Learning Temporal Contexts for Efficient Video Semantic Segmentation

Conceptos Básicos

This paper proposes an efficient technique called Coarse-to-Fine Feature Mining (CFFM) to jointly learn local temporal contexts, including static and motional contexts, for video semantic segmentation. It also introduces an extension CFFM++ that further exploits global temporal contexts from the whole video to enhance the segmentation performance.

Resumen

The paper focuses on video semantic segmentation (VSS) and proposes two key techniques to effectively leverage temporal contexts:
Local Temporal Contexts:

CFFM learns a unified representation of static and motional contexts from neighboring video frames.
It uses a Coarse-to-Fine Feature Assembling (CFFA) module to organize features from nearby frames in a multi-scale manner, capturing both static and motional contexts.
The Cross-frame Feature Mining (CFM) module then mines useful contextual information from neighboring frames to enhance the target frame features.
Global Temporal Contexts:

CFFM++ extends CFFM by additionally exploiting global temporal contexts from the whole video.
It uniformly samples frames from the video, extracts global contextual prototypes using k-means clustering, and then uses CFM to refine the target frame features with the global contexts.
The proposed CFFM and CFFM++ techniques outperform state-of-the-art methods on popular VSS benchmarks, demonstrating the effectiveness of jointly learning local and global temporal contexts.

Estadísticas

The mIoU between ground-truth masks of consecutive video frames on the VSPW validation set is 89.7%, indicating high semantic consistency across neighboring frames.

Citas

None

Ideas clave extraídas de

Learning Local and Global Temporal Contexts for Video Semantic Segmentation

by Guolei Sun,Y... a las arxiv.org 04-10-2024

https://arxiv.org/pdf/2204.03330.pdf

Consultas más profundas

How can the proposed techniques be extended to other video understanding tasks beyond semantic segmentation

The proposed techniques of Coarse-to-Fine Feature Mining (CFFM) and CFFM++ can be extended to other video understanding tasks beyond semantic segmentation by adapting the model architecture and training process to suit the specific requirements of the task at hand. For tasks such as action recognition, video captioning, or anomaly detection, the model can be modified to focus on different aspects of the video data. For example, in action recognition, the model can be trained to capture temporal dependencies between actions and recognize patterns of movement. In video captioning, the model can learn to associate visual features with textual descriptions. By adjusting the input data, loss functions, and output layers, the same principles of learning local and global temporal contexts can be applied to a wide range of video understanding tasks.

How sensitive are the CFFM and CFFM++ models to the number of reference frames and the sampling rate for global context extraction

The sensitivity of the CFFM and CFFM++ models to the number of reference frames and the sampling rate for global context extraction can impact the performance and efficiency of the models.

Number of Reference Frames: The number of reference frames used in the models can affect the model's ability to capture temporal dependencies and contextual information. Increasing the number of reference frames can provide more context but may also increase computational complexity. A higher number of reference frames may lead to better performance in capturing long-range dependencies but could also introduce more noise and redundant information. It is essential to find a balance based on the specific requirements of the task and the available computational resources.

Sampling Rate for Global Context Extraction: The sampling rate for global context extraction determines how many frames are considered for extracting global temporal contexts. A higher sampling rate can provide a broader view of the video data but may also increase the computational load. A lower sampling rate may limit the model's ability to capture long-term dependencies but can improve efficiency. Finding the optimal sampling rate involves considering the trade-off between computational cost and the amount of contextual information needed for the task.
Fine-tuning these parameters through experimentation and analysis can help optimize the models for specific video understanding tasks.

Can the global temporal context prototypes be further refined during training to better capture the video-level semantics

The global temporal context prototypes extracted during training can be further refined to better capture video-level semantics by incorporating additional training strategies and loss functions.

Fine-tuning with Additional Data: By fine-tuning the model with additional video data, the global temporal context prototypes can be refined to capture a broader range of video semantics. This additional data can help the model learn more diverse patterns and improve its ability to generalize to different video contexts.

Regularization Techniques: Applying regularization techniques such as dropout, weight decay, or batch normalization during training can help prevent overfitting and improve the generalization of the global temporal context prototypes. These techniques can encourage the model to learn more robust and representative features from the video data.

Multi-Task Learning: Incorporating multi-task learning objectives can also enhance the refinement of global temporal context prototypes. By training the model on multiple related tasks simultaneously, the model can learn to extract more informative and meaningful features that capture the underlying semantics of the video data.
By implementing these strategies and fine-tuning the training process, the global temporal context prototypes can be refined to better capture video-level semantics and improve the overall performance of the model.