mlsanityData to 14 Oct 2026

A Transformer-based Cross-modal Fusion Model with Adversarial Training for VQA Challenge 2021

arXiv 2106.13033 ↗First posted 24 Jun 2021cs.CV cs.CLcited by 1

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Abstract (arXiv)

In this paper, inspired by the successes of visionlanguage pre-trained models and the benefits from training with adversarial attacks, we present a novel transformerbased cross-modal fusion modeling by incorporating the both notions for VQA challenge 2021. Specifically, the proposed model is on top of the architecture of VinVL model [19], and the adversarial training strategy [4] is applied to make the model robust and generalized. Moreover, two implementation tricks are also used in our system to obtain better results. The experiments demonstrate that the novel framework can achieve 76.72% on VQAv2 test-std set.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 21 references that are in the map. First posted on arXiv 24 Jun 2021.

Builds on

16 of 21 references.

References · 16

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  11. 2019Unified Vision-Language Pre-Training for Image Captioning and VQAcited by 494
  12. 2019Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-trainingcited by 451
4 more
  1. 2020Large-Scale Adversarial Training for Vision-and-Language Representation Learningcited by 327
  2. 201912-in-1: Multi-Task Vision and Language Representation Learningcited by 278
  3. 2020ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graphcited by 211
  4. 2021VinVL: Revisiting Visual Representations in Vision-Language Modelscited by 110

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  1. 2023A Survey on Image-text Multimodal Modelscited by 4