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13 results on '"Agnihotri, Manish"'

1. Countering Inconsistent Labelling by Google's Vision API for Rotated Images

Author

Apte, Aman, Bandyopadhyay, Aritra, Shenoy, K Akhilesh, Andrews, Jason Peter, Rathod, Aditya, Agnihotri, Manish, and Jajodia, Aditya

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Google's Vision API analyses images and provides a variety of output predictions, one such type is context-based labelling. In this paper, it is shown that adversarial examples that cause incorrect label prediction and spoofing can be generated by rotating the images. Due to the black-boxed nature of the API, a modular context-based pre-processing pipeline is proposed consisting of a Res-Net50 model, that predicts the angle by which the image must be rotated to correct its orientation. The pipeline successfully performs the correction whilst maintaining the image's resolution and feeds it to the API which generates labels similar to the original correctly oriented image and using a Percentage Error metric, the performance of the corrected images as compared to its rotated counter-parts is found to be significantly higher. These observations imply that the API can benefit from such a pre-processing pipeline to increase robustness to rotational perturbances., Comment: 11 pages, 9 figures, Accepted at ICICV 2020 Jaipur India

Published
2019

2. Common Artist Music Assistance

Author

Agnihotri, Manish, Rathod, Adiyta, Jajodia, Aditya, and Sharma, Chethan

Subjects
Computer Science - Information Retrieval
Abstract

In today's world of growing number of songs, the need of finding apposite music content according to a user's interest is crucial. Furthermore, recommendations suitable to one user may be irrelevant to another. In this paper, we propose a recommendation system for users with common-artist music listening patterns. We use "random walk with restart" algorithm to get relevant recommendations and conduct experiments to find the optimal values of multiple parameters., Comment: 8 pages, 11 figures, ICCSE 2018 Malaysia

Published
2019

3. Countering Inconsistent Labelling by Google’s Vision API for Rotated Images

Author

Apte, Aman, Bandyopadhyay, Aritra, Shenoy, K. Akhilesh, Andrews, Jason Peter, Rathod, Aditya, Agnihotri, Manish, Jajodia, Aditya, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Manoj Kumar, editor, Dhaka, Vijaypal Singh, editor, Perumal, Thinagaran, editor, Dey, Nilanjan, editor, and Tavares, João Manuel R. S., editor

Published
2021
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4. Towards Generating Topic-Driven and Affective Responses to Assist Mental Wellness

Author

Agnihotri, Manish, Pooja Rao, S. B., Jayagopi, Dinesh Babu, Hebbar, Sushranth, Rasipuram, Sowmya, Maitra, Anutosh, Sengupta, Shubhashis, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Del Bimbo, Alberto, editor, Cucchiara, Rita, editor, Sclaroff, Stan, editor, Farinella, Giovanni Maria, editor, Mei, Tao, editor, Bertini, Marco, editor, Escalante, Hugo Jair, editor, and Vezzani, Roberto, editor

Published
2021
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9. Improving Asynchronous Interview Interaction with Follow-up Question Generation

Author

Rao S B, Pooja, Agnihotri, Manish, Babu Jayagopi, Dinesh, Rao S B, Pooja, Agnihotri, Manish, and Babu Jayagopi, Dinesh

Abstract

The user experience of an asynchronous video interview system, conventionally is not reciprocal or conversational. Interview applicants expect that, like a typical face-to-face interview, they are innate and coherent. We posit that the planned adoption of limited probing through follow-up questions is an important step towards improving the interaction. We propose a follow-up question generation model (followQG) capable of generating relevant and diverse follow-up questions based on the previously asked questions, and their answers. We implement a 3D virtual interviewing system, Maya, with capability of follow-up question generation. Existing asynchronous interviewing systems are not dynamic with scripted and repetitive questions. In comparison, Maya responds with relevant follow-up questions, a largely unexplored feature of irtual interview systems. We take advantage of the implicit knowledge from deep pre-trained language models to generate rich and varied natural language follow-up questions. Empirical results suggest that followQG generates questions that humans rate as high quality, achieving 77% relevance. A comparison with strong baselines of neural network and rule-based systems show that it produces better quality questions. The corpus used for fine-tuning is made publicly available.

Published
2021

13. Improving Asynchronous Interview Interaction with Follow-up Question Generation.

Author

S. B., Pooja Rao, Agnihotri, Manish, and Jayagopi, Dinesh Babu

Subjects
NATURAL languages, USER experience, VIRTUAL communities, MOTIVATIONAL interviewing
Abstract

The user experience of an asynchronous video interview system, conventionally is not reciprocal or conversational. Interview applicants expect that, like a typical face-to-face interview, they are innate and coherent. We posit that the planned adoption of limited probing through follow-up questions is an important step towards improving the interaction. We propose a follow-up question generation model (followQG) capable of generating relevant and diverse follow-up questions based on the previously asked questions, and their answers. We implement a 3D virtual interviewing system, Maya, with capability of follow-up question generation. Existing asynchronous interviewing systems are not dynamic with scripted and repetitive questions. In comparison, Maya responds with relevant follow-up questions, a largely unexplored feature of virtual interview systems. We take advantage of the implicit knowledge from deep pre-trained language models to generate rich and varied natural language follow-up questions. Empirical results suggest that followQG generates questions that humans rate as high quality, achieving 77% relevance. A comparison with strong baselines of neural network and rule-based systems show that it produces better quality questions. The corpus used for fine-tuning is made publicly available. [ABSTRACT FROM AUTHOR]

Published
2021
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