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 ...
contentsandquestions.AkeychallengeinVQAistorequirejointreasoningoverthevisualandtextdomains.Thepre-dominantCNN/LSTM-basedapproachtoVQAislimitedbymonolithicvectorrepresentationsthatlargelyignorestruc-tureinthesceneandinthequestion.CNNfeaturevectorscannoteffectivelycapturesituationsassimpleasmultipleobjectinstances,andLSTMs...
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...
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...
boththequestionsandanswersareopen-ended.Visualques-tionsselectivelytargetdifferentareasofanimage,includingbackgrounddetailsandunderlyingcontext.Asaresult,asys-temthatsucceedsatVQAtypicallyneedsamoredetailedunderstandingoftheimageandcomplexreasoningthanasystemproducinggenericimagecaptions.Moreover,VQACommunicatedbyMargaret...
A novel task namedVideoTextVisualQuestionAnswering(ViteVQAin short), which aims at answering questions by jointly reasoning textual and visual information in a given video. The first ViteVQA benchmark dataset, which is namedMulti-categoryMulti-frameMulti-resolutionMulti-modalbenchmark for ViteVQA (M4...
python model_vqa.py \ --model-path ./checkpoints/LLaVA-13B-v0 \ --question-file \ playground/data/coco2014_val_qa_eval/qa90_questions.jsonl \ --image-folder \ /path/to/coco2014_val \ --answers-file \ /path/to/answer-file-our.jsonl Evaluate the generated responses. In our case...
Specifically, a posterior distribution over visual objects is inferred from both context (history and questions) and answers, and it ensures the appropriate grounding of visual objects during the training process. Meanwhile, a prior distribution, which is inferred from context only, is used to ...
Visual Programming: Compositional visual reasoning without training Tanmay Gupta, Aniruddha Kembhavi PRIOR @ Allen Institute for AI https://prior.allenai.org/projects/visprog Visual Programming Visual Prediction Rationale Compositional Visual Question Answering IMAGE: Question: Are...