# Bi-LS-AttM: A Bidirectional LSTM and Attention Mechanism Model for Improving Image Captioning
## 2 Related Works  
Initially, researchers utilized computers to analyze identified content in image captioning, which was the original task for image recognition [12; 13; 14]. Later, they introduced additional requirements such as processing and determining object properties, identifying object relationships, and describing image content in natural language. Since then, numerous image captioning techniques have been introduced, broadly categorized into three groups: template-based, retrieval-based, and deep-learning-based methods. Template-based methods, which utilize fixed templates for sentence generation, identify image elements such as objects, actions, and scenes based on visual dependency grammar. For instance, [PERSON] [15] used a support vector machine (SVM) [16; 17] to detect image items and pre-established templates for sentence descriptions. However, the limitations of datasets and template algorithms impeded their performance. Similarly, [PERSON] [18] employed Web-scale N-grams for phrase extraction linked to objects, actions, and relationships in 2011. Later, [PERSON] [19] used a conditional random field (CRF) [20; 21] for data extraction from a large pool of visual descriptive text, thereby improving computer vision recognition and sentence generation. Despite these efforts, the performance of these methods was suboptimal due to the inherent restrictions of template-based approaches. The retrieval-based method stores all image descriptions in a collection. The image to be described is then compared to the training set and filtered to find similar images. Using a similar image description to the one found, the candidate description is modified accordingly. [PERSON] [22] proposed to search for images with attached titles on the Internet and obtain expressive phrases as tree fragments from the test images. Then, new descriptions are composed by filtering and merging the fragments of the extraction tree. [PERSON] [23] proposed a nonparametric density estimation (NDE) technique that estimates the frequency of visual content words of the image to be detected and transforms caption generation into an extractive summarization problem. [PERSON] [24] proposed a concept automatic recognition method that uses parallel text and visual corpora. It can filter out text terms by matching the visual characteristics of similar images in the image library and the image to be described. Retrieval-based methods can be more natural language-like, although relying heavily on the capacity of the database makes it difficult to generate sentences for specific images. In recent years, with the continued advancement of deep learning, neural networks have been extensively used in image caption tasks. [PERSON] [25] first used deep neural networks and LSTM to construct two different multimodal neural network models in 2014, continuously integrating semantic information to generate words. For the encoding part, they applied an RNN to convert vocabulary into \(D\)-dimensional word vectors. The sentence described can be written as matrix \(V\ x\ D\), where \(V\) is the number of words in a sentence, and \(D\) is the size of the word vector. Finally, they used a decoder consisting of LSTM cells to generate the final picture subtitle result word by word with the combination of image features and the language model sentence by sentence. In subsequent research, [PERSON] et al. [26] incorporated attention mechanisms into the encoder and decoder structural models to describe images. By establishing an attention matrix, they can automatically focus on different areas when predicting different words at different times to enhance the description effectiveness of the model. [PERSON] [27] used generative adversarial networks to generate diversified descriptions by controlling random noise vectors. In contemporary research, [PERSON] [28] deployed the Bahdanau attention mechanism and transfer learning techniques for image caption generation.