# SCALER: SAM-Enhanced Collaborative Learning for Label-Deficient Concealed Object Segmentation

arXiv: https://arxiv.org/abs/2511.18136 · HTML: https://mlsanity.com/paper/2511.18136/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 31 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Builds on (most cited first, 23 of 23)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications](https://mlsanity.com/paper/2304.05750/) (2023)
- [Weakly-Supervised Salient Object Detection via Scribble Annotations](https://mlsanity.com/paper/2003.07685/) (2020)
- [Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping](https://mlsanity.com/paper/2305.11003/) (2023)
- [SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More](https://mlsanity.com/paper/2408.04579/) (2024)
- [Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects](https://mlsanity.com/paper/2308.03166/) (2023)
- [Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence](https://mlsanity.com/paper/2012.04404/) (2020)
- [Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation](https://mlsanity.com/paper/2203.10739/) (2022)
- [Frequency-Spatial Entanglement Learning for Camouflaged Object Detection](https://mlsanity.com/paper/2409.01686/) (2024)
- [Weakly-Supervised Camouflaged Object Detection with Scribble Annotations](https://mlsanity.com/paper/2207.14083/) (2022)
- [Relax Image-Specific Prompt Requirement in SAM: A Single Generic Prompt for Segmenting Camouflaged Objects](https://mlsanity.com/paper/2312.07374/) (2023)
- [A Survey of Camouflaged Object Detection and Beyond](https://mlsanity.com/paper/2408.14562/) (2024)
- [HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance](https://mlsanity.com/paper/2307.07829/) (2023)
- [RUN: Reversible Unfolding Network for Concealed Object Segmentation](https://mlsanity.com/paper/2501.18783/) (2025)
- [Weakly-Supervised Salient Object Detection Using Point Supervision](https://mlsanity.com/paper/2203.11652/) (2022)
- [FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection](https://mlsanity.com/paper/2407.13133/) (2024)
- [CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection](https://mlsanity.com/paper/2408.08050/) (2024)
- [Concealed Object Segmentation with Hierarchical Coherence Modeling](https://mlsanity.com/paper/2401.11767/) (2024)
- [Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics](https://mlsanity.com/paper/2311.06654/) (2023)
- [Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers](https://mlsanity.com/paper/2211.11847/) (2022)
- [Segment Concealed Objects with Incomplete Supervision](https://mlsanity.com/paper/2506.08955/) (2025)

## Built on by (most cited first, 6 of 6)

- [Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion](https://mlsanity.com/paper/2602.01183/) (2026)
- [When Denoising Becomes Unsigning: Theoretical and Empirical Analysis of Watermark Fragility Under Diffusion-Based Image Editing](https://mlsanity.com/paper/2603.04696/) (2026)
- [Editing Away the Evidence: Diffusion-Based Image Manipulation and the Failure Modes of Robust Watermarking](https://mlsanity.com/paper/2603.12949/) (2026)
- [RIDE: Retinex-Informed Decoupling for Exposing Concealed Objects](https://mlsanity.com/paper/2605.15450/) (2026)
- [On the Controllability-Fidelity Frontier in Diffusion Editing](https://mlsanity.com/paper/2606.09901/) (2026)
- [Progressive Pseudo-Label Optimization for Point-Supervised Change Detection](https://mlsanity.com/paper/2609.02171/) (2026)

## Abstract (arXiv)

Existing methods for label-deficient concealed object segmentation (LDCOS) either rely on consistency constraints or Segment Anything Model (SAM)-based pseudo-labeling. However, their performance remains limited due to the intrinsic concealment of targets and the scarcity of annotations. This study investigates two key questions: (1) Can consistency constraints and SAM-based supervision be jointly integrated to better exploit complementary information and enhance the segmenter? and (2) beyond that, can the segmenter in turn guide SAM through reciprocal supervision, enabling mutual improvement? To answer these questions, we present SCALER, a unified collaborative framework toward LDCOS that jointly optimizes a mean-teacher segmenter and a learnable SAM. SCALER operates in two alternating phases. In textbfPhase uppercaseexpandafterromannumeral1, the segmenter is optimized under fixed SAM supervision using entropy-based image-level and uncertainty-based pixel-level weighting to select reliable pseudo-label regions and emphasize harder examples. In textbfPhase uppercaseexpandafterromannumeral2, SAM is updated via augmentation invariance and noise resistance losses, leveraging its inherent robustness to perturbations. Experiments demonstrate that SCALER yields consistent performance gains across eight semi- and weakly-supervised COS tasks. The results further suggest that SCALER can serve as a general training paradigm to enhance both lightweight segmenters and large foundation models under label-scarce conditions. Code will be released.