Feb. 1, 2024, 12:43 p.m. | Yuwei Sun Hideya Ochiai Zhirong Wu Stephen Lin Ryota Kanai

cs.CV updates on arXiv.org arxiv.org

Emerging from the pairwise attention in conventional Transformers, there is a growing interest in sparse attention mechanisms that align more closely with localized, contextual learning in the biological brain. Existing studies such as the Coordination method employ iterative cross-attention mechanisms with a bottleneck to enable the sparse association of inputs. However, these methods are parameter inefficient and fail in more complex relational reasoning tasks. To this end, we propose Associative Transformer (AiT) to enhance the association among sparsely attended input …

association attention attention mechanisms brain cs.cv cs.lg cs.ne inputs iterative studies transformer transformers

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