ZeroSight supplies a video-derived dataset and evaluation protocol for genuine zero-shot composed image retrieval plus the SC4CIR consistency method, demonstrating that prior benchmarks inflate reported performance across 27 tested approaches.
isearle: Improving textual inversion for zero-shot composed image retrieval
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UNVERDICTED 3representative citing papers
FlowCIR frames ZS-CIR as conditional flow matching transport on fixed VLM embeddings plus an inference-time Multi-Negative Steering fix for negation, reporting competitive benchmark results at far lower training cost.
E5-V produces strong universal multimodal embeddings from MLLMs trained solely on text pairs, often surpassing prior methods across retrieval and related tasks without multimodal fine-tuning.
citing papers explorer
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Never Seen Before: Benchmarking Genuine Zero-Shot Composed Image Retrieval with Consistent Video-Sourced Datasets
ZeroSight supplies a video-derived dataset and evaluation protocol for genuine zero-shot composed image retrieval plus the SC4CIR consistency method, demonstrating that prior benchmarks inflate reported performance across 27 tested approaches.
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FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval
FlowCIR frames ZS-CIR as conditional flow matching transport on fixed VLM embeddings plus an inference-time Multi-Negative Steering fix for negation, reporting competitive benchmark results at far lower training cost.
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E5-V: Universal Embeddings with Multimodal Large Language Models
E5-V produces strong universal multimodal embeddings from MLLMs trained solely on text pairs, often surpassing prior methods across retrieval and related tasks without multimodal fine-tuning.