Performance evaluation of semantic segmentation on the CITYSCAPES validation set using mIoU.

Performance evaluation of semantic segmentation on the CITYSCAPES validation set using mIoU.</p

Performance evaluation of semantic segmentation on the PASCAL VOC 2012 validation set using mIoU.

Performance evaluation of semantic segmentation on the PASCAL VOC 2012 validation set using mIoU.</p

DynamicFocusNet performance evaluation on MS-COCO 2017 val set.

DynamicFocusNet performance evaluation on MS-COCO 2017 val set.</p

Parameters setting.

Image data augmentation plays a crucial role in data augmentation (DA) by increasing the quantity and diversity of labeled training data. However, existing methods have limitations. Notably, techniques like image manipulation, erasing, and mixing can distort images, compromising data quality. Accurate representation of objects without confusion is a challenge in methods like auto augment and feature augmentation. Preserving fine details and spatial relationships also proves difficult in certain techniques, as seen in deep generative models. To address these limitations, we propose OFIDA, an object-focused image data augmentation algorithm. OFIDA implements one-to-many enhancements that not only preserve essential target regions but also elevate the authenticity of simulating real-world settings and data distributions. Specifically, OFIDA utilizes a graph-based structure and object detection to streamline augmentation. Specifically, by leveraging graph properties like connectivity and hierarchy, it captures object essence and context for improved comprehension in real-world scenarios. Then, we introduce DynamicFocusNet, a novel object detection algorithm built on the graph framework. DynamicFocusNet merges dynamic graph convolutions and attention mechanisms to flexibly adjust receptive fields. Finally, the detected target images are extracted to facilitate one-to-many data augmentation. Experimental results validate the superiority of our OFIDA method over state-of-the-art methods across six benchmark datasets.</div

Visual examples of object-focused image data augmentation algorithm: Localization, classification, and separation of target regions from original images.

Visual examples of object-focused image data augmentation algorithm: Localization, classification, and separation of target regions from original images.</p

Integrated view of the OFIDA framework and its modules.

Integrated view of the OFIDA framework and its modules.</p

The regression results of the effect of heterogeneous ownership balance degree on the efficiency of mixed ownership enterprises.

The regression results of the effect of heterogeneous ownership balance degree on the efficiency of mixed ownership enterprises.</p

Performance comparison of the OFIDA and several SOTA data augmentation methods for image classification.

Performance comparison of the OFIDA and several SOTA data augmentation methods for image classification.</p

The regression results of ownership structure on enterprise efficiency.

The regression results of ownership structure on enterprise efficiency.</p

The working process of the OFIDA.

Training DynamicFocusNet with the MS-COCO 2017 dataset to achieve accurate classification and localization of target images (a). Evaluating the performance of DynamicFocusNet using the MS-COCO 2017 test set (b). Utilizing the trained DynamicFocusNet to detect and localize target images in original images (c), and employing a cropping technique to accurately separate detected objects from original images (d), enabling precise one-to-many image data augmentation of samples.</p