Context Relation Fusion Model is composed of three sub- models: Visual Relation Fusion Model (VRFM), Question Relation Fusion Model (QRFM), and Attended Feature Fusion Model (AFFM). VRFM and QRFM are designed to construct the contextual relation features of images and questions.
In this paper, we propose a novel Context Relation Fusion Model (CRFM), which produces comprehensive contextual features forcing the VQA model to more carefully ...
It aims to develop AI systems that respond to queries about images by integrating multimodal information. Early VQA models relied on convolutional neural ...
We propose a novel low-complex multi-level contextual question model, termed Context-aware Multi-level Question Embedding Fusion (CMQEF).
Oct 10, 2022 · We propose a multi-modal transformer-based architecture to overcome this issue. Our proposed architecture consists of three main modules.
Visual relationship modeling plays an indispensable role in visual question answering (VQA). VQA models need to fully understand the visual scene and ...
In this paper, we design an effective multimodal reasoning and fusion model to achieve fine-grained multimodal reasoning and fusion.
To advance models of multimodal context, we introduce a simple yet powerful neural ar- chitecture for data that combines vision and natural language.
Missing: Relation | Show results with:Relation
May 30, 2023 · According to the scope of attention, attention can be divided into soft attention and hard attention, or local attention and global attention.
Multiple Context Learning Networks for Visual Question Answering - 2022
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This article presents a novel framework, the Multiple Context Learning Network (MCLN), to model multiple context learnings for visual question answering.