Update README.md

README.md

@@ -244,6 +244,9 @@ or [fastai](https://medium.com/spatial-data-science/deep-learning-for-geospatial
 * [WiCoNet](https://github.com/ggsDing/WiCoNet) -> code for 2022 [paper](https://ieeexplore.ieee.org/document/9759447): Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images
 * [CRGNet](https://github.com/YonghaoXu/CRGNet) -> code for 2022 [paper](https://arxiv.org/abs/2202.03740): Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations
 * [SA-UNet](https://github.com/Yancccccc/SA-UNet) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/15/3591): Improved U-Net Remote Sensing Classification Algorithm Fusing Attention and Multiscale Features
+* [MANet](https://github.com/lironui/Multi-Attention-Network) -> code for 2020 [paper](https://arxiv.org/abs/2009.02130): Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images
+* [BANet](https://github.com/lironui/BANet) -> code for 2021 [paper](https://www.mdpi.com/2072-4292/13/16/3065): Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images
+* [MACU-Net](https://github.com/lironui/MACU-Net) -> code for 2022 [paper](https://arxiv.org/abs/2007.13083): MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images
 
 ### Segmentation - Land use & land cover
 * [nga-deep-learning](https://github.com/jordancaraballo/nga-deep-learning) -> performs semantic segmentation on high resultion GeoTIF data using a modified U-Net & Keras, published by NASA researchers
@@ -1137,6 +1140,7 @@ Image fusion of low res multispectral with high res pan band.
 * [UCGAN](https://github.com/zhysora/UCGAN) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9755137): Unsupervised Cycle-consistent Generative Adversarial Networks for Pan-sharpening
 * [GCPNet](https://github.com/Keyu-Yan/GCPNet) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9758796): When Pansharpening Meets Graph Convolution Network and Knowledge Distillation
 * [PanFormer](https://github.com/zhysora/PanFormer) -> code for 2022 [paper](https://arxiv.org/abs/2203.02916): PanFormer: a Transformer Based Model for Pan-sharpening
+* [Pansharpening](https://github.com/nithin-gr/Pansharpening) -> code for 2021 [paper](https://www.researchgate.net/publication/356974466_Pansformers_Transformer-Based_Self-Attention_Network_for_Pansharpening): Pansformers: Transformer-Based Self-Attention Network for Pansharpening
 
 ## Image-to-image translation
 Translate images e.g. from SAR to RGB.
@@ -1246,6 +1250,7 @@ Efforts to detect falsified images & deepfakes. Also checkout [Synthetic data](h
 * [WordSent](https://github.com/hw2hwei/WordSent) -> code for 2020 [paper](https://ieeexplore.ieee.org/document/9308980): Word–Sentence Framework for Remote Sensing Image Captioning
 * [a-mask-guided-transformer-with-topic-token](https://github.com/Meditation0119/a-mask-guided-transformer-with-topic-token-for-remote-sensing-image-captioning) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/12/2939): A Mask-Guided Transformer Network with Topic Token for Remote Sensing Image Captioning
 * [MetaCaptioning](https://github.com/QiaoqiaoYang/MetaCaptioning) -> code for 2022 [paper](https://www.sciencedirect.com/science/article/abs/pii/S0924271622000351): Meta captioning: A meta learning based remote sensing image captioning framework
+* [Transformer-for-image-captioning](https://github.com/RicRicci22/Transformer-for-image-captioning) -> a transformer for image captioning, trained on the UCM dataset
 
 ## Mixed data learning
 These techniques combine multiple data types, e.g. imagery and text data.
@@ -1309,6 +1314,7 @@ Classification
 * [transfer_learning_cspt](https://github.com/ZhAnGToNG1/transfer_learning_cspt) -> code for 2022 [paper](https://arxiv.org/abs/2207.03860): Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain
 * [OTL](https://github.com/qlilx/OTL) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/14/3361): Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images
 * [Push-and-Pull-Network](https://github.com/WindVChen/Push-and-Pull-Network) -> code for 2022 paper: Contrastive Learning for Fine-grained Ship Classification in Remote Sensing Images
+* [selfsup_openrep](https://github.com/chagmgang/selfsup_openrep) -> a self-supervised learning method framework for domain representation
 
 ## Weakly & semi-supervised learning
 These techniques use a partially annotated dataset