**Submanifold Convolution (SC)** is a spatially sparse [convolution](https://paperswithcode.com/method/convolution) operation used for tasks with sparse data like semantic segmentation of 3D point clouds. An SC convolution computes the set of active sites in the same way as a regular convolution: it looks for the presence of any active sites in its receptive field of size $f^{d}$. If the input has size $l$ then the output will have size $\left(l − f + s\right)/s$. Unlike a regular convolution, an SC convolution discards the ground state for non-active sites by assuming that the input from those sites is zero. For more details see the [paper](https://paperswithcode.com/paper/3d-semantic-segmentation-with-submanifold), or the official code [here](https://github.com/facebookresearch/SparseConvNet).

A **Neural Turing Machine** is a working memory neural network model. It couples a neural network architecture with external memory resources. The whole architecture is differentiable end-to-end with gradient descent. The models can infer tasks such as copying, sorting and associative recall.

A Neural Turing Machine (NTM) architecture contains two basic components: a neural
network controller and a memory bank. The Figure presents a high-level diagram of the NTM
architecture. Like most neural networks, the controller interacts with the external world via
input and output vectors. Unlike a standard network, it also interacts with a memory matrix
using selective read and write operations. By analogy to the Turing machine we refer to the
network outputs that parameterise these operations as “heads.”

Every component of the architecture is differentiable. This is achieved by defining 'blurry' read and write operations that interact to a greater or lesser degree with all the elements in memory (rather
than addressing a single element, as in a normal Turing machine or digital computer). The
degree of blurriness is determined by an attentional “focus” mechanism that constrains each
read and write operation to interact with a small portion of the memory, while ignoring the
rest. Because interaction with the memory is highly sparse, the NTM is biased towards
storing data without interference. The memory location brought into attentional focus is
determined by specialised outputs emitted by the heads. These outputs define a normalised
weighting over the rows in the memory matrix (referred to as memory “locations”). Each
weighting, one per read or write head, defines the degree to which the head reads or writes
at each location. A head can thereby attend sharply to the memory at a single location or
weakly to the memory at many locations

Neural Turing Machine

Neural Turing Machines

Submanifold Convolution

3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

**FuseFormer** is a [Transformer](https://paperswithcode.com/method/transformer)-based model designed for video inpainting via fine-grained feature fusion based on novel [Soft Split and Soft Composition](https://paperswithcode.com/method/soft-split-and-soft-composition) operations. The soft split divides feature map into many patches with given overlapping interval while the soft composition stitches them back into a whole feature map where pixels in overlapping regions are summed up. FuseFormer builds soft composition and soft split into its [feedforward network](https://paperswithcode.com/method/feedforward-network) for further enhancing subpatch level feature fusion.

Source	3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com