## 2 Related Work
### End-to-End Model  
In the latest years, analysts are centered on different deep learning ways to deal with video captioning. End-to-end encoder and decoder approaches are likewise becoming well known for their promising outcomes [9; 10; 11; 13]. In this model, the encoder encoded the component vectors from the basic structures through a neural network. In addition, the decoder comprised of a repetitive neural network interprets the highlights and produces sentences. LSTM is a distinct type of RNN with the ability of disappearing gradient problems. To understand the temporal dynamics of skeletal sequences, several LSTM-based models were applied previously. An attention-based spatial-temporal graph convolutional LSTM network was proposed by [59] for recognizing the karate action from the videos. Using the benefits of the Delaunay technique, the authors found effective spatiotemporal structure information of actions in karate and improved the feature extraction by applying the attention-enhanced graph convolutional LSTM networks. They also constructed a small and researchable dataset for karate action and tactics recognition. Not only are LSTM-based approaches utilized in image recognition systems, but they may also be used to identify and solve a wide variety of problems. For example, they are employed in time-series forecasting [60], energy consumption forecasting [61], and metallic gear life span prediction [62]. Although an RNN is comprised of LSTM or GRU for generating video descriptions, those models likewise presented a consideration instrument for taking a gander at the semantic area in a video outline, as far as producing printed depictions [15; 17; 20]. In those models, CNN alludes to the semantic areas of the edges, and RNN fuses the following indispensable words to deliver sentences. Those models were exceptionally prepared to utilize the different CNN and RNN models. [PERSON] et al. [15] presented consideration systems with LSTM [52] to distinguish notable developments of the visual scene, and the technique created sentences with unexpected semantic substances. They considered the 2D and 3D CNN portrayal and an LSTM decoder to create a particular word and subsequently utilized the multimodal word-embedding framework to produce the video depiction semantically. In any case, the issue with this model is that the model can only deliver a single line sentence from the visual substance, and different ongoing models are producing the best outcomes rather than this captioning model. The Bi-LSTM [16] was likewise utilized for video captioning. Visual information was encoded with a forward and reverse pass extracted from the VGG-16 CNN [42] architecture and merged the sentences in a sequential model. The technique can create each word recurrently in every period. The model presented just single-layer Bi-LSTM and solely integrated global temporal features. Video Response Map (VRM) with medium-level attention to generate video descriptions was proposed in [8]. Authors utilized the VGG-16 [42] pre-trained on ImageNet [57] for separating the video features and went with the highlights through twofold LSTM to create descriptions. The shaded video frames were taken into consideration, the modelwas compared among the past cutting-edge models, and it resulted in promising outcomes. [PERSON] et al. [30] proposed a semantic content synopsis of long recordings. The highlights from the visual substance were successively removed and later passed through a decoder to deliver textual depictions. Moreover, the long captions subsequently went through another RNN to deliver the Neural Language Generation (NLG) based textual summarization. Another decent model proposed by [PERSON] et al. [12] enhanced the audiovisual captioning superiority by joining semantic concepts with audio amplifier features. A video descriptive model, combining spatial and temporal analysis, was proposed by [PERSON] et al. [18] that can take care of different areas in given regions, dependent on what was shown in the past regions.