Recursive Counterfactual Deconfounding for image recognition
Year: 2025
Type: article
Abstract: Image recognition is a classic and common task that has been widely applied in computer vision in the past decade. Most existing methods in literature aim to learn discriminative features for classification from labeled images; however, they generally neglect confounders that infiltrate the learned features, resulting in low discrimination performances on test images. To address this problem, we propose a Recursive Counterfactual Deconfounding model for image recognition in both closed- and open-set scenarios based on counterfactual analysis, called RCD. The proposed model consists of a factual graph and a counterfactual graph, where the relationships among image features , model predictions, and confounders are built and updated recursively to learn more discriminative features. It performs recursively so that subtler counterfactual features can be learned and progressively eliminated. Both the discriminability and generalizability of the proposed model can be improved accordingly. In addition, a negative correlation constraint is designed to alleviate the negative effects of counterfactual features during model training. Extensive experimental results in both the closed-set and open-set image recognition tasks demonstrate the effectiveness of the proposed method. Particularly under the hard testing mode of the three public datasets (Aircraft, CUB and ImageNet), the proposed method obtains a higher AUROC (Area Under the Receiver Operating Characteristic curves) by 0.7%–3.5% and a higher OSCR (Open-Set Classification Rate) by 0.2%–4.0%.
Source: Knowledge-Based Systems
Authors: Jiayin Sun, Mengyu Gao, Hong Wang, Qiulei Dong
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,430
OpenAlex ID: W4408134279