**BoundaryNet** is a resizing-free approach for layout annotation. The variable-sized user selected region of interest is first processed by an attention-guided skip network. The network optimization is guided via Fast Marching distance maps to obtain a good quality initial boundary estimate and an associated feature representation. These outputs are processed by a Residual Graph [Convolution](https://paperswithcode.com/method/convolution) Network optimized using Hausdorff loss to obtain the final region boundary.

**Vision-and-Language BERT** (**ViLBERT**) is a [BERT](https://paperswithcode.com/method/bert)-based model for learning task-agnostic joint representations of image content and natural language. ViLBERT extend the popular BERT architecture to a multi-modal two-stream model, processing both visual and textual inputs in separate streams that interact through co-attentional [transformer](https://paperswithcode.com/method/transformer) layers.

ViLBERT

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

BoundaryNet

BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation

A **Gated Linear Network**, or **GLN**, is a type of backpropagation-free neural architecture. What distinguishes GLNs from contemporary neural networks is the distributed and local nature of their credit assignment mechanism; each neuron directly predicts the target, forgoing the ability to learn feature representations in favor of rapid online learning. Individual neurons can model nonlinear functions via the use of data-dependent gating in conjunction with online convex optimization. 

GLNs are feedforward networks composed of many layers of gated geometric mixing neurons as shown in the Figure . Each neuron in a given layer outputs a gated geometric mixture of the predictions from the previous layer, with the final layer consisting of just a single neuron. In a supervised learning setting, a $\mathrm{GLN}$ is trained on (side information, base predictions, label) triplets $\left(z\_{t}, p\_{t}, x\_{t}\right)_{t=1,2,3, \ldots}$ derived from input-label pairs $\left(z\_{t}, x\_{t}\right)$. There are two types of input to neurons in the network: the first is the side information $z\_{t}$, which can be thought of as the input features; the second is the input to the neuron, which will be the predictions output by the previous layer, or in the case of layer 0 , some (optionally) provided base predictions $p\_{t}$ that typically will be a function of $z\_{t} .$ Each neuron will also take in a constant bias prediction, which helps empirically and is essential for universality guarantees.

Weights are learnt in a Gated Linear Network using Online Gradient Descent (OGD) locally at each neuron. They key observation is that as each neuron $(i, k)$ in layers $i>0$ is itself a gated geometric mixture, all of these neurons can be thought of as individually predicting the target. Given side information $z$ , each neuron $(i, k)$ suffers a loss convex in its active weights $u:=w\_{i k c\_{i k}(z)}$ of
$$
\ell\_{t}(u):=-\log \left(\operatorname{GEO}\_{u}\left(x_{t} ; p\_{i-1}\right)\right)
$$

Source	BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com