# Uncertainty-aware Joint Salient Object and Camouflaged Object Detection

arXiv: https://arxiv.org/abs/2104.02628 · HTML: https://mlsanity.com/paper/2104.02628/

## Key facts

- Cited by 114 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 61 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [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)
- [EGNet:Edge Guidance Network for Salient Object Detection](https://mlsanity.com/paper/1908.08297/) (2019)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [A Simple Pooling-Based Design for Real-Time Salient Object Detection](https://mlsanity.com/paper/1904.09569/) (2019)
- [Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation](https://mlsanity.com/paper/2003.08813/) (2020)
- [Simultaneously Localize, Segment and Rank the Camouflaged Objects](https://mlsanity.com/paper/2103.04011/) (2021)
- [Visual Saliency Based on Multiscale Deep Features](https://mlsanity.com/paper/1503.08663/) (2015)
- [F3Net: Fusion, Feedback and Focus for Salient Object Detection](https://mlsanity.com/paper/1911.11445/) (2019)
- [Adversarial Learning for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/1802.07934/) (2018)
- [Semi-Supervised Semantic Segmentation with High- and Low-level Consistency](https://mlsanity.com/paper/1908.05724/) (2019)
- [Mutual Graph Learning for Camouflaged Object Detection](https://mlsanity.com/paper/2104.02613/) (2021)
- [UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders](https://mlsanity.com/paper/2004.05763/) (2020)
- [Highly Efficient Salient Object Detection with 100K Parameters](https://mlsanity.com/paper/2003.05643/) (2020)
- [JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection](https://mlsanity.com/paper/2004.08515/) (2020)
- [Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1909.04161/) (2019)
- [Uncertainty Inspired RGB-D Saliency Detection](https://mlsanity.com/paper/2009.03075/) (2020)
- [Joint Semantic Segmentation and Boundary Detection using Iterative Pyramid Contexts](https://mlsanity.com/paper/2004.07684/) (2020)
- [Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground](https://mlsanity.com/paper/1803.06091/) (2018)
- [Progressive Feature Polishing Network for Salient Object Detection](https://mlsanity.com/paper/1911.05942/) (2019)
- [cmSalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks](https://mlsanity.com/paper/1912.10280/) (2019)
- [Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network](https://mlsanity.com/paper/1703.09695/) (2017)
- [ROSA: Robust Salient Object Detection against Adversarial Attacks](https://mlsanity.com/paper/1905.03434/) (2019)
- [Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation](https://mlsanity.com/paper/2003.11303/) (2020)
- [Joint Learning of Semantic Alignment and Object Landmark Detection](https://mlsanity.com/paper/1910.00754/) (2019)

## Built on by (most cited first, 30 of 114)

- [Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection](https://mlsanity.com/paper/2203.02688/) (2022)
- [Explicit Visual Prompting for Low-Level Structure Segmentations](https://mlsanity.com/paper/2303.10883/) (2023)
- [Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection](https://mlsanity.com/paper/2304.04709/) (2023)
- [Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers](https://mlsanity.com/paper/2303.14816/) (2023)
- [High-resolution Iterative Feedback Network for Camouflaged Object Detection](https://mlsanity.com/paper/2203.11624/) (2022)
- [Boundary-Guided Camouflaged Object Detection](https://mlsanity.com/paper/2207.00794/) (2022)
- [Deep Gradient Learning for Efficient Camouflaged Object Detection](https://mlsanity.com/paper/2205.12853/) (2022)
- [SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More](https://mlsanity.com/paper/2408.04579/) (2024)
- [CamoFormer: Masked Separable Attention for Camouflaged Object Detection](https://mlsanity.com/paper/2212.06570/) (2022)
- [VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning](https://mlsanity.com/paper/2311.15011/) (2023)
- [Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects](https://mlsanity.com/paper/2308.03166/) (2023)
- [SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More](https://mlsanity.com/paper/2304.09148/) (2023)
- [Source-free Depth for Object Pop-out](https://mlsanity.com/paper/2212.05370/) (2022)
- [ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection](https://mlsanity.com/paper/2310.20208/) (2023)
- [Advances in Deep Concealed Scene Understanding](https://mlsanity.com/paper/2304.11234/) (2023)
- [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)
- [A Survey of Camouflaged Object Detection and Beyond](https://mlsanity.com/paper/2408.14562/) (2024)
- [Camouflaged Object Detection via Context-aware Cross-level Fusion](https://mlsanity.com/paper/2207.13362/) (2022)
- [Diffusion Model for Camouflaged Object Detection](https://mlsanity.com/paper/2308.00303/) (2023)
- [A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications](https://mlsanity.com/paper/2111.01968/) (2021)
- [Boosting Camouflaged Object Detection with Dual-Task Interactive Transformer](https://mlsanity.com/paper/2205.10579/) (2022)
- [A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection](https://mlsanity.com/paper/2402.18922/) (2024)
- [SAM-COD: SAM-guided Unified Framework for Weakly-Supervised Camouflaged Object Detection](https://mlsanity.com/paper/2408.10760/) (2024)
- [CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models](https://mlsanity.com/paper/2305.17932/) (2023)
- [Exploring Deeper! Segment Anything Model with Depth Perception for Camouflaged Object Detection](https://mlsanity.com/paper/2407.12339/) (2024)
- [Salient Objects in Clutter](https://mlsanity.com/paper/2105.03053/) (2021)
- [Towards Deeper Understanding of Camouflaged Object Detection](https://mlsanity.com/paper/2205.11333/) (2022)
- [Referring Camouflaged Object Detection](https://mlsanity.com/paper/2306.07532/) (2023)
- [Generative Transformer for Accurate and Reliable Salient Object Detection](https://mlsanity.com/paper/2104.10127/) (2021)

## Abstract (arXiv)

Visual salient object detection (SOD) aims at finding the salient object(s) that attract human attention, while camouflaged object detection (COD) on the contrary intends to discover the camouflaged object(s) that hidden in the surrounding. In this paper, we propose a paradigm of leveraging the contradictory information to enhance the detection ability of both salient object detection and camouflaged object detection. We start by exploiting the easy positive samples in the COD dataset to serve as hard positive samples in the SOD task to improve the robustness of the SOD model. Then, we introduce a similarity measure module to explicitly model the contradicting attributes of these two tasks. Furthermore, considering the uncertainty of labeling in both tasks' datasets, we propose an adversarial learning network to achieve both higher order similarity measure and network confidence estimation. Experimental results on benchmark datasets demonstrate that our solution leads to state-of-the-art (SOTA) performance for both tasks.