# View-aware Salient Object Detection for 360deg Omnidirectional Image

arXiv: https://arxiv.org/abs/2209.13222 · HTML: https://mlsanity.com/paper/2209.13222/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 79 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (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)
- [A Simple Pooling-Based Design for Real-Time Salient Object Detection](https://mlsanity.com/paper/1904.09569/) (2019)
- [Multi-scale Interactive Network for Salient Object Detection](https://mlsanity.com/paper/2007.09062/) (2020)
- [Concealed Object Detection](https://mlsanity.com/paper/2102.10274/) (2021)
- [The Secrets of Salient Object Segmentation](https://mlsanity.com/paper/1406.2807/) (2014)
- [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)
- [Visual Saliency Transformer](https://mlsanity.com/paper/2104.12099/) (2021)
- [Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection](https://mlsanity.com/paper/2203.02688/) (2022)
- [Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks](https://mlsanity.com/paper/1907.06781/) (2019)
- [Label Decoupling Framework for Salient Object Detection](https://mlsanity.com/paper/2008.11048/) (2020)
- [Global Context-Aware Progressive Aggregation Network for Salient Object Detection](https://mlsanity.com/paper/2003.00651/) (2020)
- [RGB-D Salient Object Detection: A Survey](https://mlsanity.com/paper/2008.00230/) (2020)
- [Cube Padding for Weakly-Supervised Saliency Prediction in 360deg Videos](https://mlsanity.com/paper/1806.01320/) (2018)
- [Corners for Layout: End-to-End Layout Recovery from 360 Images](https://mlsanity.com/paper/1903.08094/) (2019)
- [Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground](https://mlsanity.com/paper/1803.06091/) (2018)
- [Instance-Level Salient Object Segmentation](https://mlsanity.com/paper/1704.03604/) (2017)
- [Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton](https://mlsanity.com/paper/2004.08595/) (2020)
- [Perceptual Quality Assessment of Omnidirectional Images](https://mlsanity.com/paper/2207.02674/) (2022)
- [TRACER: Extreme Attention Guided Salient Object Tracing Network](https://mlsanity.com/paper/2112.07380/) (2021)
- [Object Detection in Equirectangular Panorama](https://mlsanity.com/paper/1805.08009/) (2018)
- [Recursive Contour Saliency Blending Network for Accurate Salient Object Detection](https://mlsanity.com/paper/2105.13865/) (2021)
- [Salient Objects in Clutter](https://mlsanity.com/paper/2105.03053/) (2021)
- [Distortion-adaptive Salient Object Detection in 360^circ Omnidirectional Images](https://mlsanity.com/paper/1909.04913/) (2019)
- [Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss](https://mlsanity.com/paper/1912.08393/) (2019)

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

- [OmniZoomer: Learning to Move and Zoom in on Sphere at High-Resolution](https://mlsanity.com/paper/2308.08114/) (2023)
- [Distortion-aware Transformer in 360deg Salient Object Detection](https://mlsanity.com/paper/2308.03359/) (2023)
- [WinDB: HMD-free and Distortion-free Panoptic Video Fixation Learning](https://mlsanity.com/paper/2305.13901/) (2023)
- [Learning High-Quality Navigation and Zooming on Omnidirectional Images in Virtual Reality](https://mlsanity.com/paper/2405.00351/) (2024)
- [360VFI: A Dataset and Benchmark for Omnidirectional Video Frame Interpolation](https://mlsanity.com/paper/2407.14066/) (2024)
- [SphereSOD: Geometry-Structure Coupled Learning for 360 Salient Object Detection](https://mlsanity.com/paper/2609.07571/) (2026)

## Abstract (arXiv)

Image-based salient object detection (ISOD) in 360deg scenarios is significant for understanding and applying panoramic information. However, research on 360deg ISOD has not been widely explored due to the lack of large, complex, high-resolution, and well-labeled datasets. Towards this end, we construct a large scale 360deg ISOD dataset with object-level pixel-wise annotation on equirectangular projection (ERP), which contains rich panoramic scenes with not less than 2K resolution and is the largest dataset for 360deg ISOD by far to our best knowledge. By observing the data, we find current methods face three significant challenges in panoramic scenarios: diverse distortion degrees, discontinuous edge effects and changeable object scales. Inspired by humans' observing process, we propose a view-aware salient object detection method based on a Sample Adaptive View Transformer (SAVT) module with two sub-modules to mitigate these issues. Specifically, the sub-module View Transformer (VT) contains three transform branches based on different kinds of transformations to learn various features under different views and heighten the model's feature toleration of distortion, edge effects and object scales. Moreover, the sub-module Sample Adaptive Fusion (SAF) is to adjust the weights of different transform branches based on various sample features and make transformed enhanced features fuse more appropriately. The benchmark results of 20 state-of-the-art ISOD methods reveal the constructed dataset is very challenging. Moreover, exhaustive experiments verify the proposed approach is practical and outperforms the state-of-the-art methods.