# AIM 2024 Challenge on Video Saliency Prediction: Methods and Results

arXiv: https://arxiv.org/abs/2409.14827 · HTML: https://mlsanity.com/paper/2409.14827/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 56 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 21 of 21)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Unmasked Teacher: Towards Training-Efficient Video Foundation Models](https://mlsanity.com/paper/2303.16058/) (2023)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [YouTube UGC Dataset for Video Compression Research](https://mlsanity.com/paper/1904.06457/) (2019)
- [Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model](https://mlsanity.com/paper/1611.09571/) (2016)
- [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)
- [SA-Net: Shuffle Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/2102.00240/) (2021)
- [Contextual Encoder-Decoder Network for Visual Saliency Prediction](https://mlsanity.com/paper/1902.06634/) (2019)
- [ViNet: Pushing the limits of Visual Modality for Audio-Visual Saliency Prediction](https://mlsanity.com/paper/2012.06170/) (2020)
- [Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition](https://mlsanity.com/paper/1312.7570/) (2013)
- [DAVE: A Deep Audio-Visual Embedding for Dynamic Saliency Prediction](https://mlsanity.com/paper/1905.10693/) (2019)
- [CASP-Net: Rethinking Video Saliency Prediction from an Audio-VisualConsistency Perceptual Perspective](https://mlsanity.com/paper/2303.06357/) (2023)
- [Realistic Saliency Guided Image Enhancement](https://mlsanity.com/paper/2306.06092/) (2023)
- [Saliency Revisited: Analysis of Mouse Movements versus Fixations](https://mlsanity.com/paper/1705.10546/) (2017)
- [SalFoM: Dynamic Saliency Prediction with Video Foundation Models](https://mlsanity.com/paper/2404.03097/) (2024)
- [Context-Aware Saliency Detection for Image Retargeting Using Convolutional Neural Networks](https://mlsanity.com/paper/1910.08071/) (2019)
- [AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results](https://mlsanity.com/paper/2408.11982/) (2024)
- [Predicting video saliency using crowdsourced mouse-tracking data](https://mlsanity.com/paper/1907.00480/) (2019)

## Built on by (most cited first, 7 of 7)

- [NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results](https://mlsanity.com/paper/2604.14816/) (2026)
- [AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content](https://mlsanity.com/paper/2409.17256/) (2024)
- [AIM 2024 Challenge on UHD Blind Photo Quality Assessment](https://mlsanity.com/paper/2409.16271/) (2024)
- [Compressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results](https://mlsanity.com/paper/2409.16277/) (2024)
- [ViSAGE @ NTIRE 2026 Challenge on Video Saliency Prediction](https://mlsanity.com/paper/2604.08613/) (2026)
- [AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results](https://mlsanity.com/paper/2410.04225/) (2024)
- [EgoCampus: Egocentric Pedestrian Eye Gaze Model and Dataset](https://mlsanity.com/paper/2512.07668/) (2025)

## Abstract (arXiv)

This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences. Saliency maps are widely exploited in various applications, including video compression, quality assessment, visual perception studies, the advertising industry, etc. For this competition, a previously unused large-scale audio-visual mouse saliency (AViMoS) dataset of 1500 videos with more than 70 observers per video was collected using crowdsourced mouse tracking. The dataset collection methodology has been validated using conventional eye-tracking data and has shown high consistency. Over 30 teams registered in the challenge, and there are 7 teams that submitted the results in the final phase. The final phase solutions were tested and ranked by commonly used quality metrics on a private test subset. The results of this evaluation and the descriptions of the solutions are presented in this report. All data, including the private test subset, is made publicly available on the challenge homepage - https://challenges.videoprocessing.ai/challenges/video-saliency-prediction.html.