Implicit Non-Causal Factors are Out via Dataset Splitting for Domain Generalization Object Detection

• URL: http://arxiv.org/abs/2601.19127v1
• Date: Tue, 27 Jan 2026 02:52:13 GMT
• Title: Implicit Non-Causal Factors are Out via Dataset Splitting for Domain Generalization Object Detection
• Authors: Zhilong Zhang, Lei Zhang, Qing He, Shuyin Xia, Guoyin Wang, Fuxiang Huang,
• Abstract summary: Open world object detection faces a significant challenge in domain-invariant representation, i.e., implicit non-causal factors.<n>Most domain generalization (DG) methods based on domain adversarial learning (DAL) pay much attention to learn domain-invariant information, but often overlook the potential non-causal factors.<n>We unveil two critical causes: 1) The domain discriminator-based DAL method is subject to the extremely sparse domain label, i.e., assigning only one domain label to each dataset, thus can only associate explicit non-causal factor, which is incredibly limited
• Score: 42.27690662969569
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open world object detection faces a significant challenge in domain-invariant representation, i.e., implicit non-causal factors. Most domain generalization (DG) methods based on domain adversarial learning (DAL) pay much attention to learn domain-invariant information, but often overlook the potential non-causal factors. We unveil two critical causes: 1) The domain discriminator-based DAL method is subject to the extremely sparse domain label, i.e., assigning only one domain label to each dataset, thus can only associate explicit non-causal factor, which is incredibly limited. 2) The non-causal factors, induced by unidentified data bias, are excessively implicit and cannot be solely discerned by conventional DAL paradigm. Based on these key findings, inspired by the Granular-Ball perspective, we propose an improved DAL method, i.e., GB-DAL. The proposed GB-DAL utilizes Prototype-based Granular Ball Splitting (PGBS) module to generate more dense domains from limited datasets, akin to more fine-grained granular balls, indicating more potential non-causal factors. Inspired by adversarial perturbations akin to non-causal factors, we propose a Simulated Non-causal Factors (SNF) module as a means of data augmentation to reduce the implicitness of non-causal factors, and facilitate the training of GB-DAL. Comparative experiments on numerous benchmarks demonstrate that our method achieves better generalization performance in novel circumstances.

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