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Since the propagation of deep neural networks results in the loss of detailed feature information, the performance of most object detection methods is limited due to their tendency to learn regional features in visual space while neglecting relationships between objects. Therefore, this study proposes the Graph Relational Decision Network (GRDN), which mines relationships between objects in a dataset. The GRDN consists of a graph decision network, decision coefficient, and step-wise relation deduction module. The graph decision network comprises an edge decision network, and a node decision network, wherein a data-driven technique is employed to obtain implicit relationships between labels in a dataset. These relationships are expressed through an adaptive dynamic graph, which is subsequently recoded by means of the decision coefficient, which can enhance semantic information. In the step-wise relation deduction module, semantic information is employed as a guide to prevent distraction. A series of experiments were conducted on the MS COCO dataset. The proposed method achieves 52.8% box AP on object detection, which is 2.3% box AP higher than Cascade Mask R-CNN. The experimental results show that the addition of dynamic semantic information in this study can make up for the loss of detailed information and focus on key information, thereby improving the detection ability of small objects and occluded objects. In summary, this study extracts inter-object relationships to obtain more complete semantic information, which enriches the research of object detection. • The proposed method mines relationships between objects in a dataset. • A graph decision network is proposed to dynamically obtain semantic information. • A decision coefficient is introduced to enhance the semantic information. • The step-wise relation deduction module can obtain vital semantic information. [ABSTRACT FROM AUTHOR]

Artificial intelligence based on deep learning is better at improving the representation ability of models from data. However, due to the limitation of fixed receptive field, these agents are not able to provide a correct response outside the fixed receptive field. To address this problem, this paper provides a new perspective with improving the Image Recognition tasks. This study firstly constructs two extended receptive fields using structural causal model. Then, an approximate intervention method that changes the traditional likelihood prediction to predict the result of causal intervention is proposed. Finally, this study formulates the objective function to adapt the proxy training, which makes the whole model work well. Above all of these, a new Vision Transformer variant named Causal-ViT is proposed. Furthermore, rich experimental results of different tasks are reported. These results show that the proposed perspective makes a significant improvement in Image Recognition tasks. By simply plugging Causal-ViT to different sub-tasks, all of them bring the new benchmarks of themselves field, which proves our method is flexible. • The structural causal model is introduced for removing observation bias. • The external knowledge is incorporated into causal intervention processing. • A fusion module is designed for adapting original features and causality context. • This study conducts rich experiments and achieves new SOTA in most benchmarks. [ABSTRACT FROM AUTHOR]