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

2026-01-27

AI Summary

This work addresses the challenge posed by implicit non-causal factors that interfere with domain-invariant representation learning in open-world object detection. To mitigate this issue, the authors propose a decoupling approach based on fine-grained domain partitioning and explicit simulation of non-causal factors. Specifically, they introduce Prototype-based Granular Ball Segmentation (PGBS) to generate dense domain labels and design a Simulated Non-causal Factors (SNF) data augmentation strategy. These components are integrated into an enhanced domain-adversarial learning framework, termed GB-DAL, which effectively overcomes the reliance of conventional methods on sparse domain annotations and implicit biases. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method significantly outperforms existing approaches, achieving substantial improvements in generalization performance for object detection under unknown domains.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Representation, semantic annotation, enhancement, enrichments, access and/or integration of a variety of data on the Web

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.

Problem

• implicit non-causal factors
• domain generalization
• object detection
• dataset splitting
• domain-invariant representation

Innovation

• Domain Generalization
• Non-causal Factors
• Granular-Ball Splitting
• Adversarial Learning
• Object Detection