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.
In: NeurIPS (2024)
3 Pith papers cite this work. Polarity classification is still indexing.
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Mural transfers knowledge from a frozen LLM to text-to-image synthesis via MoT shared attention, achieving 0.85 GenEval, 86.75 DPG-Bench, and 0.66 WISE while exhibiting emergent behaviors without multimodal or reasoning supervision.
Watermarking schemes for autoregressive image generation fail against removal and forgery attacks, enabling false detections and undermining synthetic content filtering.
citing papers explorer
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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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Mural: Transferring LLM knowledge to image generation via Mixture-of-Transformers
Mural transfers knowledge from a frozen LLM to text-to-image synthesis via MoT shared attention, achieving 0.85 GenEval, 86.75 DPG-Bench, and 0.66 WISE while exhibiting emergent behaviors without multimodal or reasoning supervision.
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On the Robustness of Watermarking for Autoregressive Image Generation
Watermarking schemes for autoregressive image generation fail against removal and forgery attacks, enabling false detections and undermining synthetic content filtering.