Oct 6, 2021 · In this paper, we present a new reasoning framework to fill the gap between visual features and semantic clues in the VQA task. Our method first ...
Our Coarse-to-Fine Reasoning (CFR) framework takes an image and a question as inputs. The image is passed through the Image Embedding module to extract the re- ...
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Our Coarse-to-Fine Reasoning (CFR) framework takes an image and a question as inputs. The image is passed through the Image Embedding module to extract the re- ...
This paper proposes a new reasoning framework to fill the gap between visual features and semantic clues in the VQA task and achieves superior accuracy ...
Bridging the semantic gap between image and question is an important step to improve the accuracy of the Visual Question Answering (VQA) task.
Oct 14, 2021 · In this paper, we present a new reasoning framework to fill the gap between visual features and semantic clues in the VQA task. Our method first ...
Coarse-to-Fine Reasoning for Visual Question Answering. 2021. 4. MCB+Att. 62.2. Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual ...
Nov 24, 2024 · Abstract page for arXiv paper 2411.15770: Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering.
Our Coarse-to-Fine Reasoning (CFR) framework takes an image and a question as inputs. The image is passed through the Image Embedding module to extract the re-.
May 23, 2022 · The proposed Guided-VQA algorithm is an iterative, conditional refinement that decomposes a compositional, finegrained question into a sequence of coarse-to- ...