Pith. sign in

REVIEW 1 cited by

Can SAM Count Anything? An Empirical Study on SAM Counting

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2304.10817 v1 pith:L5DT2XKQ submitted 2023-04-21 cs.CV cs.AI

Zhiheng Ma , Xiaopeng Hong , Qinnan Shangguan This is my paper
classification cs.CVcs.AI
keywords countinganythingfew-shotobjectsperformanceattentionboundingboxes
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Meta AI recently released the Segment Anything model (SAM), which has garnered attention due to its impressive performance in class-agnostic segmenting. In this study, we explore the use of SAM for the challenging task of few-shot object counting, which involves counting objects of an unseen category by providing a few bounding boxes of examples. We compare SAM's performance with other few-shot counting methods and find that it is currently unsatisfactory without further fine-tuning, particularly for small and crowded objects. Code can be found at \url{https://github.com/Vision-Intelligence-and-Robots-Group/count-anything}.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CountZES: Counting via Zero-Shot Exemplar Selection

    cs.CV 2025-12 conditional novelty 6.0 of 10

    A training-free, three-stage exemplar-selection pipeline improves zero-shot object counting across natural, aerial, and medical images when compared with other inference-only methods.

Pith tools