**Position-Sensitive RoIAlign** is a positive sensitive version of [RoIAlign](https://paperswithcode.com/method/roi-align) - i.e. it performs selective alignment, allowing for the learning of position-sensitive region of interest aligning.

A **SqueezeNeXt Block** is a two-stage bottleneck module used in the [SqueezeNeXt](https://paperswithcode.com/method/squeezenext) architecture to reduce the number of input channels to the 3 × 3 [convolution](https://paperswithcode.com/method/convolution). We decompose with separable convolutions to further reduce the number of parameters (orange parts), followed by a 1 × 1 expansion module.

SqueezeNeXt Block

SqueezeNext: Hardware-Aware Neural Network Design

Position-Sensitive RoIAlign

Gumbel activation function, is defined using the cumulative Gumbel distribution and it can be used to perform Gumbel regression. Gumbel activation is an alternative activation function to the sigmoid or softmax activation functions and can be used to transform the unormalised output of a model to probability. Gumbel activation  $\eta_{Gumbel}$ is defined as follows:

$\eta_{Gumbel}(q_i) = exp(-exp(-q_i))$

It can be combined with Cross Entropy loss function to solve long-tailed classification problems. Gumbel Cross Entropy (GCE) is defined as follows:

$GCE(\eta_{Gumbel}(q_i),y_i) = -y_i \log(\eta_{Gumbel}(q_i))+ (1-y_i) \log(1-\eta_{Gumbel}(q_i))$

Year	2000
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