Demo Data from EDBT 2026 / reviewers in the wild / expert
Efrat Blaier
dblp:302/4487
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia analysis and retrieval › harmful content detection › hate speech detection
hateful meme detection |
0.5 | 1 | 2021 | Caption Enriched Samples for Improving Hateful Memes Detection · EMNLP (1) 2021 |
Multimedia analysis and retrieval › multimodal learning
multimodal classification |
0.5 | 1 | 2021 | Caption Enriched Samples for Improving Hateful Memes Detection · EMNLP (1) 2021 |
Computer vision › Vision and language
caption generation |
0.1 | 1 | 2021 | Caption Enriched Samples for Improving Hateful Memes Detection · EMNLP (1) 2021 |
Computer vision › Vision and language
image captioning |
0.1 | 1 | 2021 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Caption Enriched Samples for Improving Hateful Memes DetectionabstractThe 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 |