Fashion image editing demands high-dimensional, fine-grained control to follow personalized, unpredictable natural-language instructions. Yet current methods are limited by a fundamental trade-off: fashion-specific approaches offer structural accuracy but lack semantic flexibility, while general text-driven editors are semantically flexible but structurally inaccurate. To bridge this gap, we propose Pose-Star++, a training-free, plug-and-play framework that introduces two core innovations: an LVLM-based Understanding Module that shifts from word- to sentence-level semantic-visual comprehension, eliminating cumbersome instruction pre-parsing and enabling robust understanding of complex natural language; a Bidirectional Calibration Module that co-optimizes semantic and structural constraints through forward pose-guided and backward attention-guided refinement, achieving precise, whole-body-reachable region calibration even under challenging in-the-wild poses. We further contribute the first real-world-oriented fashion-editing benchmark with diverse data, instructions, and tasks, exposing long-overlooked practical challenges. Extensive experiments demonstrate that Pose-Star++ significantly outperforms existing methods in semantic alignment, pose robustness, and in-the-wild generalization across complex scenarios, advancing toward practical, user-guided fashion creation.
Yuran Dong, Bo Du, Mang Ye· IEEE Transactions on Pattern...· 0 citations
Federated Graph Learning (FGL) enables privacy-preserving GNN training over distributed graph data, yet dynamic task streams in Federated Graph Continual Learning (FGCL) inevitably lead to catastrophic forgetting. From a spectral perspective, this forgetting manifests as two fundamental challenges: high-frequency inconsistency forgetting, where newly emerging node inconsistencies disrupt message passing and erase discriminative knowledge, and low-frequency consistency forgetting, where over-adaptation to new tasks dilutes global semantic coherence. Existing FGCL methods fail to explicitly address these dual spectral issues, resulting in severe degradation of knowledge retention and task generalization across continual learning stages. To this end, we propose FedFST, a spectral-aware framework that mitigates dual spectral forgetting. FedFST comprises Historical High-Frequency Knowledge Restoration (HHKR) to reconstruct and preserve high-frequency inconsistency knowledge, and Historical Low-Frequency Semantic Transfer (HLST) to stabilize low-frequency consistency via spectral distillation. Extensive experiments demonstrate the effectiveness of FedFST in alleviating catastrophic forgetting in FGCL. The code is available at https://github.com/YunQi572/FedFST.git.
Hanyao Guo, Zihan Tan, Wenke Huang et al.· Proceedings of the 32nd ACM...· 0 citations
Federated Graph Learning (FGL) enables privacy-preserving GNN training over distributed graph data, yet dynamic task streams in Federated Graph Continual Learning (FGCL) inevitably lead to catastrophic forgetting. From a spectral perspective, this forgetting manifests as two fundamental challenges: high-frequency inconsistency forgetting, where newly emerging node inconsistencies disrupt message passing and erase discriminative knowledge, and low-frequency consistency forgetting, where over-adaptation to new tasks dilutes global semantic coherence. Existing FGCL methods fail to explicitly address these dual spectral issues, resulting in severe degradation of knowledge retention and task generalization across continual learning stages. To this end, we propose FedFST, a spectral-aware framework that mitigates dual spectral forgetting. FedFST comprises Historical High-Frequency Knowledge Restoration (HHKR) to reconstruct and preserve high-frequency inconsistency knowledge, and Historical Low-Frequency Semantic Transfer (HLST) to stabilize low-frequency consistency via spectral distillation. Extensive experiments demonstrate the effectiveness of FedFST in alleviating catastrophic forgetting in FGCL. The code is available at https://github.com/YunQi572/FedFST.git.
Hanyao Guo, Zihan Tan, Wenke Huang et al.· Proceedings of the 32nd ACM...· 0 citations
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