Researchers are tackling the challenge of consistent semantic segmentation in video for automated driving, a crucial element for reliable environmental perception. Serin Varghese, Kevin Ross, and Fabian Hueger, from Heinrich-Heine-University Düsseldorf and CARIAD SE, alongside Kira Maag et al., present a novel Spatio-Temporal Attention (STA) mechanism that integrates multi-frame context into transformer architectures. This work is significant because current models typically analyse video frames in isolation, overlooking valuable temporal information that enhances accuracy and stability in complex, dynamic environments. By modifying self-attention to process spatio-temporal feature sequences efficiently, STA demonstrably improves temporal consistency by 9.20 percentage points and mean intersection over union by up to 1.76 percentage points on the Cityscapes and BDD100k datasets, establishing it as a promising architectural enhancement for video-based semantic segmentation.
Scientists have achieved remarkable success in semantic segmentation for environmental perception. However, existing models process video frames independently, thus failing to leverage temporal consistency, which could significantly improve both accuracy and stability in dynamic scenes. In this work, researchers propose a Spatio-Temporal Attention (STA) mechanism that extends transformer attention blocks to incorporate multi-frame context, enabling robust temporal feature representations for video semantic segmentation.
Their approach modifies standard self-attention to process spatio-temporal feature sequences while maintaining computational efficiency and requiring minimal changes to existing architectures. STA demonstrates broad applicability.
Scientists have demonstrated the capability of a new method across diverse transformer architectures and its effectiveness across both lightweight and larger-scale models. A comprehensive evaluation on the Cityscapes and BDD100k datasets shows substantial improvements of 9.20 percentage points in temporal consistency metrics and up to 1.76 percentage points in mean intersection over union compared to single-frame baselines.
These results demonstrate the proposed method as an effective architectural enhancement for video-based semantic segmentation applications. Semantic segmentation has emerged as a cornerstone in the field of computer vision, enabling pixel-level classification of image content into a predefined set of semantic classes for comprehensive scene understanding.
By assigning semantic labels to every pixel, semantic segmentation finds applications in diverse areas such as medical imaging, automated driving, and augmented reality. Convolutional neural networks (CNNs) have long been the backbone of semantic segmentation, with encoder-decoder architectures such as U-Net, DeepLabv3+ and HRNet achieving strong performance through techniques like skip connections, atrous spatial pyramid pooling and multi-scale feature aggregation.
More recently, transformer-based models, such as SegFormer and SETR, have gained prominence, as their self-attention mechanism enables global context modelling and improved scalability, offering significant advantages over traditional CNNs when processing complex scene structures. Independent of the model architecture, these methods primarily work with static images and treat each image in isolation.
While effective for tasks involving single images, their inability to utilise temporal information contained in sequential data limits their applicability in dynamic environments such as automated driving. In these applications, video data is often available and a reliable, temporally consistent prediction is of utmost interest.
An example of a single-frame prediction that is unstable over time is shown in Fig. Temporal consistency refers to the smoothness and stability of predictions over sequential frames in video data. This is an important consideration for applications in safety-critical domains such as robotics and automated driving, where the consistency of predictions is a key factor in ensuring robust system behaviour (Maag et al., 2021; Rottmann et al., 2020).
Challenges in maintaining temporal consistency stem from factors such as occlusions, lighting changes, and rapid scene dynamics. In, temporal consistency based on optical flow was defined as the property that objects or structures in an image sequence remain consistently represented over time when their motion is accounted for.
Optical flow provides the estimated displacement of pixels between two consecutive frames. If the semantic segmentation prediction of a model from one frame is transferred (i.e., “warped”) to the next frame using optical flow, the prediction is considered temporally consistent if it matches the actual model prediction in the next frame.
Deviations indicate temporal inconsistency, e.g., when boundaries flicker, objects jump, or textures are not stable. Video semantic segmentation combines the demands of accurate spatial segmentation with the need for temporally coherent predictions across consecutive frames. In contrast to image segmentation, video segmentation must additionally address challenges unique to video data, such as motion blur, varying frame rates, and cross-frame feature correspondence.
As a result, video segmentation requires models that not only capture fine-grained spatial detail but also enforce stability over time. Early approaches often relied on motion cues such as optical flow to enforce temporal consistency, but these methods are computationally expensive since they require estimating pixel-wise motion fields across frames.
