CCNet
Criss-Cross Network
**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.
CCNet: Criss-Cross Attention for Semantic Segmentation
https://paperswithcode.com/paper/ccnet-criss-cross-attention-for-semantic
CCNet: Criss-Cross Attention for Semantic Segmentation