In the fields of image processing and computer vision, model structures that realize multiple functions have always been the goals pursued by researchers. Currently, a common method is to design independent model structures for different tasks, which will cause repeated calculations and waste of memory. Therefore, a model structure that can handle different tasks under the same framework becomes increasingly important.
To this end, this paper proposes a multi-task learning method based on diffusion models and indicator vectors, which can achieve four functions: image blending, harmonization, perspective adjustment, and object placement. This model structure not only improves the performance of each task, but also fully exploits the interdependence and optimizability between different tasks.
Our model structure adopts a two-branch network structure, one of which is used to implement image blending and harmonization tasks, and the other is used to implement perspective adjustment and object placement tasks. In each branch, we use a diffusion model to further improve the performance. The advantage of this model structure is that different tasks can share network features, avoiding repeated calculations and memory waste.
Specifically, we input background images and foreground object images with foreground bounding boxes, then extract global features and local features of the foreground objects, and perform global feature fusion and local feature fusion. During the local fusion process, we use aligned foreground feature maps for feature modulation to achieve better detail preservation. Indicator vectors are used in both global fusion and local fusion, aiming to more fully control the properties of foreground objects.
To verify the performance of the model, we conduct experiments on multiple datasets. Experimental results show that the proposed model outperforms current state-of-the-art methods on both the evaluation metrics PSNR and SSIM in image blending and harmonization tasks. In view adjustment and object placement tasks, it outperforms other methods on the evaluation metrics MAE and IoU. This shows that the model has good performance and feasibility.
When further analyzing the experimental results, we found that the performance and feasibility of the model structure were affected by some factors. First, the variability of different data sets will affect the performance of the model, because factors such as image quality, task type, and difficulty of different data sets are different. Second, the hyperparameter settings of the model also affect performance, such as learning rate, batch size, and weight decay, etc. In addition, the way of data augmentation will also affect the performance of the model, because different data augmentation methods will affect the generalization ability and robustness of the model.
To sum up, the multi-task learning method based on diffusion model and indicator vector proposed in this article provides a new idea for realizing a multi-functional model structure. We believe that this method is of great significance to research in the fields of image processing and computer vision and can provide useful reference for future research. At the same time, we also hope to further explore the performance and feasibility of the model structure so that it can be better applied to practical problems.