In comparison, recent transformer-based methods typically use 3D attentions, which have high computational costs, or introduce plug-in modules that extend existing architectures to model temporal coherence, offering a more scalable alternative to dense optical flow computation. In comparison, a standalone transformer approach for video semantic segmentation is presented that integrates temporal context and can be integrated into common transformer architectures with minimal overhead.
This work proposes a novel Spatio-Temporal Attention (STA) mechanism that enhances transformer-based video semantic segmentation by directly integrating temporal reasoning into the core attention module. In contrast to existing methods, which either depend on computationally expensive optical flow to align features across frames or create new architectures (CNN or transformer) with spatio-temporal components, the approach eliminates the need for explicit motion estimation and can be easily integrated into any transformer-based architectures.
STA extends the standard formulation of self-attention by incorporating information from (multiple) previous frames into the attention calculation of the current frame. This allows the model to capture cross-frame feature correlations and ensure that the resulting feature maps are enriched with temporal context while preserving fine-grained spatial details.
As a result, STA not only improves segmentation accuracy but also enforces temporal consistency across consecutive frames, a crucial property in video-based applications such as automated driving. The benefit of the method is that the attention information from the previous frames is calculated anyway and can be fed directly into the attention mechanism of the current frame without an additional module, which keeps the additional computational overhead very low.
To validate its effectiveness, STA is integrated into state-of-the-art transformer architectures, SegFormer and UMixFormer, and a systematic evaluation is conducted across different model scales, demonstrating both its general applicability and scalability to varying computational budgets. The comprehensive evaluation on Cityscapes and BDD100k datasets demonstrates substantial improvements in both spatial accuracy and temporal consistency.
STA-enhanced models achieve improvements of up to 1.76 percentage points in mean intersection over union (mIoU) and remarkable gains of up to 9.20 percentage points in mean temporal consistency (mTC), with the most substantial improvements observed on challenging driving scenarios in BDD100k. The temporal context ablation study reveals that using two previous frames for the prediction provide optimal performance, offering practical guidance for real-time applications where computational efficiency is paramount.
Temporal consistency is a critical aspect in video semantic segmentation, as inconsistent predictions across frames can lead to perceptual artifacts and unreliable scene understanding (Maag et al., 2019; Maag et al., 2021). Early works addressed temporal smoothing using post-processing methods that require semantic segmentation prediction and then refine the predictions using optical flow (Dong et al., 2015; Hur and Roth, 2016), but these approaches often failed to capture long-range dependencies and struggled with dynamic objects.
Subsequent approaches integrate optical flow directly into the training process rather than as post-processing refinement. In, the predictions of two network branches are combined: a reference branch that extracts highly detailed features on a reference frame and warps these features forward using frame-to-frame optical flow estimates, and an update branch that computes features on the current frame and performs a temporal update for each video frame.
In, deep feature flow was proposed, where the expensive convolutional sub-network is executed only on sparse key frames and the resulting deep feature maps are propagated to other frames via a flow field. The works of Varghese et al. addressed this problem through specialised loss functions and architectural modifications, advancing the understanding of temporal consistency in automotive applications.
While these optical flow-based methods provide valuable complementary approaches to temporal modelling, they require separate motion estimation pipelines that can be computationally intensive and error-prone, motivating the development of methods that incorporate temporal reasoning directly within network architectures. Video semantic segmentation has evolved from simple frame-by-frame processing to sophisticated architectures that explicitly model temporal dependencies.
CNN-based video segmentation methods typically introduce temporal modules such as convolutional long short-term memory (ConvLSTM,) or 3D convolutions to incorporate temporal cues. In, “clockwork” CNNs are introduced driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability.
However, these CNN-based approaches are fundamentally limited by their localized receptive fields, struggling with long-range temporal dependencies that are crucial for maintaining consistency across video sequences. In addition, attention modules are used in CNNs. Two examples of such architectures are TDNet, which uses attention propagation modules to efficiently combine sub-features across frames, and TMANet, which considers self-attention to aggregate the relations between consecutive video frames.
Transformer-based approaches have emerged as powerful alternatives, naturally modelling long-range dependencies via self-attention. However, standard vision transformers operate purely in the spatial domain on single frames, without incorporating temporal information (Xie et al., 2021; Zheng et al., 2021).
