# BIAS: A Biologically Inspired Algorithm for Video Saliency Detection

arXiv: https://arxiv.org/abs/2604.08858 · HTML: https://mlsanity.com/paper/2604.08858/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 91 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 27 of 27)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [Exploring the Limitations of Behavior Cloning for Autonomous Driving](https://mlsanity.com/paper/1904.08980/) (2019)
- [Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling](https://mlsanity.com/paper/2301.03580/) (2023)
- [Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model](https://mlsanity.com/paper/1611.09571/) (2016)
- [Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning](https://mlsanity.com/paper/2008.00334/) (2020)
- [Unified Image and Video Saliency Modeling](https://mlsanity.com/paper/2003.05477/) (2020)
- [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)
- [DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark](https://mlsanity.com/paper/1904.12634/) (2019)
- [DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation](https://mlsanity.com/paper/2107.10189/) (2021)
- [ViNet: Pushing the limits of Visual Modality for Audio-Visual Saliency Prediction](https://mlsanity.com/paper/2012.06170/) (2020)
- [A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents](https://mlsanity.com/paper/2106.10197/) (2021)
- [Driver Anomaly Detection: A Dataset and Contrastive Learning Approach](https://mlsanity.com/paper/2009.14660/) (2020)
- [Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition](https://mlsanity.com/paper/1312.7570/) (2013)
- [Simple vs complex temporal recurrences for video saliency prediction](https://mlsanity.com/paper/1907.01869/) (2019)
- [Hierarchical Domain-Adapted Feature Learning for Video Saliency Prediction](https://mlsanity.com/paper/2010.01220/) (2020)
- [Temporal-Spatial Feature Pyramid for Video Saliency Detection](https://mlsanity.com/paper/2105.04213/) (2021)
- [Spatio-Temporal Self-Attention Network for Video Saliency Prediction](https://mlsanity.com/paper/2108.10696/) (2021)
- [SalFoM: Dynamic Saliency Prediction with Video Foundation Models](https://mlsanity.com/paper/2404.03097/) (2024)
- [The Unbearable Slowness of Being: Why do we live at 10 bits/s?](https://mlsanity.com/paper/2408.10234/) (2024)
- [Transformer-based Video Saliency Prediction with High Temporal Dimension Decoding](https://mlsanity.com/paper/2401.07942/) (2024)
- [Fast 2-D Complex Gabor Filter with Kernel Decomposition](https://mlsanity.com/paper/1704.05231/) (2017)
- [Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset](https://mlsanity.com/paper/2508.14567/) (2025)

## Abstract (arXiv)

We present BIAS, a fast, biologically inspired model for dynamic visual saliency detection in continuous video streams. Building on the Itti--Koch framework, BIAS incorporates a retina-inspired motion detector to extract temporal features, enabling the generation of saliency maps that integrate both static and motion information. Foci of attention (FOAs) are identified using a greedy multi-Gaussian peak-fitting algorithm that balances winner-take-all competition with information maximization. BIAS detects salient regions with millisecond-scale latency and outperforms heuristic-based approaches and several deep-learning models on the DHF1K dataset, particularly in videos dominated by bottom-up attention. Applied to traffic accident analysis, BIAS demonstrates strong real-world utility, achieving state-of-the-art performance in cause-effect recognition and anticipating accidents up to 0.72 seconds before manual annotation with reliable accuracy. Overall, BIAS bridges biological plausibility and computational efficiency to achieve interpretable, high-speed dynamic saliency detection.