Few-shot adaptation with 50 target-domain samples enables VLMs finetuned on different DocVQA domains to match or surpass fully supervised counterparts, revealing visual understanding as the primary bottleneck.
Named-Entity Recognition in the Crime Domain (CrimeNER): Case Study and Dataset
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
The extraction of critical information from crime-related documents is a crucial task for law enforcement agencies. The extraction of this information can be interpreted as a Named-Entity Recognition (NER) task. However, there is a considerable lack of adequately annotated data on general real-world crime scenarios. To address this issue, we present CrimeNER, a case study of crime-related NER, and a general crime-related Named-Entity Recognition database (CrimeNER-db), consisting of more than 1.5K annotated documents extracted from public reports of terrorist attacks and the US Department of Justice's press notes. We define 4 coarse types of crime entity and 21 fine-grained entity types. We address the quality of the presented database with experiments using fully supervised finetuned general NER models and zero- and few-shot experiments to address the generalization capabilities. The database is available on GitHub.
fields
cs.CV 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering
Few-shot adaptation with 50 target-domain samples enables VLMs finetuned on different DocVQA domains to match or surpass fully supervised counterparts, revealing visual understanding as the primary bottleneck.