# Seeing the Unseen: Camouflaged Object Detection Beyond the Visible Spectrum

arXiv: https://arxiv.org/abs/2608.30355 · HTML: https://mlsanity.com/paper/2608.30355/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 19 of 19)

- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [Anabranch Network for Camouflaged Object Segmentation](https://mlsanity.com/paper/2105.09451/) (2021)
- [Concealed Object Detection](https://mlsanity.com/paper/2102.10274/) (2021)
- [Simultaneously Localize, Segment and Rank the Camouflaged Objects](https://mlsanity.com/paper/2103.04011/) (2021)
- [Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection](https://mlsanity.com/paper/2203.02688/) (2022)
- [Context-aware Cross-level Fusion Network for Camouflaged Object Detection](https://mlsanity.com/paper/2105.12555/) (2021)
- [Weakly-Supervised Camouflaged Object Detection with Scribble Annotations](https://mlsanity.com/paper/2207.14083/) (2022)
- [Camouflaged Object Detection via Context-aware Cross-level Fusion](https://mlsanity.com/paper/2207.13362/) (2022)
- [Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection](https://mlsanity.com/paper/2408.15020/) (2024)
- [DMSSN: Distilled Mixed Spectral-Spatial Network for Hyperspectral Salient Object Detection](https://mlsanity.com/paper/2404.00694/) (2024)
- [Nested Unfolding Network for Real-World Concealed Object Segmentation](https://mlsanity.com/paper/2511.18164/) (2025)
- [PlantCamo: Plant Camouflage Detection](https://mlsanity.com/paper/2410.17598/) (2024)
- [SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model](https://mlsanity.com/paper/2507.20186/) (2025)
- [MCOD: The First Challenging Benchmark for Multispectral Camouflaged Object Detection](https://mlsanity.com/paper/2509.15753/) (2025)
- [HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone](https://mlsanity.com/paper/2605.17286/) (2026)
- [Evaluating an Adaptive Multispectral Turret System for Autonomous Tracking Across Variable Illumination Conditions](https://mlsanity.com/paper/2512.22263/) (2025)
- [HyperCOD: The First Challenging Benchmark and Baseline for Hyperspectral Camouflaged Object Detection](https://mlsanity.com/paper/2601.03736/) (2026)

## Abstract (arXiv)

Recent advances in camouflaged object detection (COD) have led to substantial progress in challenging low-visibility scenarios, with pioneering studies demonstrating notable success in localizing objects in camouflaged scenes. Despite these achievements, existing approaches predominantly rely on conventional three-channel RGB imagery, thereby constraining the available visual information to a limited spectral range. Multispectral images offer a wide range of information about a scene by capturing fine-grained spectral signatures. Hence, by leveraging multispectral images for COD, we introduce a novel approach to detect camouflaged objects from the corresponding multispectral inputs. In particular, we propose an end-to-end framework, textbftextitMSFormer, that takes a multispectral camouflaged image as input and predicts a binary mask for it. Additionally, we also provide empirical justification for integrating multispectral bands for this complex low-vision task. Our extensive experiments demonstrate the effectiveness of our method, which outperforms existing methods.