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Efrat Blaier

dblp:302/4487 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2021
—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 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.

Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%
Artificial intelligence
1 paper
Vision and language · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Multimedia analysis and retrieval › harmful content detection › hate speech detection
hateful meme detection
0.512021
Caption Enriched Samples for Improving Hateful Memes Detection · EMNLP (1) 2021
Multimedia analysis and retrieval › multimodal learning
multimodal classification
0.512021
Caption Enriched Samples for Improving Hateful Memes Detection · EMNLP (1) 2021
Computer vision › Vision and language
caption generation
0.112021
Caption Enriched Samples for Improving Hateful Memes Detection · EMNLP (1) 2021
Computer vision › Vision and language
image captioning
0.112021
Caption Enriched Samples for Improving Hateful Memes Detection · EMNLP (1) 2021

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

multimodal fine-tuning · 1.0language model pretraining · 1.0image captioning · 1.0
YearPublicationVenuePosition
2021 Caption Enriched Samples for Improving Hateful Memes Detection
abstract
The recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not.Specifically, both unimodal language models and multimodal vision-language models cannot reach the human level of performance.Motivated by the need to model the contrast between the image content and the overlayed text, we suggest applying an off-the-shelf image captioning tool in order to capture the first.We demonstrate that the incorporation of such automatic captions during fine-tuning improves the results for various unimodal and multimodal models.Moreover, in the unimodal case, continuing the pre-training of language models on augmented and original caption pairs, is highly beneficial to the classification accuracy.Our code is publicly available 1 .
Efrat Blaier, Itzik Malkiel, Lior Wolf
EMNLP (1)1