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A Focused Dynamic Attention Model for Visual Question Answering
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Visual Question and Answering (VQA) problems are attracting increasing interest from multiple research disciplines. Solving VQA problems requires techniques from both computer vision for understanding the visual contents of a presented image or video, as well as the ones from natural language processing for understanding semantics of the question and generating the answers. Regarding visual content modeling, most of existing VQA methods adopt the strategy of extracting global features from the image or video, which inevitably fails in capturing fine-grained information such as spatial configuration of multiple objects. Extracting features from auto-generated regions -- as some region-based image recognition methods do -- cannot essentially address this problem and may introduce some overwhelming irrelevant features with the question. In this work, we propose a novel Focused Dynamic Attention (FDA) model to provide better aligned image content representation with proposed questions. Being aware of the key words in the question, FDA employs off-the-shelf object detector to identify important regions and fuse the information from the regions and global features via an LSTM unit. Such question-driven representations are then combined with question representation and fed into a reasoning unit for generating the answers. Extensive evaluation on a large-scale benchmark dataset, VQA, clearly demonstrate the superior performance of FDA over well-established baselines.
Forward citations
Cited by 6 Pith papers
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A tree-structured memory network that uses parse trees and distinguishes visual from verbal words improves video question answering accuracy over flat sequence attention baselines.
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Visual question answering: from early developments to recent advances -- a survey
A survey that classifies VQA architectures by encoder, fusion, and decoder, reviews datasets and metrics, and discusses applications and future directions.
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Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey
A survey that organizes VQA methods from feature extraction through MLLM reasoning, datasets, and metrics, without introducing new experimental results.
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A Comprehensive Survey on Visual Question Answering Datasets and Algorithms
A broad but dated survey of VQA datasets and algorithms that organizes the pre-2021 literature into four dataset categories and six model paradigms.
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