Lu Jin — accepted AI-conference papers

2026

Dual-Estimator: Decoupling Global and Local Semantic Shift for Drift Compensation in Class-Incremental Learning

Fankang Xu, Lu Jin, Yanpeng Sun, Shiyu Xuan, Zechao Li

CVPR 2026

Continual Learning (CL) provides an effective paradigm for acquiring new knowledge, and the principle of learning without retaining past samples has led to exemplar-free CL that better matches practical conditions. However, a key challenge is the semantic shift, which requires reliable activation of

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2023

Counterfactual-based Saliency Map: Towards Visual Contrastive Explanations for Neural Networks

Xue Wang, Zhibo Wang, Haiqin Weng, Hengchang Guo, Zhifei Zhang, Lu Jin +2

ICCV 2023poster

Explaining deep models in a human-understandable way has been explored by many works that mostly explain why an input causes a corresponding prediction (ie., Why P?). However, seldom they could handle those more complex causal questions like "why P rather than Q?" and "why one is P while another is…

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