Development of Naïve Algorithm for Generation of Artificial Intelligence based System for Conversing with a Human
Abstract
We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. 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, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ∼0.25M images, ∼0.76M questions, and ∼10M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines and methods for VQA are provided and compared with human performance.”
Downloads
References
H. Agrawal, C. S. Mathialagan, Y. Goyal, N. Chavali, P. Banik, A. Mohapatra, A. Osman, and D. Batra. Cloudcv: Large-scale distributed computer vision as a cloud service. In Mobile Cloud Visual Media Computing, pages 265–290. Springer International Publishing, 2015.
S. Antol, C. L. Zitnick, and D. Parikh. Zero-Shot Learning via Visual Abstraction. In ECCV, 2014.
J. P. Bigham, C. Jayant, H. Ji, G. Little, A. Miller, R. C. Miller, R. Miller, A. Tatarowicz, B. White, S. White, and T. Yeh. VizWiz: Nearly Realtime Answers to Visual Questions. In User Interface Software and Technology, 2010.
K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. Freebase: A Collaboratively Created Graph Database for Structuring Human Knowledge. In International Conference on Management of Data, 2008.
A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. R. H. Jr., and T. M. Mitchell. Toward an Architecture for Never-Ending Language Learning. In AAAI, 2010. 2
X. Chen, H. Fang, T. Lin, R. Vedantam, S. Gupta, P. Dolla´r, and C. L. Zitnick. Microsoft COCO captions: Data collection and evaluation server. CoRR, abs/1504.00325, 2015.
X. Chen, H. Fang, T.-Y. Lin, R. Vedantam, S. Gupta, P. Dolla´r, and C. L. Zitnick. Microsoft COCO Captions: Data Collection and Evaluation Server. arXiv preprint arXiv:1504.00325, 2015.
X. Chen, A. Shrivastava, and A. Gupta. NEIL: Extracting Visual Knowledge from Web Data. In ICCV, 2013.
X. Chen and C. L. Zitnick. Mind’s Eye: A Recurrent Visual Representation for Image Caption Generation. In CVPR, 2015.
G. Coppersmith and E. Kelly. Dynamic wordclouds and vennclouds for exploratory data analysis. In ACL Workshop on Interactive Language Learning and Visualization, 2014.
J. Deng, A. C. Berg, and L. Fei-Fei. Hierarchical Semantic Indexing for Large Scale Image Retrieval. In CVPR, 2011.
J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell. Long-term Recurrent Convolutional Networks for Visual Recognition and Description. In CVPR, 2015.
D. Elliott and F. Keller. Comparing Automatic Evaluation Measures for Image Description. In ACL, 2014.
A. Fader, L. Zettlemoyer, and O. Etzioni. Paraphrase-Driven Learning for Open Question Answering. In ACL, 2013.
A. Fader, L. Zettlemoyer, and O. Etzioni. Open Question Answering over Curated and Extracted Knowledge Bases. In International Conference on Knowledge Discovery and Data Mining, 2014.
H. Fang, S. Gupta, F. N. Iandola, R. Srivastava, L. Deng, P. Dolla´r, J. Gao, X. He, M. Mitchell, J. C. Platt, C. L. Zitnick, and G. Zweig. From Captions to Visual Concepts and Back. In CVPR, 2015.
A. Farhadi, M. Hejrati, A. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth. Every Picture Tells a Story: Generating Sentences for Images. In ECCV, 2010.
H. Gao, J. Mao, J. Zhou, Z. Huang, and A. Yuille. Are you talking to a machine? dataset and methods for multilingual image question answering. In NIPS, 2015.
D. Geman, S. Geman, N. Hallonquist, and L. Younes. A Visual Turing Test for Computer Vision Systems. In PNAS, 2014.
J. Gordon and B. V. Durme. Reporting bias and knowledge extraction. In Proceedings of the 3rd Workshop on Knowledge Extraction, at CIKM 2013, 2013
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
