# UniST: Towards Unifying Saliency Transformer for Video Saliency Prediction and Detection

arXiv: https://arxiv.org/abs/2309.08220 · HTML: https://mlsanity.com/paper/2309.08220/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 46 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 26 of 26)

- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification](https://mlsanity.com/paper/1712.04851/) (2017)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [Motion Guided Attention for Video Salient Object Detection](https://mlsanity.com/paper/1909.07061/) (2019)
- [Revisiting Video Saliency: A Large-scale Benchmark and a New Model](https://mlsanity.com/paper/1801.07424/) (2018)
- [TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection](https://mlsanity.com/paper/1908.05786/) (2019)
- [A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection](https://mlsanity.com/paper/2203.04708/) (2022)
- [Video Salient Object Detection via Fully Convolutional Networks](https://mlsanity.com/paper/1702.00871/) (2017)
- [ViNet: Pushing the limits of Visual Modality for Audio-Visual Saliency Prediction](https://mlsanity.com/paper/2012.06170/) (2020)
- [STAViS: Spatio-Temporal AudioVisual Saliency Network](https://mlsanity.com/paper/2001.03063/) (2020)
- [Memory Aggregation Networks for Efficient Interactive Video Object Segmentation](https://mlsanity.com/paper/2003.13246/) (2020)
- [Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation](https://mlsanity.com/paper/2012.11655/) (2020)
- [Temporal-Spatial Feature Pyramid for Video Saliency Detection](https://mlsanity.com/paper/2105.04213/) (2021)
- [Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM](https://mlsanity.com/paper/1709.06316/) (2017)
- [TransVOS: Video Object Segmentation with Transformers](https://mlsanity.com/paper/2106.00588/) (2021)
- [CASP-Net: Rethinking Video Saliency Prediction from an Audio-VisualConsistency Perceptual Perspective](https://mlsanity.com/paper/2303.06357/) (2023)
- [TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation](https://mlsanity.com/paper/2301.04619/) (2023)
- [DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection](https://mlsanity.com/paper/2012.04886/) (2020)

## Built on by (most cited first, 1 of 1)

- [Transformer-based Video Saliency Prediction with High Temporal Dimension Decoding](https://mlsanity.com/paper/2401.07942/) (2024)

## Abstract (arXiv)

Video saliency prediction and detection are thriving research domains that enable computers to simulate the distribution of visual attention akin to how humans perceiving dynamic scenes. While many approaches have crafted task-specific training paradigms for either video saliency prediction or video salient object detection tasks, few attention has been devoted to devising a generalized saliency modeling framework that seamlessly bridges both these distinct tasks. In this study, we introduce the Unified Saliency Transformer (UniST) framework, which comprehensively utilizes the essential attributes of video saliency prediction and video salient object detection. In addition to extracting representations of frame sequences, a saliency-aware transformer is designed to learn the spatio-temporal representations at progressively increased resolutions, while incorporating effective cross-scale saliency information to produce a robust representation. Furthermore, a task-specific decoder is proposed to perform the final prediction for each task. To the best of our knowledge, this is the first work that explores designing a transformer structure for both saliency modeling tasks. Convincible experiments demonstrate that the proposed UniST achieves superior performance across seven challenging benchmarks for two tasks, and significantly outperforms the other state-of-the-art methods.