The pipeline we propose consists of three parts: 1) recognizing the interaction type; 2) detecting the object that the interaction is targeting; and 3) learning incrementally the models from data recorded by the robot sensors. Our main contributions lie in the target object detection, guided by the recognized interaction, and in the incremental object learning. The novelty of our approach is the focus on natural, heterogeneous, and multimodal HRIs to incrementally learn new object models.

**Weight Standardization** is a normalization technique that smooths the loss landscape by standardizing the weights in convolutional layers. Different from the previous normalization methods that focus on *activations*, WS considers the smoothing effects of *weights* more than just length-direction decoupling. Theoretically, WS reduces the Lipschitz constants of the loss and the gradients.
Hence, WS smooths the loss landscape and improves training.

In Weight Standardization, instead of directly optimizing the loss $\mathcal{L}$ on the original weights $\hat{W}$, we reparameterize the weights $\hat{W}$ as a function of $W$, i.e. $\hat{W}=\text{WS}(W)$, and optimize the loss $\mathcal{L}$ on $W$ by [SGD](https://paperswithcode.com/method/sgd):

$$
    \hat{W} = \Big[ \hat{W}\_{i,j}~\big|~ \hat{W}\_{i,j} = \dfrac{W\_{i,j} - \mu\_{W\_{i,\cdot}}}{\sigma\_{W\_{i,\cdot}+\epsilon}}\Big]
$$

$$
    y = \hat{W}*x
$$

where

$$
    \mu_{W\_{i,\cdot}} = \dfrac{1}{I}\sum\_{j=1}^{I}W\_{i, j},~~\sigma\_{W\_{i,\cdot}}=\sqrt{\dfrac{1}{I}\sum\_{i=1}^I(W\_{i,j} - \mu\_{W\_{i,\cdot}})^2}
$$

Similar to [Batch Normalization](https://paperswithcode.com/method/batch-normalization), WS controls the first and second moments of the weights of each output channel individually in convolutional layers. Note that many initialization methods also initialize the weights in some similar ways. Different from those methods, WS standardizes the weights in a differentiable way which aims to normalize gradients during back-propagation. Note that we do not have any affine transformation on $\hat{W}$. This is because we assume that normalization layers such as BN or [GN](https://paperswithcode.com/method/group-normalization) will normalize this convolutional layer again.

Weight Standardization

Micro-Batch Training with Batch-Channel Normalization and Weight Standardization

HRI pipeline

SoftPool: a fast and efficient method that sums exponentially weighted activations. Compared to a range of other pooling methods, SoftPool retains more information in the downsampled activation maps. More refined downsampling leads to better classification accuracy.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com