RQ1: What is the current state-of-the-art in NQG research? RQ2: How can existing NQG methods be customized to address educational needs? RQ3: What are the gaps in NQG research for educational purposes, and how can future research bridge these gaps? The remainder of this review is str...
Gao et al. (2018)realized that a lot of spatial information is lost when we take only the one-dimensional vector representation from the 2nd to last layer of some convolutional networks, like ResNet. To solve for this, they use what they call “question guided hybrid convolutions”, where ...
Because extracted bounding-boxes are often located around things (countable objects), information on stuff (amorphous background regions such as grass and sky) is not reflected well in the visual encoder. Because stuff is amorphous and uncountable, it is common to use semantic segmentation to ...
is quite naturally related to image captioning in that text has to be produced based on an image. Important differences include the fact that the produced text needs to answer the question rather than describe what is visible in the image. Also, additional text data is present in VQA in the...
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation - Cold-Winter/vqs
This is the official PyTorch implementation of our paper: QA-CLIMS: Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation Songhe Deng, Wei Zhuo, Jinheng Xie, Linlin Shen Computer Vision Institute, Shenzhen University ACM International Conference on Multimedia, 2023 ...
Images Shih-Han Chou1,2, Wei-Lun Chao3, Wei-Sheng Lai5, Min Sun2, Ming-Hsuan Yang4,5 1University of British Columbia 2National Tsing Hua University 3The Ohio State University 4University of California at Merced 5Google "Scene" question example: Q: What room is depicted in the image?
", "is_impossible": "false", "id": "56be4db0acb8001400a502ee", "answers": [ { "answer_start": "403", "text": "Santa Clara, California" } ] }, { "question": "What was the winning score of the Super Bowl 50?", "is_impossible": "true", "id": "56be4db0acb8001400a...
What is QAnything?QAnything(Question and Answer based on Anything) is a local knowledge base question-answering system designed to support a wide range of file formats and databases, allowing for offline installation and use.With QAnything, you can simply drop any locally stored file of any ...
Specifically, on top of the grid features from the ResNet-50 model, we add a Pyramid Pool- ing Module (PPM, a component widely used for semantic segmentation; details in supplementary material) [52, 43] to aggregate visual information from grid features of differ- ent ...