Temporal Memory Attention for Video Semantic Segmentation (2102.08643v2)

Abstract: Video semantic segmentation requires to utilize the complex temporal relations between frames of the video sequence. Previous works usually exploit accurate optical flow to leverage the temporal relations, which suffer much from heavy computational cost. In this paper, we propose a Temporal Memory Attention Network (TMANet) to adaptively integrate the long-range temporal relations over the video sequence based on the self-attention mechanism without exhaustive optical flow prediction. Specially, we construct a memory using several past frames to store the temporal information of the current frame. We then propose a temporal memory attention module to capture the relation between the current frame and the memory to enhance the representation of the current frame. Our method achieves new state-of-the-art performances on two challenging video semantic segmentation datasets, particularly 80.3% mIoU on Cityscapes and 76.5% mIoU on CamVid with ResNet-50.

• The paper introduces the Temporal Memory Attention Network (TMANet) for video semantic segmentation, which utilizes temporal memory and attention to capture long-term dependencies more efficiently by removing the need for optical flow.
• TMANet's Temporal Memory Attention Module effectively integrates past frame information with current frames, improving video representation and enhancing semantic segmentation accuracy.
• The TMANet achieves state-of-the-art results on Cityscapes (80.3% mIoU) and CamVid (76.5% mIoU), demonstrating superior performance and lower computational cost for real-time applications.

Temporal Memory Attention for Video Semantic Segmentation

The paper "Temporal Memory Attention for Video Semantic Segmentation" introduces an advanced method to handle the intricacies of video semantic segmentation by utilizing Temporal Memory Attention Network (TMANet). This approach underscores the importance of effectively capitalizing on temporal relations inherent in video sequences.

Approach Overview

Traditional approaches often rely on optical flow to represent motion between frames, necessitating high computational resources and rendering these methods less efficient. TMANet circumvents the dependency on optical flow, leveraging a self-attention mechanism to manage long-term dependencies across video frames in a more computationally efficient manner.

Central to this novel approach is the use of memory networks to store temporal information, harnessing self-attention to integrate past frame data with current frame data. This enhances the representation of each video frame and optimizes the semantic segmentation task. By storing past frames in a structured memory and using a temporal memory attention module, the model identifies and exploits relationships between frames, significantly bolstering prediction accuracy.

Innovation and Contributions

The key contributions of this paper are outlined as follows:

1. Introduction of TMANet: The paper innovates by incorporating memory and self-attention mechanisms into video semantic segmentation, distinguishing it from prior models by dropping optical flow requirements and thus improving efficiency and speed.
2. Temporal Memory Attention Module: By capturing temporal correlations efficiently, this module lays the groundwork for integrating and representing long-term dependencies in video frames, facilitating precise semantic segmentation.
3. State-of-the-Art Results: TMANet achieves superior performance on prominent datasets such as Cityscapes (80.3% mIoU) and CamVid (76.5% mIoU) using ResNet-50 architecture, outperforming existing methods while maintaining a lower computational footprint.

Methodological Details

The TMANet systematically constructs memory from prior frames and processes them using shared network backbones for feature extraction. It employs encoding layers designed with a combination of 1x1 and 3x3 convolutions to optimize memory and query processing. The attention module calculates temporal relationships through efficient matrix operations and a softmax layer to derive attention maps.

Implications and Future Directions

The proposed method significantly reduces computational demands while offering robust segmentation performance, suggesting its suitability for applications requiring real-time processing or those constrained by computational resources. The removal of optical flow prerequisites reflects a promising direction for future video segmentation models that balance accuracy with efficiency.

From a theoretical perspective, TMANet exemplifies how integrating memory networks and self-attention can advance current understanding and performance in video processing tasks. Continued research could explore further computational optimizations and extensions to handle larger and more complex datasets, suggesting potential applications in augmented reality or autonomous navigation where precise real-time video segmentation is crucial.

The researchers have laid a foundation for exploring memory attention techniques, possibly spurring development of new variants tailored for specialized domains within computer vision. As semantic segmentation finds broader applications, the relevance and efficiency of approaches like TMANet will become increasingly critical, fostering further innovations in AI that capitalize on temporal dynamics.