**IoU-guided NMS** is a type of non-maximum suppression that help to eliminate the suppression failure caused by the misleading classification confidences. This is achieved through using the predicted IoU instead of the classification confidence as the ranking keyword for bounding boxes. 

A **Split Attention** block enables attention across feature-map groups. As in [ResNeXt blocks](https://paperswithcode.com/method/resnext-block), the feature can be divided into several groups, and the number of feature-map groups is given by a cardinality hyperparameter $K$. The resulting feature-map groups are called cardinal groups. Split Attention blocks introduce a new radix hyperparameter $R$ that indicates the number of splits within a cardinal group, so the total number of feature groups is $G = KR$. We may apply a series of transformations {$\mathcal{F}\_1, \mathcal{F}\_2, \cdots\mathcal{F}\_G$} to each individual group, then the intermediate representation of each group is $U\_i = \mathcal{F}\_i\left(X\right)$, for $i \in$ {$1, 2, \cdots{G}$}.

A combined representation for each cardinal group can be obtained by fusing via an element-wise summation across multiple splits. The representation for $k$-th cardinal group is 
$\hat{U}^k = \sum_{j=R(k-1)+1}^{R k} U_j $, where $\hat{U}^k \in \mathbb{R}^{H\times W\times C/K}$ for $k\in{1,2,...K}$, and $H$, $W$ and $C$ are the block output feature-map sizes. 
Global contextual information with embedded channel-wise statistics can be gathered with [global average pooling](https://paperswithcode.com/method/global-average-pooling) across spatial dimensions  $s^k\in\mathbb{R}^{C/K}$. Here the $c$-th component is calculated as:

$$
    s^k\_c = \frac{1}{H\times W} \sum\_{i=1}^H\sum\_{j=1}^W \hat{U}^k\_c(i, j).
$$

A weighted fusion of the cardinal group representation $V^k\in\mathbb{R}^{H\times W\times C/K}$ is aggregated using [channel-wise soft attention](https://paperswithcode.com/method/channel-wise-soft-attention), where each feature-map channel is produced using a weighted combination over splits. The $c$-th channel is calculated as:

$$
    V^k_c=\sum_{i=1}^R a^k_i(c) U_{R(k-1)+i} ,
$$

where $a_i^k(c)$ denotes a (soft) assignment weight given by:

$$
a_i^k(c) =
\begin{cases}
  \frac{exp(\mathcal{G}^c_i(s^k))}{\sum_{j=0}^R exp(\mathcal{G}^c_j(s^k))} & \quad\textrm{if } R>1, \\
   \frac{1}{1+exp(-\mathcal{G}^c_i(s^k))} & \quad\textrm{if } R=1,\\
\end{cases}
$$

and mapping $\mathcal{G}_i^c$ determines the weight of each split for the $c$-th channel based on the global context representation $s^k$.

Split Attention

ResNeSt: Split-Attention Networks

IoU-guided NMS

Acquisition of Localization Confidence for Accurate Object Detection

**Synergistic Image and Feature Alignment** is an unsupervised domain adaptation framework that conducts synergistic alignment of domains from both image and feature perspectives. In SIFA, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features by leveraging adversarial learning in multiple aspects and with a deeply supervised mechanism. The feature encoder is shared between both adaptive perspectives to leverage their mutual benefits via end-to-end learning.

Source	Acquisition of Localization Confidence for Accurate Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com