• Corpus ID: 232320648

Multi-Modal Answer Validation for Knowledge-Based VQA

@article{Wu2021MultiModalAV,
  title={Multi-Modal Answer Validation for Knowledge-Based VQA},
  author={Jialin Wu and Jiasen Lu and Ashish Sabharwal and Roozbeh Mottaghi},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.12248}
}
The problem of knowledge-based visual question answering involves answering questions that require external knowledge in addition to the content of the image. Such knowledge typically comes in various forms, including visual, textual, and commonsense knowledge. Using more knowledge sources increases the chance of retrieving more irrelevant or noisy facts, making it challenging to comprehend the facts and find the answer. To address this challenge, we propose Multi-modal Answer Validation using… 

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References

SHOWING 1-10 OF 59 REFERENCES
Boosting Visual Question Answering with Context-aware Knowledge Aggregation
TLDR
The proposed KG-Aug model is capable of retrieving context-aware knowledge subgraphs given visual images and textual questions, and learning to aggregate the useful image- and question-dependent knowledge which is then utilized to boost the accuracy in answering visual questions.
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge
TLDR
This paper addresses the task of knowledge-based visual question answering and provides a benchmark, called OK-VQA, where the image content is not sufficient to answer the questions, encouraging methods that rely on external knowledge resources.
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA
TLDR
This work study open-domain knowledge, the setting when the knowledge required to answer a question is not given/annotated, neither at training nor test time, and significantly out-performs state-of-the-art on OK-VQA, the largest available dataset for open- domain knowledge-based VQA.
FVQA: Fact-Based Visual Question Answering
TLDR
A conventional visual question answering dataset is extended, which contains image-question-answer triplets, through additional image- question-answer-supporting fact tuples, and a novel model is described which is capable of reasoning about an image on the basis of supporting-facts.
ConceptBert: Concept-Aware Representation for Visual Question Answering
TLDR
This work presents a concept-aware algorithm, ConceptBert, for questions which require common sense, or basic factual knowledge from external structured content, and introduces a multi-modal representation which learns a joint Concept-Vision-Language embedding inspired by the popular BERT architecture.
Explicit Knowledge-based Reasoning for Visual Question Answering
TLDR
A method for visual question answering which is capable of reasoning about contents of an image on the basis of information extracted from a large-scale knowledge base is described, addressing one of the key issues in general visual answering.
Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge
TLDR
The injection of knowledge from general-purpose knowledge bases into vision-and-language transformers is investigated and it is shown that the injection of additional knowledge regularizes the space of embeddings, which improves the representation of lexical and semantic similarities.
PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text
TLDR
PullNet is described, an integrated framework for learning what to retrieve and reasoning with this heterogeneous information to find the best answer in an open-domain question answering setting.
Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering
TLDR
A learning-based approach which goes straight to the facts via a learned embedding space is developed and demonstrated state-of-the-art results on the challenging recently introduced fact-based visual question answering dataset are demonstrated.
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering
TLDR
This work develops an entity graph and uses a graph convolutional network to `reason' about the correct answer by jointly considering all entities and shows that this leads to an improvement in accuracy of around 7% compared to the state of the art.
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