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 ...
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation - Cold-Winter/vqs
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 ...
ARMAN(Salemi et al.2021) is a pre-trained transformer-based encoder-decoder model. Important sentences are selected based on the modified semantic score of the document to form a summary of it. For a more accurate summary and similar to human writing patterns, the sentences have been reordered...
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 ...
Visual Generation is the task of generating visual output (image or video) from a given textual input. It often requires a sound understanding of the semantic information and accordingly generating relevant and context-rich coherent visual formations. ...
“what is printed above the number 30 on the player on the left’s shirt” in Fig.1. Extending the perception space of graph nodes is a good way to deal with questions that require complex location logic and it makes the model learn logical relationships between OCR tokens and objects. ...
What is a structured question? A structured question is a closed-ended inquiry employed in surveys to elicit quick and accurate responses while minimizing participant thought. These questions will lessen the researcher’s job candidates because the solutions are straightforward and easy to analyze. A...
To test the generalizability of the model, a modified VQAv2 dataset is prepared and evaluated. Through extensive experiments, the model demonstrates competitive performance, effectively handling diverse question types such as “what”, “where”, “who”, “why”, and “how”. A detailed analysis...
and answers were created by domain experts. However, it is worth noting that the questions in RACE primarily focus on assessing comprehension and English language skills, rather than requiring higher-order cognitive abilities. Additionally, RACE includes general-style questions such as "What would be...