# A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection

arXiv: https://arxiv.org/abs/1610.01708 · HTML: https://mlsanity.com/paper/1610.01708/

## Key facts

- Cited by 23 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 6 Oct 2016.

## Built on by (most cited first, 23 of 23)

- [TranSalNet: Towards perceptually relevant visual saliency prediction](https://mlsanity.com/paper/2110.03593/) (2021)
- [TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction](https://mlsanity.com/paper/2301.02315/) (2023)
- [ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgery](https://mlsanity.com/paper/2112.08189/) (2021)
- [Spatio-Temporal Self-Attention Network for Video Saliency Prediction](https://mlsanity.com/paper/2108.10696/) (2021)
- [SUM: Saliency Unification through Mamba for Visual Attention Modeling](https://mlsanity.com/paper/2406.17815/) (2024)
- [Instance-Level Relative Saliency Ranking with Graph Reasoning](https://mlsanity.com/paper/2107.03824/) (2021)
- [Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net](https://mlsanity.com/paper/2108.07851/) (2021)
- [Shifting Focus with HCEye: Exploring the Dynamics of Visual Highlighting and Cognitive Load on User Attention and Saliency Prediction](https://mlsanity.com/paper/2404.14232/) (2024)
- [Data Augmentation via Latent Diffusion for Saliency Prediction](https://mlsanity.com/paper/2409.07307/) (2024)
- [Predicting Visual Attention in Graphic Design Documents](https://mlsanity.com/paper/2407.02439/) (2024)
- [Modeling Object Dissimilarity for Deep Saliency Prediction](https://mlsanity.com/paper/2104.03864/) (2021)
- [Learning Saliency From Fixations](https://mlsanity.com/paper/2311.14073/) (2023)
- [Position: Do Not Explain Vision Models Without Context](https://mlsanity.com/paper/2404.18316/) (2024)
- [DarkDeblur: Learning single-shot image deblurring in low-light condition](https://mlsanity.com/paper/2503.02194/) (2025)
- [Towards Accurate RGB-D Saliency Detection with Complementary Attention and Adaptive Integration](https://mlsanity.com/paper/2102.04046/) (2021)
- [Clustered Saliency Prediction](https://mlsanity.com/paper/2207.02205/) (2022)
- [Learning Gaussian Representation for Eye Fixation Prediction](https://mlsanity.com/paper/2403.14821/) (2024)
- [MDS-ViTNet: Improving saliency prediction for Eye-Tracking with Vision Transformer](https://mlsanity.com/paper/2405.19501/) (2024)
- [Phase-Aligned RoPE for Mixed-Resolution Diffusion Transformer](https://mlsanity.com/paper/2511.19778/) (2025)
- [Noise-Aware Video Saliency Prediction](https://mlsanity.com/paper/2104.08038/) (2021)
- [Deriving Explanation of Deep Visual Saliency Models](https://mlsanity.com/paper/2109.03575/) (2021)
- [Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance](https://mlsanity.com/paper/2211.05364/) (2022)
- [ObjectVisA-120: Object-based Visual Attention Prediction in Interactive Street-crossing Environments](https://mlsanity.com/paper/2601.13218/) (2026)

## Abstract (arXiv)

Traditional saliency models usually adopt hand-crafted image features and human-designed mechanisms to calculate local or global contrast. In this paper, we propose a novel computational saliency model, i.e., deep spatial contextual long-term recurrent convolutional network (DSCLRCN) to predict where people looks in natural scenes. DSCLRCN first automatically learns saliency related local features on each image location in parallel. Then, in contrast with most other deep network based saliency models which infer saliency in local contexts, DSCLRCN can mimic the cortical lateral inhibition mechanisms in human visual system to incorporate global contexts to assess the saliency of each image location by leveraging the deep spatial long short-term memory (DSLSTM) model. Moreover, we also integrate scene context modulation in DSLSTM for saliency inference, leading to a novel deep spatial contextual LSTM (DSCLSTM) model. The whole network can be trained end-to-end and works efficiently when testing. Experimental results on two benchmark datasets show that DSCLRCN can achieve state-of-the-art performance on saliency detection. Furthermore, the proposed DSCLSTM model can significantly boost the saliency detection performance by incorporating both global spatial interconnections and scene context modulation, which may uncover novel inspirations for studies on them in computational saliency models.