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Nethra Gunti

dblp:306/1433 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Representation and self-supervised learning · 100%
Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%
Network and information security
1 paper
Web and mobile security · 100%

Topics — the 3 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Representation and self-supervised learning › representation learning › embedding learning
joint embedding
0.612022
Memotion Analysis through the Lens of Joint Embedding (Student Abstract) · AAAI 2022
Machine learning › Representation and self-supervised learning › multimodal representation learning
multimodal embedding
0.612022
Memotion Analysis through the Lens of Joint Embedding (Student Abstract) · AAAI 2022
Multimedia analysis and retrieval › social media analysis
meme analysis
0.612022
Memotion Analysis through the Lens of Joint Embedding (Student Abstract) · AAAI 2022

Methods — techniques the papers use, named apart from their topics

joint embedding · 1.7
YearPublicationVenuePosition
2022 Memotion Analysis through the Lens of Joint Embedding (Student Abstract)
abstract
Joint embedding (JE) is a way to encode multi-modal data into a vector space where text remains as the grounding key and other modalities like image are to be anchored with such keys. Meme is typically an image with embedded text onto it. Although, memes are commonly used for fun, they could also be used to spread hate and fake information. That along with its growing ubiquity over several social platforms has caused automatic analysis of memes to become a widespread topic of research. In this paper, we report our initial experiments on Memotion Analysis problem through joint embeddings. Results are marginally yielding SOTA.
Nethra Gunti, Sathyanarayanan Ramamoorthy, Parth Patwa, Amitava Das 0001
AAAI1