Architectures like TimeSformer, ViViT, and Video Swin Transformers extend transformers to video understanding by explicitly modelling spatio-temporal dependencies. TimeSformer factorizes attention into spatial and temporal components, reducing the quadratic cost of full 3D attention. ViViT, in contrast, explores multiple variants of spatio-temporal attention, providing a flexible framework for video modelling.
Video Swin Transformer builds on the hierarchical Swin architecture by applying shifted windows in both space and time, enabling efficient local spatio-temporal modelling at multiple scales. While these architectures demonstrate the potential of transformer-based video understanding, they typically require complete architectural redesigns (Baghbaderani et al., 2024; Weng et al., 2023; Xing et al., 2022) and substantial computational overhead (Park et al., 2022; Yang et al., 2024), limiting their adaptability to existing transformer models.
An alternative approach involves modular plug-in temporal modules that add temporal reasoning to existing networks without modifying the core architecture. The Sparse Temporal Transformer (STT,) introduces a temporal module that captures cross-frame context using query and key selection, which encodes temporal dependencies from previous frames. Despite these advances, a fundamental gap remains in transformer architectures for video understanding.
Improvements of 9.20 percentage points in temporal consistency metrics were achieved through the implementation of a Spatio-Temporal Attention mechanism. This work introduces a novel approach to video semantic segmentation by directly integrating temporal reasoning into transformer attention blocks. The research demonstrates that the STA mechanism effectively incorporates multi-frame context, resulting in robust temporal feature representations.
Evaluation on the Cityscapes dataset revealed a 1.76 percentage point increase in mean intersection over union compared to single-frame baseline models. The STA mechanism modifies standard self-attention to process spatio-temporal feature sequences, maintaining computational efficiency and requiring minimal alterations to existing architectures.
Applicability across diverse transformer architectures was confirmed, with the approach remaining effective in both lightweight and larger-scale models. Analysis using the BDD100k dataset further substantiated these findings, consistently showing improvements in segmentation performance. The study focused on quantifying temporal consistency through established metrics, demonstrating a substantial gain over methods that process video frames independently.
Specifically, the research measured temporal consistency by assessing the smoothness and stability of predictions across sequential video frames. The STA mechanism successfully addressed challenges stemming from occlusions, lighting changes, and rapid scene dynamics, leading to more reliable predictions.
By capturing cross-frame feature correlations, the model generates feature maps enriched with temporal context, enhancing both segmentation accuracy and temporal stability. This advancement is particularly relevant for applications requiring robust system behaviour, such as robotics and automated driving.
A novel spatio-temporal attention mechanism extends transformer architectures to process multi-frame video sequences for semantic segmentation. This approach integrates temporal reasoning directly into attention computations, creating a unified architectural enhancement that maintains computational efficiency while leveraging correlations between frames.
Unlike methods reliant on optical flow or CNN-based temporal modules, this mechanism avoids additional computational demands and potential issues with long-range dependencies, offering a lightweight and scalable solution for video analysis. Evaluations conducted on the Cityscapes and BDD100k datasets demonstrate consistent improvements in both spatial accuracy, with gains of up to 1.76 percentage points in mean intersection over union, and temporal consistency, increasing by as much as 9.20 percentage points in temporal consistency metrics.
Ablation studies identified an optimal temporal context of three frames, balancing performance with computational cost. Although the method introduces a throughput reduction of 15-17%, this is considered a reasonable trade-off given the substantial gains in temporal consistency, particularly for applications demanding prediction stability such as automated driving.
The authors acknowledge a slight reduction in processing speed, but highlight the mechanism’s general applicability across different transformer architectures and model scales without requiring architecture-specific adjustments. Future research will focus on extending the mechanism to other video tasks, including object tracking and action recognition, and exploring more efficient temporal modelling strategies to further optimise performance for real-time applications. Overall, this work represents a practical advancement in integrating temporal reasoning into transformer-based models, making it suitable for deployment in real-world scenarios.
👉 More information
🗞 Spatio-Temporal Attention for Consistent Video Semantic Segmentation in Automated Driving
🧠 ArXiv: https://arxiv.org/abs/2602.10052
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