Update README.md

README.md

@@ -2779,7 +2779,7 @@ Image retrieval is the task of retrieving images from a collection that are simi
 
 Image Captioning is the task of automatically generating a textual description of an image. In remote sensing, image captioning can be used to automatically generate captions for satellite or aerial images, which can be useful for a variety of purposes, such as image search and retrieval, data cataloging, and data dissemination. The generated captions can provide valuable information about the content of the images, including the location, the type of terrain or objects present, and the weather conditions, among others. This information can be used to quickly and easily understand the content of the images, without having to manually examine each image. [Image source](https://github.com/chan64/remote_sensing_image_captioning)
 
-  21.1. [remote-sensing-image-caption](https://github.com/TalentBoy2333/remote-sensing-image-caption) -> image classification and image caption by PyTorch
+  21.1. [awesome-remote-image-captioning](https://github.com/iOPENCap/awesome-remote-image-captioning) -> a list of awesome remote sensing image captioning resources
 
   21.2. [Fine tuning CLIP with Remote Sensing (Satellite) images and captions](https://huggingface.co/blog/fine-tune-clip-rsicd) -> fine tuning CLIP on the [RSICD](https://github.com/201528014227051/RSICD_optimal) image captioning dataset, to enable querying large catalogues in natural language. With [repo](https://github.com/arampacha/CLIP-rsicd), uses 🤗
 
@@ -2801,6 +2801,8 @@ Image Captioning is the task of automatically generating a textual description o
 
   21.11. [Transformer-for-image-captioning](https://github.com/RicRicci22/Transformer-for-image-captioning) -> a transformer for image captioning, trained on the UCM dataset
 
+  21.12. [remote-sensing-image-caption](https://github.com/TalentBoy2333/remote-sensing-image-caption) -> image classification and image caption by PyTorch
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 ## 22. Visual Question Answering
 
@@ -3023,6 +3025,8 @@ Supervised deep learning techniques typically require a huge number of annotated
 
   27.10. [DIAL](https://github.com/alteia-ai/DIAL) -> code for 2022 [paper](https://arxiv.org/abs/2201.01047): DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing
 
+  27.11. [whales](https://github.com/microsoft/whales) -> An active learning pipeline for identifying whales in high-resolution satellite imagery, by Microsoft
+
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 ## 28. Federated learning
 Federated learning is an approach to distributed machine learning where a central processor coordinates the training of an individual model in each of its clients. It is a type of distributed ML which means that the data is distributed among different devices or locations and the model is trained on all of them. The central processor aggregates the model updates from all the clients and then sends the global model parameters back to the clients. This is done to protect the privacy of data, as the data remains on the local device and only the global model parameters are shared with the central processor. This technique can be used to train models with large datasets that cannot be stored in a single device, as well as to enable certain privacy-preserving applications.