# Thangka Image--Text Matching Based on Adaptive Pooling Layer and Improved Transformer
## 3 Results
### Model Comparison Experiment  
We compared the proposed model with several powerful image-text matching baseline models. We divided the baseline models into two groups, with the first five models being VSE models and the remaining four models being pre-trained models. We chose the Thangka dataset and the Flickr30k public dataset as the evaluation datasets. For the models compared in this paper, on the Thangka dataset, we followed Section 3.2 and set the learning rate as 0.0001, epochs as 100, and batch size as 128 for training to ensure the fairness of the test results. On the public dataset, we tested the pre-trained models provided by the respective baseline model's official sources to validate the performance of our model on the public dataset. All experimental models underwent at least two to three tests, with results filtered to exclude deviations from the official data exceeding 5%, and the experimental results were averaged. The experimental results are shown in Tables 1 and 2. * SCAN ([PERSON] et al., 2018) [2]: Stacked Cross Attention for Image-Text Matching. * CAMP ([PERSON] et al., 2019) [23]: Cross-Modal Adaptive Message Passing for Text-Image Retrieval
* CAAN ([PERSON] et al., 2020) [3]: Context-Aware Attention Network for Image-Text Retrieval
* IMRAM ([PERSON] et al., 2020) [4]: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
* GSMN ([PERSON] et al., 2020) [24]: Graph Structured Network for Image-Text Matching
* ViLBERT ([PERSON] et al., 2019) [6]: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
* OSCAR ([PERSON] et al., 2020) [7]: Object-Semantics Aligned Pre-training for Vision-Language Tasks
* CLIP ([PERSON] et al., 2021) [25]: Learning Transferable Visual Models From Natural Language Supervision
* MVP ([PERSON] et al., 2022) [26]: Multi-stage vision-language pre-training via multi-level semantic alignmentTable 1 lists the recall rates (_R@1_, _R@5_, _R@10_) of different methods on the two matching tasks Image-to-Text and Text-to-Image of the Thangka image and text dataset. As can be seen from the table, without using any large-scale corpus for pre-training, the effect of this method on Thangka image and text data exceeds other models, and the recall rate _R@1_ on I2T and T2I is 54.7% and 33.6%, respectively, which are 9.5% and 10.6%, respectively, higher than the highest performance of the comparison method on the two matching tasks. It reflects that this method has better applicability and effect on the Thangka image and text dataset. At the same time, compared with the pre-trained model, the OIPS of this method is much better than other pre-trained models, which reflects that the pre-trained models of this method are much faster in training efficiency and execution speed. For the I2T and T2I tasks on the Flickr30k dataset, the comparative results are shown in Table 2.  
The experimental results of different methods on the public dataset Flickr30k are shown in Table 2. From the table, it can be observed that our method achieves a recall rate (R@1) of 77.9% for image-to-text (I2T) retrieval and 57.3% for text-to-image (T2I) retrieval on this dataset. Compared to the highest-performing IMRAM model in the best VSE method, our method improves the performance by 5.1% for I2T and 6.3% for T2I. Compared to the state-of-the-art pre-trained model MVP, our method has a lower R@1 recall rate for T2I, with a difference of approximately 2-3 percentage points for R@5 and R@10. For I2T, the R@1 recall rate is 3 percentage lower, while R@5 and R@10 are roughly the same or slightly higher. It is noteworthy that our proposed method surpasses both pre-trained models, even without utilizing any large-scale corpora for pre-training. This approach has led to significant improvements in performance. Furthermore, our model outperforms the pre-trained