Domain-Conditioned Class Imbalance: Why Global Class Balance Fails Across Domains

Modern visual recognition systems increasingly rely on unlabeled images collected from diverse visual domains. A common goal in dataset construction is to avoid obvious marginal imbalance, such as skewed class totals or uneven domain sizes. However, marginal balance can be misleading in multi-domain data: even when per-class totals and per-domain sample sizes are balanced, sample allocations can be highly skewed across domain-class pairs. We call this hidden conditional skew Domain-Conditioned Class Imbalance (DCI), where class imbalance is revealed only after conditioning on domain context. For controlled evaluation, we fix class and domain marginals and vary only the joint domain--class allocation, making the effect of domain-conditioned imbalance directly measurable. To address this challenge, we propose HyperIDAC, a hypernetwork-based classifier for multi-domain imbalance. HyperIDAC infers domain priors from visual features and lightweight zero-shot predictions, then dynamically generates instance-specific classifier parameters and domain-conditioned class embeddings. This design yields domain-sensitive decision boundaries while leveraging CLIP semantics to support underrepresented domain--class pairs. Furthermore, a multi-level pseudo-labeling module combines teacher–student training with historical label tracking and a fallback for low-confidence samples, mitigating bias reinforcement in unlabeled settings. Extensive experiments on four multi-domain benchmarks under three regimes show that HyperIDAC substantially outperforms state-of-the-art methods, highlighting the value of domain-aware hypernetwork-based adaptation under DCI.