# Visual Saliency Detection Based on Multiscale Deep CNN Features

arXiv: https://arxiv.org/abs/1609.02077 · HTML: https://mlsanity.com/paper/1609.02077/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2016.

## Built on by (most cited first, 26 of 26)

- [Boundary-Aware Segmentation Network for Mobile and Web Applications](https://mlsanity.com/paper/2101.04704/) (2021)
- [Does Thermal Really Always Matter for RGB-T Salient Object Detection?](https://mlsanity.com/paper/2210.04266/) (2022)
- [COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality](https://mlsanity.com/paper/2112.05892/) (2021)
- [Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals](https://mlsanity.com/paper/2303.06845/) (2023)
- [MFFN: Multi-view Feature Fusion Network for Camouflaged Object Detection](https://mlsanity.com/paper/2210.06361/) (2022)
- [DC-Net: Divide-and-Conquer for Salient Object Detection](https://mlsanity.com/paper/2305.14955/) (2023)
- [REB: Reducing Biases in Representation for Industrial Anomaly Detection](https://mlsanity.com/paper/2308.12577/) (2023)
- [Adaptive Low Rank Adaptation of Segment Anything to Salient Object Detection](https://mlsanity.com/paper/2308.05426/) (2023)
- [Video Salient Object Detection via Adaptive Local-Global Refinement](https://mlsanity.com/paper/2104.14360/) (2021)
- [LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability](https://mlsanity.com/paper/2303.16407/) (2023)
- [Relevance for Human Robot Collaboration](https://mlsanity.com/paper/2409.07753/) (2024)
- [Visual Boundary Knowledge Translation for Foreground Segmentation](https://mlsanity.com/paper/2108.00379/) (2021)
- [Relating Blindsight and AI: A Review](https://mlsanity.com/paper/2201.00616/) (2021)
- [Disentangle Saliency Detection into Cascaded Detail Modeling and Body Filling](https://mlsanity.com/paper/2202.04112/) (2022)
- [Deep Learning Techniques for Visual Counting](https://mlsanity.com/paper/2206.03033/) (2022)
- [Sharp Eyes: A Salient Object Detector Working The Same Way as Human Visual Characteristics](https://mlsanity.com/paper/2301.07431/) (2023)
- [Hyperspectral Remote Sensing Images Salient Object Detection: The First Benchmark Dataset and Baseline](https://mlsanity.com/paper/2504.02416/) (2025)
- [Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance](https://mlsanity.com/paper/2605.25385/) (2026)
- [Shape-Tailored Deep Neural Networks](https://mlsanity.com/paper/2102.08497/) (2021)
- [Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation](https://mlsanity.com/paper/2108.04226/) (2021)
- [Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2201.09574/) (2022)
- [Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts](https://mlsanity.com/paper/2206.00679/) (2022)
- [PAANet:Visual Perception based Four-stage Framework for Salient Object Detection using High-order Contrast Operator](https://mlsanity.com/paper/2211.08724/) (2022)
- [Deep-Learning-Assisted Analysis of Cataract Surgery Videos](https://mlsanity.com/paper/2312.05900/) (2023)
- [Enhancing Visual Token Representations for Video Large Language Models via Training-Free Spatial-Temporal Pooling and Gridding](https://mlsanity.com/paper/2605.22078/) (2026)
- [When Depth Hurts: Reliability-Aware Geometry Distillation for Depth-Free RGB-D Salient Object Detection](https://mlsanity.com/paper/2609.03378/) (2026)

## Abstract (arXiv)

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. The penultimate layer of our neural network has been confirmed to be a discriminative high-level feature vector for saliency detection, which we call deep contrast feature. To generate a more robust feature, we integrate handcrafted low-level features with our deep contrast feature. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks, improving the F- measure by 6.12% and 10.0% respectively on the DUT-OMRON dataset and our new dataset (HKU-IS), and lowering the mean absolute error by 9% and 35.3% respectively on these two datasets.