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You searched for subject:(Visual Dialog). Showing records 1 – 2 of 2 total matches.

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Georgia Tech

1. Chattopadhyay, Prithvijit. Evaluating visual conversational agents via cooperative human-AI games.

Degree: MS, Computer Science, 2019, Georgia Tech

As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but also in terms of how it translates to helping humans perform certain tasks, i.e., the performance of human-AI teams. This thesis introduces a cooperative game – GuessWhich – to measure human-AI team performance in the specific context of the AI being a visual conversational agent. GuessWhich involves live interaction between the human and the AI. The AI, which we call Alice, is provided an image which is unseen by the human. Following a brief description of the image, the human questions Alice about this secret image to identify it from a fixed pool of images. We measure performance of the human-Alice team by the number of guesses it takes the human to correctly identify the secret image after a fixed number of dialog rounds with Alice. We compare performance of the human-Alice teams for two versions of Alice. Our human studies suggest a counter-intuitive trend – that while AI literature shows that one version outperforms the other when paired with an AI questioner bot, we find that this improvement in AI-AI performance does not translate to improved human-AI performance. As this implies a mismatch between benchmarking of AI in isolation and in the context of human-AI teams, this thesis further motivates the need to evaluate AI additionally in the latter setting to effectively leverage the progress in AI for efficient human-AI teams. Advisors/Committee Members: Parikh, Devi (advisor), Batra, Dhruv (advisor), Lee, Stefan (advisor).

Subjects/Keywords: Visual conversational agents; Visual dialog; Human-AI teams; Reinforcement learning; Machine learning; Computer vision; Artificial intelligence

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chattopadhyay, P. (2019). Evaluating visual conversational agents via cooperative human-AI games. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61308

Chicago Manual of Style (16th Edition):

Chattopadhyay, Prithvijit. “Evaluating visual conversational agents via cooperative human-AI games.” 2019. Masters Thesis, Georgia Tech. Accessed August 25, 2019. http://hdl.handle.net/1853/61308.

MLA Handbook (7th Edition):

Chattopadhyay, Prithvijit. “Evaluating visual conversational agents via cooperative human-AI games.” 2019. Web. 25 Aug 2019.

Vancouver:

Chattopadhyay P. Evaluating visual conversational agents via cooperative human-AI games. [Internet] [Masters thesis]. Georgia Tech; 2019. [cited 2019 Aug 25]. Available from: http://hdl.handle.net/1853/61308.

Council of Science Editors:

Chattopadhyay P. Evaluating visual conversational agents via cooperative human-AI games. [Masters Thesis]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61308

2. Jain, Unnat. Visual questioning agents.

Degree: MS, Computer Science, 2018, University of Illinois – Urbana-Champaign

Curious questioning or the ability to inquire about surrounding environment or additional context, is an important step towards building agents which go beyond learning from a static knowledge base. The ability to request feedback is the first step in building intelligent agents which can incorporate this feedback to enhance learning. Visual questioning tasks help model this human skill of “curiosity.” In this thesis, we focus on two relevant vision based questioning tasks – visual question generation and visual dialog. We propose novel approaches and evaluation metrics for these tasks. For visual question generation, we combined language models with variational autoencoders to enhance diversity in text generations. We also suggest diversity metrics to quantify these improvements. For visual dialog, we introduce a reformulated dataset to enable training of questioning agents in a dialog setup. We also introduce simpler and more effective baselines for the task. Our combined results in visual question generation and visual dialog contribute to establishing visual questioning as an important next step for computer vision, and more generally, for artificial intelligence. Advisors/Committee Members: Lazebnik, Svetlana (advisor), Schwing, Alexander Gerhard (advisor).

Subjects/Keywords: Visual Question Generation; Visual Dialog; Variational Autoencoders; Language and Vision; Computer Vision

…and experimentation in more details. 1.2 VISUAL DIALOG Second, we broaden our visual… …This is curated from the visual dialog dataset [11] containing short dialogues… …about a scene between two people. We propose an alternate take on visual dialog. We argue that… …history of question-answer pairs is equally 2 Figure 1.2: Visual dialog as a combination of… …the visual dialog dataset to facilitate training of questioning agents for visual dialog. We… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jain, U. (2018). Visual questioning agents. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/101574

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Jain, Unnat. “Visual questioning agents.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed August 25, 2019. http://hdl.handle.net/2142/101574.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Jain, Unnat. “Visual questioning agents.” 2018. Web. 25 Aug 2019.

Vancouver:

Jain U. Visual questioning agents. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Aug 25]. Available from: http://hdl.handle.net/2142/101574.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jain U. Visual questioning agents. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/101574

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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