Feb 23, 2024 · To address this, we introduce Low-Cost Multimodal Transformer (LoCoMT), a novel multimodal attention mechanism that aims to reduce computational ...
Feb 23, 2024 · In this work, we introduce Low-Cost Multimodal Transformer (LoCoMT), a novel attention mechanism for efficient multimodal fusion in Transformer.
The recently proposed Multimodal Bottleneck. Transformer (MBT) [19] aims to reduce computational cost by in- troducing bottleneck tokens into the input sequence ...
Multimodal Transformer With a Low-Computational-Cost Guarantee ; Publisher: Institute of Electrical and Electronics Engineers Inc. ; Issue Date: 2024-04 ; Citation.
Apr 16, 2024 · MULTIMODAL TRANSFORMER WITH A LOW-COMPUTATIONAL-COST GUARANTEE ; Session: MLSP-P4: Learning from Multimodal Data II Poster ; Track: Machine ...
Sungjin Park, Edward Choi, 2024, Multimodal Transformer with a Low-Computational-Cost Guarantee, Proc. of International Conference on Acoustics, Speech, and ...
Unconditional Image-Text Pair Generation with Multimodal Cross Quantizer. H ... Multimodal Transformer with a Low-Computational-Cost Guarantee. S Park, E ...
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Feb 23, 2024 · Transformer-based models have significantly improved performance across a range of multimodal understanding tasks, such as visual question ...
Aug 11, 2024 · Each use case is run over five different random dataset splits. Evaluation 1 (low-dimensional embeddings). To reduce computation costs, we first ...
Oct 31, 2024 · Relaxed Recursive Transformers with Layer-wise Low-Rank Adaptation: Achieving High Performance and Reduced Computational Cost in Large Language