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Makarand Tapaswi on Muck Rack

Makarand Tapaswi

As seen in: The Hindu
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Articles

MALeR: Improving Compositional Fidelity in Layout-Guided Generation

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By Dhruv Srivastava, Makarand Tapaswi
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (695) Issue with article?

What You See is What You Ask: Evaluating Audio Descriptions

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By Eshika Khandelwal, Makarand Tapaswi
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (511) Issue with article?

Seeing Eye to AI: Comparing Human Gaze and Model Attention in Video Memorability

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By Makarand Tapaswi, Vishnu Sreekumar, Eshika Khandelwal
| arxiv.org
[Submitted on 26 Nov 2023 ( v1 ), last revised 5 Nov 2024 (this version, v2)] Title:Seeing Eye to AI: Comparing Human Gaze and Model Attention in Video Memorability View a PDF of the paper titled Seeing Eye to AI: Comparing Human Gaze and Model Attention in Video Memorability, by Prajneya Kumar and 3 other authors View PDF HTML (experimental) Abstract:Understanding what makes a video memorable has important applications in advertising or education technology.
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