The main goal of COIN is to interpret the result of VQA systems by trying to generate images with the minimum possible change from the original ones so that the VQA system changes its answer. Therefore, we evaluate here the capability of G to generate these images.
Jan 10, 2022 · In this paper, we introduce an interpretability approach for VQA models by generating counterfactual images.
An interpretability approach for VQA models by generating counterfactual images so that the generated image is supposed to have the minimal possible change ...
COIN: Counterfactual Image Generation for Visual Question Answering Interpretation. Overview of attention for article published in Sensors, March 2022.
Mar 3, 2022 · COIN is accepted by Sensors ... COIN: Counterfactual Image Generation for Visual Question Answering Interpretation” has been accepted by Sensors.
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In this ongoing work, we propose addressing this shortcoming by learning to generate counterfactual images for a VQA model – i.e. given a question-image pair, ...
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Abstract. Visual Question Answering (VQA) has been a popular task that combines vision and language, with numerous relevant implementations in literature.
Nov 14, 2019 · This ongoing work proposes learning to generate counterfactual images for a VQA model - i.e. given a question-image pair, the model is asked ...
Visual question answering (or VQA) is a new and exciting problem that combines natural language processing and computer vision techniques.
Example outputs of G for shape-based questions from the ...
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... (RQ3): Generating realistic counterfactual images is very important to interpret the result of VQA models to users. As shown in Figure 3 and Figure 4, the ...