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SUR-adapter
SUR-adapter PublicACM MM'23 (oral), SUR-adapter for pre-trained diffusion models can acquire the powerful semantic understanding and reasoning capabilities from large language models to build a high-quality textual …
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Mirror-Gradient
Mirror-Gradient PublicWWW'24, Mirror Gradient (MG) makes multimodal recommendation models approach flat local minima easier compared to models with normal training.
Repositories
- SUR-adapter Public
ACM MM'23 (oral), SUR-adapter for pre-trained diffusion models can acquire the powerful semantic understanding and reasoning capabilities from large language models to build a high-quality textual semantic representation for text-to-image generation.
- Mirror-Gradient Public
WWW'24, Mirror Gradient (MG) makes multimodal recommendation models approach flat local minima easier compared to models with normal training.
- SEM Public
SEM can automatically decide to select and integrate attention operators to compute attention maps.
- SPEM Public
MMM'23, SPEM adopts a self-adaptive pooling strategy based on global max-pooling, global min-pooling and a lightweight module for producing the attention map.
- LSAS Public
ICME'23, Lightweight sub-attention strategy (LSAS) utilizes high-order sub-attention modules to improve the original self-attention modules.
- RAS Public
Recurrent Attention Strategy (RAS) improves depth with lightweight attention modules. Experiments show RAS improves performance with minimal parameter and computation cost, outperforming other attention modules.
- CEM Public
EMNLP'22, CEM improves MHCH performance by correcting prediction bias and training an auxiliary cost simulator based on user state and labor cost causal graph, without requiring complex model crafting.
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