ConeSep tackles noisy triplet correspondences in composed image retrieval by introducing geometric fidelity quantization to locate noise, negative boundary learning for semantic opposites, and targeted unlearning via optimal transport, outperforming prior methods on FashionIQ and CIRR.
Multimodal high-order relationship inference network for fashion compatibility modeling in internet of multimedia things.IEEE IoT, 11(1):353–365
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ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval
ConeSep tackles noisy triplet correspondences in composed image retrieval by introducing geometric fidelity quantization to locate noise, negative boundary learning for semantic opposites, and targeted unlearning via optimal transport, outperforming prior methods on FashionIQ and CIRR.