mlsanityData to 7 Oct 2026

Exploring Diverse Methods in Visual Question Answering

arXiv 2404.13565 ↗First posted 21 Apr 2024cs.CV cs.AI cs.CLcited by 45

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Abstract (arXiv)

This study explores innovative methods for improving Visual Question Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms. Leveraging a balanced VQA dataset, we investigate three distinct strategies. Firstly, GAN-based approaches aim to generate answer embeddings conditioned on image and question inputs, showing potential but struggling with more complex tasks. Secondly, autoencoder-based techniques focus on learning optimal embeddings for questions and images, achieving comparable results with GAN due to better ability on complex questions. Lastly, attention mechanisms, incorporating Multimodal Compact Bilinear pooling (MCB), address language priors and attention modeling, albeit with a complexity-performance trade-off. This study underscores the challenges and opportunities in VQA and suggests avenues for future research, including alternative GAN formulations and attentional mechanisms.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). Builds on 28 of its 57 references that are in the map. First posted on arXiv 21 Apr 2024.

Builds on

28 of 57 references.

References · 28

  1. 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,498
  2. 2015VQA: Visual Question Answeringcited by 2,489
  3. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  4. 2016Generative Adversarial Text to Image Synthesiscited by 735
  5. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 460
  6. 2016Hierarchical Question-Image Co-Attention for Visual Question Answeringcited by 278
  7. 2016Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Groundingcited by 243
  8. 2023NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Spacecited by 88
  9. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  10. 2023PLGSLAM: Progressive Neural Scene Represenation with Local to Global Bundle Adjustmentcited by 62
  11. 2023Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networkscited by 54
  12. 2024Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an examplecited by 50
16 more
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  2. 2018Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagerycited by 42
  3. 2023MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learningcited by 41
  4. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
  5. 2023CPSeg: Finer-grained Image Semantic Segmentation via Chain-of-Thought Language Promptingcited by 28
  6. 2024Adapting LLMs for Efficient Context Processing through Soft Prompt Compressioncited by 22
  7. 2024TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detectioncited by 21
  8. 2019Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systemscited by 20
  9. 2024Few-shot Name Entity Recognition on StackOverflowcited by 14
  10. 2018Vehicle Pose and Shape Estimation through Multiple Monocular Visioncited by 13
  11. 2023ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMscited by 13
  12. 2023Building Lane-Level Maps from Aerial Imagescited by 12
  13. 2023Influence Pathway Discovery on Social Mediacited by 11
  14. 2023Segment Any Buildingcited by 8
  15. 2016DualNet: Domain-Invariant Network for Visual Question Answeringcited by 4
  16. 2019Joint Coverage and Power Control in Highly Dynamic and Massive UAV Networks: An Aggregative Game-theoretic Learning Approachcited by 4

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Citing papers per year: 2024: 32; 2025: 13.

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