While deep generative models have significantly advanced representation learning, they may inherit or amplify biases and fairness issues by encoding sensitive attributes alongside predictive features. Enforcing strict independence in disentanglement is often unrealistic when target and sensitive factors are naturally correlated. To address this challenge, we propose CAD-VAE(Correlation-Aware Disentangled VAE), which introduces a correlated latent code to capture the information shared between the target and sensitive attributes. Given this correlated latent, our method effectively separates overlapping factors without extra domain knowledge by directly minimizing the conditional mutual information between target and sensitive codes. A relevance-driven optimization strategy refines the correlated code by efficiently capturing essential correlated features and eliminating redundancy. Extensive experiments on benchmark datasets demonstrate that CAD-VAE produces fairer representations, realistic counterfactuals, and improved fairness-aware image editing.

Xi Xiao, Chenrui Ma, Yunbei Zhang, Chen Liu, Zhuxuanzi Wang, Yanshu Li, Lin Zhao, Guosheng Hu, Tianyang Wang, Hao Xu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

The deployment of automated pavement defect detection is often hindered by poor cross-domain generalization. Supervised detectors achieve strong in-domain accuracy but require costly re-annotation for new environments, while standard self-supervised methods capture generic features and remain vulnerable to domain shift. We propose PROBE, a self-supervised framework that visually probes target domains without labels. PROBE introduces a Self-supervised Prompt Enhancement Module (SPEM), which derives defect-aware prompts from unlabeled target data to guide a frozen ViT backbone, and a Domain-Aware Prompt Alignment (DAPA) objective, which aligns prompt-conditioned source and target representations. Experiments on four challenging benchmarks show that PROBE consistently outperforms strong supervised, self-supervised, and adaptation baselines, achieving robust zero-shot transfer, improved resilience to domain variations, and high data efficiency in few-shot adaptation. These results highlight self-supervised prompting as a practical direction for building scalable and adaptive visual inspection systems. Source code is publicly available: https://github.com/xixiaouab/PROBE/tree/main

The evolution of individuals' living standards has transformed clothing preferences, elevating fashion beyond mere utility to a potent means of self-expression. However, the intricate task of outfit selection persists as a challenge, marked by traditional methods facing challenges such as the oversight of combined factors of scene and body shape, insufficient emphasis on detail-oriented matching, and overreliance on rigid hierarchical structures. To tackle these challenges, this article introduces a novel model, termedGlobal-Local matching network towardsOutfit recommendation for diverse bodyShapes andScenes (GLOSS). Specifically, we first introduce a newly compiled fashion dataset, StreetFashion, to capture the combined factors of body shapes and scene characteristics. Additionally, we develop innovative multi-level globality- and locality-aware matching methods to enhance the accuracy of outfit recommendations by comprehensively considering both global and local relationships among clothing items, outfits, users, and scenes. Furthermore, we develop a personalized outfit heterogeneous graph that incorporates historical interactions among fashion entities, enabling effective modeling of nonstrict hierarchical relationships. Evaluation conducted on both our collected dataset and an adapted existing dataset demonstrates the effectiveness of our proposed approach in outfit recommendation. Our codes are released at:https://github.com/ChenrMa/GLOSS.

Sequential recommender systems have demonstrated a huge success for next-item recommendation by explicitly exploiting the temporal order of users' historical interactions. In practice, user interactions contain more useful temporal information beyond order, as shown by some pioneering studies. In this paper, we systematically investigate various temporal information for sequential recommendation and identify three types of advantageous temporal patterns beyond order, including absolute time information, relative item time intervals and relative recommendation time intervals. We are the first to explore item-oriented absolute time patterns. While existing models consider only one or two of these three patterns, we propose a novel holistic temporal pattern based neural network, named HTP, to fully leverage all these three patterns. In particular, we introduce novel components to address the subtle correlations between relative item time intervals and relative recommendation time intervals, which render a major technical challenge. Extensive experiments on three real-world benchmark datasets show that our HTP model consistently and substantially outperforms many state-of-the-art models. Our code is publically available at https://github.com/623851394/HTP/tree/main/HTP-main.