The framework of our head network.

Given a feature map X, RepConv conducts parameter reorganization, resulting in X′. Then, the content-aware attention module (CAAM) separates content-aware category representations M from X′. The Dynamic Graph Convolutional Network (D-GCN) models global and local relations in M, generating a robust representation P with rich relational information across categories. Object detection is performed by DETECT on X′, producing classification scores Cls and bounding box regression results Bbox. Finally, the classification scores Cls are averaged with S, yielding the final scores Y for each category.</p

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 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

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

Migration of activator peaks in the transversal direction.

<p>(<b>a</b>) snapshot of YS domain (the YS domain is growing over time as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102718#pone-0102718-g007" target="_blank">figure 7</a>). Morphogen concentration is denoted as z-axis height. Substrate has relatively low values inside the growing rectangle, and relatively high outside the growing rectangle. Y equals to 1.0 inside the rectangle and equal to 0.0 outside that rectangle. The initial rectangular is 5 space steps wide by 10 space steps long. The length of the rectangular increases one space step every 10,000 time steps. Space step dx = 0.3, time step dt = 0.4dx². (<b>b, c</b>) profile of S along the dotted line as shown in panel a. The high/low value of S profile is 1.0/0.6 and 1.0/0.4 in panel b and c. Activator peaks migrate out of the YS domain in a left-right order and a symmetrical manner under condition b and c respectively.</p

A/H dynamics in tip splitting.

<p>(<b>a</b>) A/H dynamics as a function of S, Y. When (S, Y) pairs fall into the crescent moon region, the A/H subsystem has a classic Turing instability (by a linear Turing-instability criterion, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102718#pone.0102718-Murray1" target="_blank">[15]</a> page 87). When the (S, Y) pairs are located below the moon region, the temporal behavior of the A/H subsystem is oscillatory. For other (S, Y) pairs, the A/H subsystem has a stable temporal response. The dotted line shows a typical trajectory for cell differentiation. The cell (S, Y) state goes from the bottom right, ‘walks across’ the crescent moon, and reaches the top left. (<b>b</b>) Sites of Turing-ready cells formed a strip at the growing tip. When the black strip grew wide enough, it splits into two. Parameters:  = 2.0,  = 0.04, dx = 0.01, dt = 0.4dx², time steps between figures is 5000dt. (<b>c</b>) Dispersion relation of k1, k2, and k3 corresponds to the chosen (S, Y) pairs in the crescent moon region numbed 1, 2, and 3, respectively.</p

Growing YS domain produces activator peak insertion.

<p>When we command the rectangular YS domain to extend over time (by setting the length of the rectangular to be a function of time. The initial length of the rectangular area is 10 space steps. The length increases by 1 space step every 10,000 time steps; the width of the rectangular is held constant at 5 space steps, space step dx = 0.3, and time step dt = 0.4 dx²), A/H dynamics forms the activator peak insertion. The left column is the change of activator spatial pattern over time, and the right column is the corresponding spatial pattern of YS domain. Time increases from top to bottom. The first activator peak appears at the open end the YS domain (marked by the asterisk). More activator peaks will be induced and emerge right behind the leading activator peak when the growth creates enough space (marked by double-arrows).</p