Visual Question Answering is a semantic task that aims to answer questions based on an image. Source: [visualqa.org](https://visualqa.org/) 相关学科: Image CaptioningVisual ReasoningVisual DialogVisual GroundingRelational ReasoningQuestion AnsweringVisual Commonsense ReasoningReferring Expression ...
Some studies, such as [36, 48], have required human evaluation to assess the quality of predicted answers, which is impractical. Accuracy is the most widely used evaluation metric for both multiple-choice questions and open-ended questions in the case of classification based VQA models. Simple ...
We introduce the VizWiz-VQA- Grounding dataset, the first dataset that visually grounds answers to visual questions asked by people with visual im- pairments. We analyze our dataset and compare it with five VQA-Grounding datasets to demonstrate what makes it similar...
For free-form, open-ended questions, the joint feature representations are converted into answers usually using a recurrent network like LSTMs.Wu et al. (2016)extract data about the image to provide the language model with more context. They use the Doc2Vec algorithm to get embeddings, which ...
boththequestionsandanswersareopen-ended.Visualques- tionsselectivelytargetdifferentareasofanimage,including backgrounddetailsandunderlyingcontext.Asaresult,asys- temthatsucceedsatVQAtypicallyneedsamoredetailed understandingoftheimageandcomplexreasoningthana systemproducinggenericimagecaptions.Moreover,VQA ...
In contrast to these tasks, VQA on 360◦ images requires further inferring the answers accord- ing to questions, demanding more sophisticated reasoning of the scene. 3. VQA 360◦ Dataset We first present the proposed VQA 360◦ dataset to give a clear look at the task and its intrinsic...
and deep learning. These systems need to be trained for the task and evaluated on large data collections consisting of images and pairs of questions asked about the images with corresponding answers. Although there has been great progress in image recognition in radiology1, the datasets that allowe...
Recent Vision-Language Models (VLMs) have demonstrated remarkable capabilities in visual understanding and reasoning, and in particular on multiple-choice Visual Question Answering (VQA). Still, these models can make distinctly unnatural errors, for example, providing (wrong) answers to unanswerable VQA...
Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, ...
To address this question, we took canonical tasks from the domains of intuitive physics, causal reasoning and intuitive psychology that could be studied by providing images and language-based questions. We submitted them to some of the currently most advanced LLMs. To evaluate whether the LLMs sh...