The QMulFA includes three steps: (1) fusing the multimodal information to generate feature-wise attention weight vectors, (2) squeezing the attention weight vectors, and (3) adjusting the question features.
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In this paper, we propose a novel neural network module named “multimodal feature-wise attention module” (MulFA) to model the feature-wise attention. Extensive ...
Sep 1, 2021 · By introducing MulFA modules, we construct an effective union feature-wise and spatial co-attention network (UFSCAN) model for VQA. Our ...
In this paper, we propose a novel neural network module named “multimodal feature-wise attention module” (MulFA) to model the feature-wise attention. Extensive ...
Abstract. Our model revolves around expanding web-searching to the multi-domain , our project help to cover the gap present in today's research with regards ...
Multimodal feature-wise co-attention method for visual question answering. Work. HTML. Year: 2021. Type: article. Source: Information fusion.
May 30, 2023 · The multi-mode multiplicative feature embedding effectively fuses the features of free form image area, detection frame and question ...
MedFuseNet: An attention-based multimodal deep learning model for ...
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Oct 6, 2021 · A high-level model design for the task of VQA. The model has four major components—image feature extraction, question feature extraction, ...
In this paper, we propose an attention-based multi-modal fusion to combine image and question features by dynam- ically deciding how much weight to put on each ...
Dec 21, 2023 · To deal with this issue, we have used a Two-way Co-Attention Mechanism (TCAM), which is capable enough to fuse different visual features (region ...