mlsanityData to 14 Oct 2026

Check It Again: Progressive Visual Question Answering via Visual Entailment

arXiv 2106.04605 ↗First posted 8 Jun 2021cs.CVcited by 18

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Abstract (arXiv)

While sophisticated Visual Question Answering models have achieved remarkable success, they tend to answer questions only according to superficial correlations between question and answer. Several recent approaches have been developed to address this language priors problem. However, most of them predict the correct answer according to one best output without checking the authenticity of answers. Besides, they only explore the interaction between image and question, ignoring the semantics of candidate answers. In this paper, we propose a select-and-rerank (SAR) progressive framework based on Visual Entailment. Specifically, we first select the candidate answers relevant to the question or the image, then we rerank the candidate answers by a visual entailment task, which verifies whether the image semantically entails the synthetic statement of the question and each candidate answer. Experimental results show the effectiveness of our proposed framework, which establishes a new state-of-the-art accuracy on VQA-CP v2 with a 7.55% improvement.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 23 of its 28 references that are in the map. First posted on arXiv 8 Jun 2021.

Builds on

23 of 28 references.

References · 23

  1. 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,516
  2. 2015VQA: Visual Question Answeringcited by 2,506
  3. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,553
  4. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,312
  5. 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,296
  6. 2020Oscar: Object-Semantics Aligned Pre-training for Vision-Language Taskscited by 1,214
  7. 2017Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answeringcited by 339
  8. 2019Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biasescited by 280
  9. 2019Visual Entailment: A Novel Task for Fine-Grained Image Understandingcited by 252
  10. 2019RUBi: Reducing Unimodal Biases in Visual Question Answeringcited by 185
  11. 2015DenseCap: Fully Convolutional Localization Networks for Dense Captioningcited by 176
  12. 2020Counterfactual Samples Synthesizing for Robust Visual Question Answeringcited by 153
11 more
  1. 2019Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Groundedcited by 128
  2. 2016Analyzing the Behavior of Visual Question Answering Modelscited by 119
  3. 2015Yin and Yang: Balancing and Answering Binary Visual Questionscited by 109
  4. 2018Overcoming Language Priors in Visual Question Answering with Adversarial Regularizationcited by 105
  5. 2020MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answeringcited by 63
  6. 2019Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inferencecited by 49
  7. 2020Overcoming Language Priors with Self-supervised Learning for Visual Question Answeringcited by 43
  8. 2019Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effectscited by 25
  9. 2017Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answeringcited by 24
  10. 2019RankQA: Neural Question Answering with Answer Re-Rankingcited by 15
  11. 2018Joint Training of Candidate Extraction and Answer Selection for Reading Comprehensioncited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 4; 2024: 3; 2025: 0; 2026: 3.

18 papers.

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  1. 2022Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQAcited by 30
  2. 2023Robust Visual Question Answering: Datasets, Methods, and Future Challengescited by 23
  3. 2021Counterfactual Samples Synthesizing and Training for Robust Visual Question Answeringcited by 22
  4. 2024Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answeringcited by 18
  5. 2022Generative Bias for Robust Visual Question Answeringcited by 12
  6. 2022Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learningcited by 9
  7. 2021General Greedy De-bias Learningcited by 7
  8. 2021Language bias in Visual Question Answering: A Survey and Taxonomycited by 6
  9. 2023Understanding Social Media Cross-Modality Discourse in Linguistic Spacecited by 6
  10. 2024Multimodal Reranking for Knowledge-Intensive Visual Question Answeringcited by 6
  11. 2022Fine-Grained Visual Entailmentcited by 4
  12. 2023Object Attribute Matters in Visual Question Answeringcited by 2
6 more
  1. 2022Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question Answeringcited by 0
  2. 2023SC-ML: Self-supervised Counterfactual Metric Learning for Debiased Visual Question Answeringcited by 0
  3. 2024VEglue: Testing Visual Entailment Systems via Object-Aligned Joint Erasingcited by 0
  4. 2026Characterizing the Predictive Impact of Modalities with Supervised Latent-Variable Modelingcited by 0
  5. 2026Online Self-Calibration Against Hallucination in Vision-Language Modelscited by 0
  6. 2026When to Rethink: Learning Multi-Perspective Self-Verification for Vision-Language Modelscited by 0