# A Unified Visual and Linguistic Semantics Method for Enhanced Image Captioning
## 5 Experiments
### Baselines  
To validate the effectiveness of the proposed model, we select the following baselines:  
* **CS-NIC**[34]. The CS-NIC model excels in generating Chinese captions for images, employing Jieba for precise Chinese text segmentation, addressing the unique challenge of tokenizing Chinese without explicit word boundaries. Utilizing the pool5 layer of GoogLeNet, it effectively extracts image features, with a focus on convolutional neural network (CNN) technology for nuanced visual understanding. The model sets both visual and word embeddings to a size of 512, balancing rich representation with computational efficiency. For translation tasks, it favors Baidu's service, noted for superior English-Chinese translation accuracy, underscoring its commitment to precision. CS-NIC stands out as a sophisticated tool, harmonizing advanced image processing and linguistic analysis, specifically optimized for the Chinese language image captioning domain. The CS-NIC model excels in generating Chinese captions for images through a bilingual approach, effectively integrating linguistic diversity into visual descriptions. However, its dependency on machine translation may compromise the depth and accuracy of the captions, posing challenges in fully capturing the nuances of human language. * **CIC**[45]. The Convolutional Image Captioning (CIC) model represents a paradigm shift in automated image captioning, employing a convolutional neural network (CNN) instead of traditional recurrent neural network (RNN) methods. It features a four-component architecture including input and output embedding layers, image embedding, and a convolutional module with masked convolutions. This unique structure allows CIC to forgo RNN's recurrent functions, leveraging a feed-forward deep network for direct modeling of image-word relations. The CIC model, therefore, offers a more efficient approach to image captioning, combining convolutional processing's strengths with streamlined computational complexity. The CIC model stands out for its parallel processing efficiency, offering speedy training and competitive accuracy in image captioning by leveraging CNNs. Its primary limitation, however, lies in its potential struggle with long-range textual dependencies, which may impact the coherence of generated captions for complex visuals. * **SC**[46]. The Stack-Cap (SC) model employs a unique coarse-to-fine sentence decoder for image captioning, comprising one coarse decoder and a sequence of attention-based fine decoders. This structure allows for the refined prediction of each word, using cues from the preceding decoder. Initially, the coarse decoder provides a basic description from global image features. In subsequent stages, each fine decoder enhances the image description, integrating both the image features and the output from the previous stage. This method involves using attention weights from one stage to inform the next, leading to progressively refined predictions. The architecture, with one coarse and several stacked fine decoders, demonstrates a sophisticated approach to incrementally improving image captions. The SC model excels in producing detailed image captions through a unique coarse-to-fine approach, effectively addressing the vanishing gradient issue. However, its multi-stage prediction framework increases training complexity and computational demands. * **Oscar**[47]. The Oscar model introduces a pioneering approach in vision-language pre-training (VLP) by utilizing object tags as anchor points to facilitate the learning of semantic alignments between images and texts. This model significantly enhances the process of cross-modal representation learning by structuring input as triples of word sequences, object tags, and image region features. By leveraging detected object tags in images, Oscar efficiently aligns these with corresponding textual descriptions, thereby improving the accuracy and relevance of generated image captions.