2021

Make it dynamic. Dynamic Convolution Ordinary static convolution applies the same kernel to all input images, whereas dynamic convolution adapts to different images and processes them with more suitable convolution parameters, which increases the expressive power of the model without increasing network depth or width. Dynamic convolution dynamically aggregates multiple parallel convolution kernels according to attention. The attention dynamically adjusts the weight of each kernel according to the input, thereby producing an adaptive dynamic convolution. Because the attention is a function of the input, dynamic convolution is no longer a linear function. Superimposing kernels in a nonlinear way through attention gives stronger representational power. The rough structure is as follows: ...

Mathematics is the queen of the sciences. — Gauss Probabilistic undirected graphical model Before understanding the probabilistic undirected graphical model, let us look at the definition of the probabilistic graphical model (PGM): it is a probability distribution represented by a graph. Suppose there is a joint probability distribution $P(Y)$, where $Y\in \mathcal{Y}$ is a group of random variables, and the probability distribution $P(Y)$ is represented by an undirected graph $G=(V, E)$; that is, in graph $G$, a node $v\in V$ represents a random variable $Y_v$, $Y_v=(Y_v)_{v\in V}$; an edge $e\in E$ represents the probabilistic dependency between random variables. ...

Image segmentation is pixel-level classification of an image, and mainly includes semantic segmentation and instance segmentation. Semantic segmentation only requires segmentation at the pixel level, while instance segmentation additionally requires separating different individuals on top of semantic segmentation. This post briefly introduces several basic semantic segmentation networks. FCN FCN (Full Convolution Network) is a classic image segmentation network whose main structure is an encoder-decoder, where the encoder extracts image feature information and the decoder interprets the extracted features. FCN replaces the fully connected layers in the network with convolution layers, so that 2D feature maps are obtained; these are then upsampled through deconvolution (transposed convolution), and finally a softmax layer produces the mask for each class. It can also handle input images of different sizes. The rough structure is shown below. ...