**PointNet** provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. It directly takes point clouds as input and outputs either class labels for the entire input or per point segment/part labels for each point of the input.

Source: [Qi et al.](https://arxiv.org/pdf/1612.00593v2.pdf)

Image source: [Qi et al.](https://arxiv.org/pdf/1612.00593v2.pdf)

**VGG** is a classical convolutional neural network architecture. It was based on an analysis of how to increase the depth of such networks. The network utilises small 3 x 3 filters. Otherwise the network is characterized by its simplicity: the only other components being pooling layers and a fully connected layer.

Image: [Davi Frossard](https://www.cs.toronto.edu/frossard/post/vgg16/)

Very Deep Convolutional Networks for Large-Scale Image Recognition

PointNet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

**DDParser**, or **Baidu Dependency Parser**, is a Chinese dependency parser trained on a large-scale manually labeled dataset called Baidu Chinese Treebank (DuCTB).

For inputs, for the $i$ th word, its input vector $e_{i}$ is the concatenation of the word embedding and character-level representation:

$$
e\_{i}=e\_{i}^{w o r d} \oplus C h a r L S T M\left(w\_{i}\right)
$$

Where $\operatorname{CharLSTM}\left(w_{i}\right)$ is the output vectors after feeding the character sequence into a [BiLSTM](https://paperswithcode.com/method/bilstm) layer. The experimental results on DuCTB dataset show that replacing POS tag embeddings with $\operatorname{CharLSTM}\left(w_{i}\right)$ leads to the improvement.

For the BiLSTM encoder, three BiLSTM layers are employed over the input vectors for context encoding. Denote $r\_{i}$ the output vector of the top-layer BiLSTM for $w\_{i}$

The dependency parser of [Dozat and Manning](https://arxiv.org/abs/1611.01734) is used. Dimension-reducing MLPs are applied to each recurrent output vector $r\_{i}$ before applying the biaffine transformation. Applying smaller MLPs to the recurrent output states before the biaffine classifier has the advantage of stripping away information not relevant to the current decision. Then biaffine attention is used both in the dependency arc classifier and relation classifier. The computations of all symbols in the Figure are shown below:

$$
h_{i}^{d-a r c}=M L P^{d-a r c}\left(r_{i}\right)
$$
$$
h_{i}^{h-a r c}=M L P^{h-a r c}\left(r_{i}\right) \\
$$
$$
h_{i}^{d-r e l}=M L P^{d-r e l}\left(r_{i}\right) \\
$$
$$
h_{i}^{h-r e l}=M L P^{h-r e l}\left(r_{i}\right) \\
$$
$$
S^{a r c}=\left(H^{d-a r c} \oplus I\right) U^{a r c} H^{h-a r c} \\
$$
$$
S^{r e l}=\left(H^{d-r e l} \oplus I\right) U^{r e l}\left(\left(H^{h-r e l}\right)^{T} \oplus I\right)^{T}
$$

For the decoder, the first-order Eisner algorithm is used to ensure that the output is a projection tree. Based on the dependency tree built by biaffine parser, we get a word sequence through the in-order traversal of the tree. The output is a projection tree only if the word sequence is in order.

Source	PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com