TEMA is the first framework for multi-modification composed image retrieval, using entity mapping to improve accuracy on both new complex datasets and existing benchmarks while balancing efficiency.
Optimizing instruc- tion synthesis: Effective exploration of evolutionary space with tree search
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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.
Air-Know decouples MLLM-based external arbitration from proxy learning via knowledge internalization and dual-stream training to overcome noisy triplet correspondence in composed image retrieval.
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TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval
TEMA is the first framework for multi-modification composed image retrieval, using entity mapping to improve accuracy on both new complex datasets and existing benchmarks while balancing efficiency.
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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.
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Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval
Air-Know decouples MLLM-based external arbitration from proxy learning via knowledge internalization and dual-stream training to overcome noisy triplet correspondence in composed image retrieval.