Pith. sign in

REVIEW 4 cited by

Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection

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 2304.04709 v2 pith:5L3RA5A2 submitted 2023-04-10 cs.CV

Lv Tang , Haoke Xiao , Bo Li This is my paper
classification cs.CV
keywords objectperformancesegmentationcamouflagedtaskaddressdetectionevaluation
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

SAM is a segmentation model recently released by Meta AI Research and has been gaining attention quickly due to its impressive performance in generic object segmentation. However, its ability to generalize to specific scenes such as camouflaged scenes is still unknown. Camouflaged object detection (COD) involves identifying objects that are seamlessly integrated into their surroundings and has numerous practical applications in fields such as medicine, art, and agriculture. In this study, we try to ask if SAM can address the COD task and evaluate the performance of SAM on the COD benchmark by employing maximum segmentation evaluation and camouflage location evaluation. We also compare SAM's performance with 22 state-of-the-art COD methods. Our results indicate that while SAM shows promise in generic object segmentation, its performance on the COD task is limited. This presents an opportunity for further research to explore how to build a stronger SAM that may address the COD task. The results of this paper are provided in \url{https://github.com/luckybird1994/SAMCOD}.

Discussion (0). Sign in to comment.

Forward citations

Cited by 4 Pith papers

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

  1. ST-SAM: SAM-Driven Self-Training Framework for Semi-Supervised Camouflaged Object Detection

    cs.CV 2025-07 conditional novelty 6.0 of 10

    ST-SAM combines self-training with entropy-based pseudo-label filtering and SAM prompt-based mutual correction to achieve strong camouflaged object detection with only 1 percent labeled data.

  2. LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment

    cs.CV 2025-07 conditional novelty 6.0 of 10

    LoD-Loc v2 localizes aerial cameras by aligning predicted building silhouettes with rendered low-detail city-model silhouettes, achieving accurate 4-DoF pose without textured maps.

  3. SAM4D: Segment Anything in Camera and LiDAR Streams

    cs.CV 2025-06 conditional novelty 6.0 of 10

    SAM4D is a promptable model that segments and tracks objects across camera and LiDAR streams with cross-modal prompts, trained on pseudo-labels generated by an automated data engine.

  4. Segment Concealed Objects with Incomplete Supervision

    cs.CV 2025-06 conditional novelty 6.0 of 10

    SEE is a unified mean-teacher framework that derives SAM prompts from coarse teacher masks to generate pseudo-labels, and reports state-of-the-art results for weakly and semi-supervised concealed object segmentation.

Pith tools