A framework uses modality-agnostic prompts to adapt SAM for multi-modal camouflaged object detection, with a mask refine module for better boundaries.
Plantcamo: Plant camouflage detection
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Camo-M3FD is a curated visible-thermal benchmark dataset for cross-spectral camouflaged pedestrian detection, with annotations and baseline evaluations showing the value of fusion.
SWNet combines visible and NIR spectra with a Pyramid Vision Transformer, bimodal gated fusion, and edge refinement to outperform prior methods on camouflaged weed detection in the Weeds-Banana dataset.
citing papers explorer
-
Modality-Agnostic Prompt Learning for Multi-Modal Camouflaged Object Detection
A framework uses modality-agnostic prompts to adapt SAM for multi-modal camouflaged object detection, with a mask refine module for better boundaries.
-
Camo-M3FD: A New Benchmark Dataset for Cross-Spectral Camouflaged Pedestrian Detection
Camo-M3FD is a curated visible-thermal benchmark dataset for cross-spectral camouflaged pedestrian detection, with annotations and baseline evaluations showing the value of fusion.
-
SWNet: A Cross-Spectral Network for Camouflaged Weed Detection
SWNet combines visible and NIR spectra with a Pyramid Vision Transformer, bimodal gated fusion, and edge refinement to outperform prior methods on camouflaged weed detection in the Weeds-Banana dataset.