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However, pronounced geometric heterogeneity across categories entangles incompatible optimization signals in shared modules, resulting in gradient conflicts and negative transfer during training. To address this challenge, we first introduce gradient-based diagnostics to quantify module-level cross-category contention. Building on results of diagnostics, we propose DecomPose, a difficulty-aware decomposition framework that mitigates optimization contention via: (1) difficulty-aware gradient decoupling, which groups categories using a data-driven difficulty proxy and routes each instance to a group-specific correspondence branch to isolate incompatible updates; and (2) stability-driven asymmetric branching, which assigns higher-capacity branches to structurally simple categories as stable optimi","title":"DecomPose: Disentangling Cross-Category Optimization Contention for Category-Level 6D Object Pose Estimation","url":"https://arxiv.org/abs/2605.15728","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.15728v1 Announce Type: cross \nAbstract: Category-level 6D object pose estimation is typically formulated as a multi-category joint learning problem with fully shared model parameters. 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