**MixNet** is a type of convolutional neural network discovered via AutoML that utilises MixConvs instead of regular depthwise convolutions.

**FCPose** is a fully convolutional multi-person [pose estimation framework](https://paperswithcode.com/methods/category/pose-estimation-models) using dynamic instance-aware convolutions. Different from existing methods, which often require ROI (Region of Interest) operations and/or grouping post-processing, FCPose eliminates the ROIs and grouping pre-processing with dynamic instance aware keypoint estimation heads. The dynamic keypoint heads are conditioned on each instance (person), and can encode the instance concept in the dynamically-generated weights of their filters. 

Overall, FCPose is built upon the one-stage object detector [FCOS](https://paperswithcode.com/method/fcos). The controller that generates the weights of the keypoint heads is attached to the FCOS heads. The weights $\theta\_{i}$ generated by the controller is used to fulfill the keypoint head $f$ for the instance $i$. Moreover, a keypoint refinement module is introduced to predict the offsets from each location of the heatmaps to the ground-truth keypoints. Finally, the coordinates derived from the predicted heatmaps are refined by the offsets predicted by the keypoint refinement module, resulting in the final keypoint results. "Rel. coord." is a map of the relative coordinates from all the locations of the feature maps $F$ to the location where the weights are generated. The relative coordinate map is concatenated to $F$ as the input to the keypoint head.

FCPose

FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions

MixNet

MixConv: Mixed Depthwise Convolutional Kernels

**Conditional Batch Normalization (CBN)** is a class-conditional variant of [batch normalization](https://paperswithcode.com/method/batch-normalization). The key idea is to predict the $\gamma$ and $\beta$ of the batch normalization from an embedding - e.g. a language embedding in VQA. CBN enables the linguistic embedding to manipulate entire feature maps by scaling them up or down, negating them, or shutting them off. CBN has also been used in [GANs](https://paperswithcode.com/methods/category/generative-adversarial-networks) to allow class information to affect the batch normalization parameters.

Consider a single convolutional layer with batch normalization module $\text{BN}\left(F\_{i,c,h,w}|\gamma\_{c}, \beta\_{c}\right)$ for which pretrained scalars $\gamma\_{c}$ and $\beta\_{c}$ are available. We would like to directly predict these affine scaling parameters from, e.g., a language embedding $\mathbf{e\_{q}}$. When starting the training procedure, these parameters must be close to the pretrained values to recover the original [ResNet](https://paperswithcode.com/method/resnet) model as a poor initialization could significantly deteriorate performance. Unfortunately, it is difficult to initialize a network to output the pretrained $\gamma$ and $\beta$. For these reasons, the authors propose to predict a change $\delta\beta\_{c}$ and $\delta\gamma\_{c}$ on the frozen original scalars, for which it is straightforward to initialize a neural network to produce an output with zero-mean and small variance.

The authors use a one-hidden-layer MLP to predict these deltas from a question embedding $\mathbf{e\_{q}}$ for all feature maps within the layer:

$$\Delta\beta = \text{MLP}\left(\mathbf{e\_{q}}\right)$$

$$\Delta\gamma = \text{MLP}\left(\mathbf{e\_{q}}\right)$$

So, given a feature map with $C$ channels, these MLPs output a vector of size $C$. We then add these predictions to the $\beta$ and $\gamma$ parameters:

$$ \hat{\beta}\_{c} = \beta\_{c} + \Delta\beta\_{c} $$

$$ \hat{\gamma}\_{c} = \gamma\_{c} + \Delta\gamma\_{c} $$

Finally, these updated $\hat{β}$ and $\hat{\gamma}$ are used as parameters for the batch normalization: $\text{BN}\left(F\_{i,c,h,w}|\hat{\gamma\_{c}}, \hat{\beta\_{c}}\right)$. The authors freeze all ResNet parameters, including $\gamma$ and $\beta$, during training. A ResNet consists of
four stages of computation, each subdivided in several residual blocks. In each block, the authors apply CBN to the three convolutional layers.

Source	MixConv: Mixed Depthwise Convolutional Kernels
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com