# Generalised Co-Salient Object Detection

arXiv: https://arxiv.org/abs/2208.09668 · HTML: https://mlsanity.com/paper/2208.09668/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 58 references that are in the map.
- First posted on arXiv 20 Aug 2022.

## Builds on (most cited first, 27 of 27)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [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)
- [What Are Bayesian Neural Network Posteriors Really Like?](https://mlsanity.com/paper/2104.14421/) (2021)
- [EGNet:Edge Guidance Network for Salient Object Detection](https://mlsanity.com/paper/1908.08297/) (2019)
- [Uncertainty-aware Joint Salient Object and Camouflaged Object Detection](https://mlsanity.com/paper/2104.02628/) (2021)
- [Distributional Smoothing with Virtual Adversarial Training](https://mlsanity.com/paper/1507.00677/) (2015)
- [Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction](https://mlsanity.com/paper/2112.13528/) (2021)
- [Re-thinking Co-Salient Object Detection](https://mlsanity.com/paper/2007.03380/) (2020)
- [Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes](https://mlsanity.com/paper/2005.08140/) (2020)
- [DisturbLabel: Regularizing CNN on the Loss Layer](https://mlsanity.com/paper/1605.00055/) (2016)
- [Gradient-Induced Co-Saliency Detection](https://mlsanity.com/paper/2004.13364/) (2020)
- [Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection](https://mlsanity.com/paper/2003.06167/) (2020)
- [Group Collaborative Learning for Co-Salient Object Detection](https://mlsanity.com/paper/2104.01108/) (2021)
- [CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection](https://mlsanity.com/paper/2011.04887/) (2020)
- [Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection](https://mlsanity.com/paper/2110.00338/) (2021)
- [Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection](https://mlsanity.com/paper/2203.05787/) (2022)
- [Learning structure-aware semantic segmentation with image-level supervision](https://mlsanity.com/paper/2104.07216/) (2021)
- [Semi-supervised Salient Object Detection with Effective Confidence Estimation](https://mlsanity.com/paper/2112.14019/) (2021)

## Built on by (most cited first, 2 of 2)

- [Towards Open World Detection: A Survey](https://mlsanity.com/paper/2508.16527/) (2025)
- [AusSmoke meets MultiNatSmoke: a fully-labelled diverse smoke segmentation dataset](https://mlsanity.com/paper/2604.23542/) (2026)

## Abstract (arXiv)

We propose a new setting that relaxes an assumption in the conventional Co-Salient Object Detection (CoSOD) setting by allowing the presence of "noisy images" which do not show the shared co-salient object. We call this new setting Generalised Co-Salient Object Detection (GCoSOD). We propose a novel random sampling based Generalised CoSOD Training (GCT) strategy to distill the awareness of inter-image absence of co-salient objects into CoSOD models. It employs a Diverse Sampling Self-Supervised Learning (DS3L) that, in addition to the provided supervised co-salient label, introduces additional self-supervised labels for noisy images (being null, that no co-salient object is present). Further, the random sampling process inherent in GCT enables the generation of a high-quality uncertainty map highlighting potential false-positive predictions at instance level. To evaluate the performance of CoSOD models under the GCoSOD setting, we propose two new testing datasets, namely CoCA-Common and CoCA-Zero, where a common salient object is partially present in the former and completely absent in the latter. Extensive experiments demonstrate that our proposed method significantly improves the performance of CoSOD models in terms of the performance under the GCoSOD setting as well as the model calibration degrees.