4 papers
RECOUNT: Reference-guided Counting with Synthetic Visual Exemplars
Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Text-guided zero-shot object counters excel at spatial localization but categorize poorly on novel or fine-grained classes: natural language is too coarse to fully specify visual i…
FiGO: Fine-Grained Object Counting without Annotations
Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Class-agnostic counting (CAC) methods reduce annotation costs by letting users define what to count at test-time through text or visual exemplars. However, current open-vocabulary…
AFreeCA: Annotation-Free Counting for All
Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Object counting methods typically rely on manually annotated datasets. The cost of creating such datasets has restricted the versatility of these networks to count objects from spe…
SYRAC: Synthesize, Rank, and Count
Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Crowd counting is a critical task in computer vision, with several important applications. However, existing counting methods rely on labor-intensive density map annotations, neces…