevNet**, is a variant of a [ResNet](https://paperswithcode.com/method/resnet) where each layer’s activations can be reconstructed exactly from the next layer’s. Therefore, the activations for most layers need not be stored in memory during backpropagation. The result is a network architecture whose activation storage requirements are independent of depth, and typically at least an order of magnitude smaller compared with equally sized ResNets.

RevNets are composed of a series of reversible blocks. Units in each layer are partitioned into two groups, denoted $x\_{1}$ and $x\_{2}$; the authors find what works best is partitioning the channels. Each reversible block takes inputs $\left(x\_{1}, x\_{2}\right)$ and produces outputs $\left(y\_{1}, y\_{2}\right)$ according to the following additive coupling rules – inspired the transformation in [NICE](https://paperswithcode.com/method/nice) (nonlinear independent components estimation) – and residual functions $F$ and $G$ analogous to those in standard ResNets:

$$y\_{1} = x\_{1} + F\left(x\_{2}\right)$$
$$y\_{2} = x\_{2} + G\left(y\_{1}\right)$$

Each layer’s activations can be reconstructed from the next layer’s activations as follows:

$$ x\_{2} = y\_{2} − G\left(y\_{1}\right)$$
$$ x\_{1} = y\_{1} − F\left(x\_{2}\right)$$

Note that unlike residual blocks, reversible blocks must have a stride of 1 because otherwise the layer
discards information, and therefore cannot be reversible. Standard ResNet architectures typically
have a handful of layers with a larger stride. If we define a RevNet architecture analogously, the
activations must be stored explicitly for all non-reversible layers.

Source	CornerNet-Lite: Efficient Keypoint Based Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com