This article introduces a multimodal language model called "Image and Dial Lam". The model is capable of processing instructions in multiple input modalities, including text, images, and audio, and generating corresponding outputs. By fusing visual and verbal information, Image by Indele Lam is able to better understand and interpret the meaning of multimodal instructions.
The paper begins with an introduction to the architecture and working principle of Image by Indelelem. The model adopts a local and global attention mechanism based on visual perception, so as to better correlate image information with language information. By combining visual features with textual representation, Image by Indile Lam is able to produce more accurate and descriptive output. The paper goes on to describe the performance of Image by Indele Lam on different tasks.
Experiments have shown that Image by Indele Lam has made significant improvements in handling multimodal instructions. Compared to other models, Image Bandai Lam performs better on descriptive instruction generation and image associativity tasks, and is able to capture details and critical information in images more accurately. However, the paper also points out some limitations and failures of Image by Dilem.
For example, the model is prone to the problem of fictional objects in the generation of descriptive instructions, which may be caused by the model's insufficient acquisition of image information or the small global visual token. In addition, Image Banddialam performs weaker on some tasks than other models.
Overall, the paper introduces an innovative multimodal language model, Image Band Dialam. The model excels in processing multimodal instructions and is better able to combine visual and verbal information. However, there is still some room for improvement in the model, which needs further research and optimization.
