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

• URL: http://arxiv.org/abs/2309.08220v1
• Date: Fri, 15 Sep 2023 07:39:53 GMT
• Title: UniST: Towards Unifying Saliency Transformer for Video Saliency Prediction and Detection
• Authors: Junwen Xiong, Peng Zhang, Chuanyue Li, Wei Huang, Yufei Zha, Tao You
• Abstract summary: We introduce the Unified Saliency Transformer (UniST) framework, which comprehensively utilizes the essential attributes of video saliency prediction and video salient object detection. To the best of our knowledge, this is the first work that explores designing a transformer structure for both saliency modeling tasks.
• Score: 9.063895463649414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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