# Visual Attention and Saliency Detection

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/visual-attention-and-saliency-detection/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on computational modeling and detection of visual saliency, including topics such as saliency detection, visual attention, deep learning for salient object detection, analysis of eye movements, image and video segmentation, and the interplay between bottom-up and top-down attention mechanisms. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t11605 |
| Works | 64 |

## Topic papers all

Showing 15 of 64.

- [The Visual Object Tracking VOT2016 Challenge Results](https://scholariq.org/papers/the-visual-object-tracking-vot2016-challenge-results/)
- [Predicting human gaze using low-level saliency combined with face detection](https://scholariq.org/papers/predicting-human-gaze-using-low-level-saliency-combined-with-face-detection/)
- [Faces and text attract gaze independent of the task: Experimental data and computer model](https://scholariq.org/papers/faces-and-text-attract-gaze-independent-of-the-task-experimental-data-and/)
- [Mutual Graph Learning for Camouflaged Object Detection](https://scholariq.org/papers/mutual-graph-learning-for-camouflaged-object-detection/)
- [Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study](https://scholariq.org/papers/advancing-image-understanding-in-poor-visibility-environments-a-collective/)
- [Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection](https://scholariq.org/papers/uncertainty-guided-transformer-reasoning-for-camouflaged-object-detection/)
- [Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition](https://scholariq.org/papers/deep-attention-based-spatially-recursive-networks-for-fine-grained-visual/)
- [Feature Aggregation and Propagation Network for Camouflaged Object Detection](https://scholariq.org/papers/feature-aggregation-and-propagation-network-for-camouflaged-object-detection/)
- [Artificial Intelligence, Artists, and Art](https://scholariq.org/papers/artificial-intelligence-artists-and-art/)
- [Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review](https://scholariq.org/papers/exploring-deep-learning-based-architecture-strategies-applications-and-current/)
- [Gaze Estimation by Exploring Two-Eye Asymmetry](https://scholariq.org/papers/gaze-estimation-by-exploring-two-eye-asymmetry/)
- [Research on insect pest image detection and recognition based on bio-inspired methods](https://scholariq.org/papers/research-on-insect-pest-image-detection-and-recognition-based-on-bio-inspired/)
- [Scale-Aware Modulation Meet Transformer](https://scholariq.org/papers/scale-aware-modulation-meet-transformer/)
- [Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation](https://scholariq.org/papers/leveraging-auxiliary-tasks-with-affinity-learning-for-weakly-supervised-semantic/)
- [Retinotopic organization of visual mental images as revealed by functional magnetic resonance imaging](https://scholariq.org/papers/retinotopic-organization-of-visual-mental-images-as-revealed-by-functional/)

## Topic primary papers

Showing 15 of 27.

- [Predicting human gaze using low-level saliency combined with face detection](https://scholariq.org/papers/predicting-human-gaze-using-low-level-saliency-combined-with-face-detection/)
- [Faces and text attract gaze independent of the task: Experimental data and computer model](https://scholariq.org/papers/faces-and-text-attract-gaze-independent-of-the-task-experimental-data-and/)
- [Mutual Graph Learning for Camouflaged Object Detection](https://scholariq.org/papers/mutual-graph-learning-for-camouflaged-object-detection/)
- [Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection](https://scholariq.org/papers/uncertainty-guided-transformer-reasoning-for-camouflaged-object-detection/)
- [Feature Aggregation and Propagation Network for Camouflaged Object Detection](https://scholariq.org/papers/feature-aggregation-and-propagation-network-for-camouflaged-object-detection/)
- [SLIC_SVM based leaf diseases saliency map extraction of tea plant](https://scholariq.org/papers/slic-svm-based-leaf-diseases-saliency-map-extraction-of-tea-plant/)
- [Edge-Aware Multiscale Feature Integration Network for Salient Object Detection in Optical Remote Sensing Images](https://scholariq.org/papers/edge-aware-multiscale-feature-integration-network-for-salient-object-detection/)
- [Detail to Attention: Exploiting Visual Tasks for Selective Rendering](https://scholariq.org/papers/detail-to-attention-exploiting-visual-tasks-for-selective-rendering/)
- [Simulating human saccadic scanpaths on natural images](https://scholariq.org/papers/simulating-human-saccadic-scanpaths-on-natural-images/)
- [Cascade Graph Neural Networks for RGB-D Salient Object Detection](https://scholariq.org/papers/cascade-graph-neural-networks-for-rgb-d-salient-object-detection/)
- [A GPU based saliency map for high-fidelity selective rendering](https://scholariq.org/papers/a-gpu-based-saliency-map-for-high-fidelity-selective-rendering/)
- [Salient Object Detection Techniques in Computer Vision—A Survey](https://scholariq.org/papers/salient-object-detection-techniques-in-computer-vision-a-survey/)
- [Kernelized Subspace Ranking for Saliency Detection](https://scholariq.org/papers/kernelized-subspace-ranking-for-saliency-detection/)
- [Predicting Visual Discomfort of Stereoscopic Images Using Human Attention Model](https://scholariq.org/papers/predicting-visual-discomfort-of-stereoscopic-images-using-human-attention-model/)
- [Directing gaze in 3D models with stylized focus](https://scholariq.org/papers/directing-gaze-in-3d-models-with-stylized-focus/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.