[4]Damien Teney, Peter Anderson, Xiaodong He, Antovan den Hengel. Tips and Tricks for Visual Question Answering: Learning from the 2017 Challenge. In CVPR, 2018. [5]Mateusz Malinowski, Marcus Rohrbach, Mario Fritz. Ask Your Neurons: A Neural-based Approach to Answering Questions about Image...
We introduce the task of Image-Set Visual Question Answering (ISVQA), which generalizes the commonly studied single-image VQA problem to multi-image settings. Taking a natural language question and a set of images as input, it aims to answer the question based on the content of the images....
Abstract. This paper focuses on scene graph completion which aims at predicting new relations between two entities utilizing existing scene graphs and images. By comparing with the well-known knowledge graph, we first identify that each scene graph is associated with an image and each entity of a...
Visual Question Answering (VQA) is a task that answers questions on given images. Although previous works achieve a great improvement in VQA performance, t... S Park,S Hwang,J Hong,... - 《IEEE Access》 被引量: 0发表: 2020年 Surgical-VQA: Visual Question Answering in Surgical Scenes usi...
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We find that incorporating and reasoning aboutconsistency between images and captions significantly improves performance.Concretely, our model improves state-of-the-art on caption retrieval by 7.1%and on image retrieval by 4.4% on the MSCOCO dataset. 展开 关键词: Visual question answering Image-...
2. Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder poster:论文链接 过往的VQA模型有很大的language bias。 (language bias就有点像因为某些答案出现次数多,模型记住了问题的答案,根本不管图上显示的是什么,比如,问“香蕉啥颜色”,就回答“黄色”。这种现象严重影响了在...
Now imagine you’re a computer. You’re given that same image and the text ”what sport is depicted in this image?” and asked to produce the answer. Not so easy anymore, is it? This problem is known asVisual Question Answering (VQA): answering open-ended questions about images. VQA ...
- Scalability: VQA models need to handle a large number of images and questions efficiently to be practical for real-time applications. - Bias: VQA models can inherit biases from training data, leading to biased or unfair answers. 4. Approaches in Visual Question Answering: There are two prima...
There are a lot of datasets that address different kinds of tasks in the visual question answering domain. Some of the primary ones are discussed here. DAQUAR(Dataset for Question Answering on Real World Images) is a dataset of human question-answer pairs about images. ...