# Metaheuristics Optimization with Deep Learning Enabled Automated Image Captioning System
## 2 Prior Image Captioning Techniques  
In [PERSON] et al. [13], a fine-grained, structured attention-related technique was suggested when using the structural features of semantic matters in high-resolution distant sensing images. The segmentation is mutually trained with captioning in a unified outline with no need for pixel-wise annotations. [PERSON] et al. [14] provide an RSIR technique which mainly focuses on exploiting and producing written descriptions to precisely define the relations among the matters and their features in RS images including captions (e.g., sentences). The initial level focuses to encrypt the image's visual characteristics and later convert the encrypted features to a textual description which sums up the image content-containing captions. The next level focuses on converting the produced textual descriptions as to semantically useful feature vectors. Lastly, estimating the likeness among the textual descriptions' vectors of query images with that of archive images restores images of high likeness to the query image.  
[PERSON] et al. [15] suggested an end wise trainable deep bidirectional LSTM (Bi-LSTM) method for addressing the issue. With the combination of two separate LSTM networks and a deep CNN (DCNN), this methodology can learn long-term visual-language interactions with the help of future and historical context data at a high level semantic area. In [PERSON] et al. [16], an advanced image captioning method--with image captioning, object detection, and color analysis--was suggested for the automated generation of the textual descriptions of images. In an encrypted-decrypted method for image captioning, VGG16 can be utilized as an encoder and an LSTM network and can be employed as a decoder.  
[PERSON] et al. [17] recommend a hierarchical transformer-related medical imaging report generation technique. This presented technique has two parts: one is ann image encoder that extracts heuristic visual features through a bottom-up attention algorithm; the other is a non-recurrent captioning decoding technique that enhances computational efficacy through parallel computation. [PERSON] et al. [18] suggested an original methodology to indirectly design the association between areas of interest in an image by a graph NN along with the original context-aware attention system for guiding attention selection by completely memorizing formerly attended visual contents.  
In [PERSON] et al. [19], the authors introduced a new method to apply the Generative Adversarial Network into sequence generation. The greedy decoding method is utilized for generating an effective baseline reward for self-critical training. The visual and semantic relationship of diverse objects are combined into local-relation attention. The authors in [20] developed an attention-based encoder-decoder deep model which utilizes convolutional features derived from a CNN model that is pre-trained on ImageNet (Xception) along with the object features derived by the YOLOv4 model, pre-trained on MS COCO. The authors also introduced a novel positional encoding scheme for object features, termed the \"importance factor\".