Visual Question Answering (VQA) VQA v2 val BLIP-2 ViT-G FlanT5 XXL Visual Question Answering VQA v2 test-dev BLIP-2 ViT-G OPT 6.7B Visual Question Answering VQA v2 val BLIP-2 ViT-G OPT 6.7B Show all 7 benchmarks Papers Dataset Loaders ...
2.5 模型排名 在huggingface上,我们将视觉问答(visual-question-answering)模型按下载量从高到低排序,共计427个模型中,文中的ViLT模型排名第三。 三、总结 本文对transformers之pipeline的视觉问答(visual-question-answering)从概述、技术原理、pipeline参数、pipeline实战、模型排名等方面进行...
Since the release of the first VQA dataset in 2014, additional datasets have been released and many algorithms have been proposed. In this review, we critically examine the current state of VQA in terms of problem formulation, existing datasets, evaluation metrics, and algorithms. In particular, ...
[10] D. A. Hudson and C. D. Manning, “GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering,” in2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, Jun. 2019, pp. 6693–6702, doi: 10.1109/CVPR.2019.00686...
模型架构基于论文Hierarchical Question-Image Co-Attention for Visual Question Answering。 技术层面 应用程序中使用的模型是在VQA 2.0数据集上训练的,在该数据集上论文的准确率为 54%,在VQA-Flask-App中使用的模型准确率为 49.20%。 本地运行应用程序
Dual-Key,除了visual trigger外,还需要一个question trigger。 文中使用的question trigger非常简单:在question开头添加一个单词,如consider。 有了trigger后,就能得到Poisoned VQA Questions与Poisoned Image Features,将其与clean Dataset结合,获得Poisoned VQA Dataset。 一般来说,Poisoning Percentage(有毒样本在训练集中...
Visual question answering (VQA) is a challenging task that requires a computer system to understand both a question and an image. While there is much research on VQA in English, there is a lack of datasets for other languages, and English annotation is not directly applicable in those ...
DAQUAR(Dataset for Question Answering on Real World Images) is a dataset of human question-answer pairs about images. COCO-QAis an extension of the COCO (Common Objects in Context) dataset. The questions are of 4 different types: object, number, color, and location. All answers are of a ...
Structured Two-stream Attention Network for Video Question Answering - Lianli Gao et al,AAAI 2019. [code] Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering - Xiangpeng Li et al,AAAI 2019. [code] WK-VQA: World Knowledge-enabled Visual Question Answering - Sa...
- Attention Mechanisms: Attention mechanisms help models focus on relevant parts of the image or question, improving performance. - Adversarial Training: Adversarial trainingtechniques mitigate bias in VQA models by explicitly addressing dataset imbalances or biased annotations. - Explainable VQA: Researchers...