Don’t Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
虽然以前的VQA直接将Image-Question元组（I，Q）映射到应答（A），但GVQA将VQA的任务分为两步：LOOK：找到回答问题所需的对象/图像块，并识别块中的视觉概念；从问题中找出合理答案的空间，并通过考虑哪些概念是合理的，从一组公认的视觉概念中返回适当的视觉概念。 GVQA的另一个新颖之处是它把回答“是”/“否”作为一项直观的验证任务。 给定一个问题和一个图像，问题首先通过问题分类器
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相关文章
- 1. Don’t Just Assume； Look and Answer：Overcoming Priors for Visual Question Answering阅读笔记
- 2. nips 208 visual question answering 导读
- 3. 【论文笔记-AAAI2020】Overcoming Language Priors in VQA via Decomposed Linguistic Representations
- 4. Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
- 5. Learning Conditioned Graph Structures for Interpretable Visual Question Answering
- 6. Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
- 7. 阅读笔记（Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding)
- 8. 《Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering》
- 9. (Paper Reading)Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
- 10. CVPR 2018 Oral：Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering