DualFashion introduces a dual-diffusion Transformer with image and text branches that generates both visual items and semantic descriptions for explainable personalized fashion recommendation.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
CS3 strengthens two-tower retrievers via cycle-adaptive feature denoising, cross-tower mutual awareness, and cascade knowledge reuse, delivering consistent gains on public datasets and up to 8.36% revenue lift in production advertising at millisecond latency.
Diff-SBSR uses a frozen Stable Diffusion backbone enhanced by multimodal CLIP and BLIP features plus Circle-T loss to outperform prior methods on zero-shot sketch-based 3D shape retrieval benchmarks.
citing papers explorer
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Dual-Diffusional Generative Fashion Recommendation
DualFashion introduces a dual-diffusion Transformer with image and text branches that generates both visual items and semantic descriptions for explainable personalized fashion recommendation.
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CS3: Efficient Online Capability Synergy for Two-Tower Recommendation
CS3 strengthens two-tower retrievers via cycle-adaptive feature denoising, cross-tower mutual awareness, and cascade knowledge reuse, delivering consistent gains on public datasets and up to 8.36% revenue lift in production advertising at millisecond latency.
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Diff-SBSR: Learning Multimodal Feature-Enhanced Diffusion Models for Zero-Shot Sketch-Based 3D Shape Retrieval
Diff-SBSR uses a frozen Stable Diffusion backbone enhanced by multimodal CLIP and BLIP features plus Circle-T loss to outperform prior methods on zero-shot sketch-based 3D shape retrieval benchmarks.