A multimodal CNN on 87,547 Vogue images classifies fashion houses at 78.2% top-1 accuracy, decades at 88.6%, and years at 58.3% with 2.2-year mean error, and shows texture and luminance carry most of the house-identity signal.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
JBM-Diff applies conditional graph diffusion to remove preference-irrelevant multimodal noise and false-positive/negative behaviors, then augments training data via partial-order credibility scoring.
TRU is a plug-and-play unlearning method for multimodal recommenders that applies ranking fusion, modality scaling, and layer isolation to achieve better retain-forget trade-offs than uniform baselines.
citing papers explorer
-
FASH-iCNN: Making Editorial Fashion Identity Inspectable Through Multimodal CNN Probing
A multimodal CNN on 87,547 Vogue images classifies fashion houses at 78.2% top-1 accuracy, decades at 88.6%, and years at 58.3% with 2.2-year mean error, and shows texture and luminance carry most of the house-identity signal.
-
Joint Behavior-guided and Modality-coherence Conditional Graph Diffusion Denoising for Multi Modal Recommendation
JBM-Diff applies conditional graph diffusion to remove preference-irrelevant multimodal noise and false-positive/negative behaviors, then augments training data via partial-order credibility scoring.
-
TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning
TRU is a plug-and-play unlearning method for multimodal recommenders that applies ranking fusion, modality scaling, and layer isolation to achieve better retain-forget trade-offs than uniform baselines.