Generalized Category Discovery (GCD) aims to transfer knowledge from known categories to automatically discover new, unseen ones while preserving recognition of the known classes. Despite recent progress, existing GCD approaches typically assume that all data are drawn from the same distribution, which is rarely valid in real-world scenarios. In practice, data often experience simultaneous domain shifts and novel category emergence, causing severe performance degradation of existing systems. To address this challenge, we propose CausalGCD, a causality-inspired framework designed to mitigate domain-shift bias in category discovery. Specifically, we first analyze the causal graph to uncover the relationships among key variables in cross-domain GCD. We then introduce the concept of causal dependency risk and propose a Causal Dependency Risk Estimator to capture causal semantics, further deriving a theoretically computable upper bound to optimize this risk under cross-domain GCD settings. Furthermore, we propose a Causal Geometric Manifold Constraint that enforces invariant manifold-level associations between known and unknown categories across domains, thereby facilitating robust discovery of novel classes. Extensive experiments on the SSB-C and DomainNet benchmarks demonstrate the effectiveness of CausalGCD and highlight the significance of causal reasoning in open-world category discovery.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Generalized Category Discovery (GCD) aims to identify both known and novel classes from unlabeled data with the aid of labeled examples. While promising, most existing GCD methods rely on simultaneous access to labeled and unlabeled datasets--an assumption often impractical in real-world deployments. Continual Category Discovery (CCD) relaxes this requirement by adapting a pre-trained model to streaming unlabeled data, yet it typically assumes domain-consistent data distributions. This places a strong limitation on its applicability. In this work, we study Open Continual Category Discovery (OCCD), where the model must robustly discover previously unseen concepts from real-world data streams that may originate from heterogeneous and shifting domains. To address this, we propose an adaptive framework built on three key ideas. First, we propose a weight-aware separation module, which leverages partial unbalanced optimal transport for instance probability modeling and employs binary response spectrum quantization to generate cues for distinguishing known and unknown categories, enabling automatic sample separation. Second, for known categories, we introduce a cross-domain semantic alignment module that incorporates adversarial learning to perform adaptive prototype matching, thereby enhancing robustness against domain shifts. Finally, for unknown categories, we design a category topology consistency constraint that preserves semantic relationships between known and novel classes during distribution shifts. Experiments show our approach excels at discovering new categories while maintaining strong performance on known ones in evolving domains.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表