**STDC**, or **Short-Term Dense Concatenate**, is a module for semantic segmentation to extract deep features with scalable
receptive field and multi-scale information. It aims to remove structure redundancy in the BiSeNet architecture, specifically BiSeNet adds an extra path to encode spatial information which can be time-consuming,. Instead, STDC gradually reduces the dimension of feature maps and use the aggregation of them for image representation.

We concatenate response maps from multiple continuous layers, each of which encodes input image/feature in different scales and respective fields, leading to multi-scale feature representation. To speed up, the filter size of layers is gradually reduced with negligible loss in segmentation performance.

The Huber loss function describes the penalty incurred by an estimation procedure f. Huber (1964) defines the loss function piecewise by[1]

    L δ ( a ) = { 1 2 a 2 for  | a | ≤ δ , δ ⋅ ( | a | − 1 2 δ ) , otherwise. {\displaystyle L_{\delta }(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta \cdot \left(|a|-{\frac {1}{2}}\delta \right),&{\text{otherwise.}}\end{cases}}}

This function is quadratic for small values of a, and linear for large values, with equal values and slopes of the different sections at the two points where | a | = δ |a|=\delta . The variable a often refers to the residuals, that is to the difference between the observed and predicted values a = y − f ( x ) a=y-f(x), so the former can be expanded to[2]

    L δ ( y , f ( x ) ) = { 1 2 ( y − f ( x ) ) 2 for  | y − f ( x ) | ≤ δ , δ   ⋅ ( | y − f ( x ) | − 1 2 δ ) , otherwise. {\displaystyle L_{\delta }(y,f(x))={\begin{cases}{\frac {1}{2}}(y-f(x))^{2}&{\text{for }}|y-f(x)|\leq \delta ,\\\delta \ \cdot \left(|y-f(x)|-{\frac {1}{2}}\delta \right),&{\text{otherwise.}}\end{cases}}}

The Huber loss is the convolution of the absolute value function with the rectangular function, scaled and translated. Thus it "smoothens out" the former's corner at the origin. 

.. math::
        \ell(x, y) = L = \{l_1, ..., l_N\}^T

    with

    .. math::
        l_n = \begin{cases}
        0.5 (x_n - y_n)^2, & \text{if } |x_n - y_n| < delta \\
        delta * (|x_n - y_n| - 0.5 * delta), & \text{otherwise }
        \end{cases}

Huber loss

STDC

Rethinking BiSeNet For Real-time Semantic Segmentation

Self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors.

Source	Rethinking BiSeNet For Real-time Semantic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com