**Criss-Cross Network** (**CCNet**) aims to obtain full-image contextual information in an effective and efficient way. Concretely,
for each pixel, a novel criss-cross attention module harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies. **CCNet** is with the following
merits: **1)** GPU memory friendly. Compared with the [non-local block](https://paperswithcode.com/method/non-local-block), the proposed recurrent criss-cross attention module requires 11× less GPU memory usage. **2)** High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 85% of the non-local block. **3)** The state-of-the-art performance.

FCANet contains a novel multi-spectral channel attention module. Given an input feature map $X \in \mathbb{R}^{C \times H \times W}$, multi-spectral channel attention first splits $X$ into many parts $x^{i} \in \mathbb{R}^{C' \times H \times W}$. Then it applies a 2D DCT to each part $x^{i}$. Note that a 2D DCT can use pre-processing results to reduce computation. After processing each part,  all results are concatenated into a vector. Finally, fully connected layers, ReLU activation and a sigmoid are used to get the attention vector as in an SE block. This can be formulated as:
\begin{align}
    s = F_\text{fca}(X, \theta) & = \sigma (W_{2} \delta (W_{1}[(\text{DCT}(\text{Group}(X)))]))
\end{align}
\begin{align}
    Y & = s  X
\end{align}
where $\text{Group}(\cdot)$ indicates dividing the input into many groups and $\text{DCT}(\cdot)$ is the 2D discrete cosine transform. 

This work based on information compression and discrete cosine transforms achieves excellent performance on the classification task.

FcaNet

FcaNet: Frequency Channel Attention Networks

CCNet

CCNet: Criss-Cross Attention for Semantic Segmentation

**KNN and IoU-based Verification** is used to verify detections and choose between multiple detections of the same underlying object. It was originally used within the context of blood cell counting in medical images. To avoid this double counting problem, the KNN algorithm is applied in each platelet to determine its closest platelet and then using the intersection of union (IOU) between two platelets we calculate their extent of overlap. The authors allow 10% of the overlap between platelet and its closest platelet based on empirical observations. If the overlap is larger than that, they ignore that cell as a double count to get rid of spurious counting.

Source	CCNet: Criss-Cross Attention for Semantic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com