Continual Semantic Segmentation with Backward Background Shift Awareness

Core Concepts

A framework for continual semantic segmentation that effectively detects and mitigates the backward background shift to improve both the plasticity and stability of the network.

Abstract

The paper proposes a framework called Background Aware Continual Semantic Segmentation (BACS) to address three key challenges in continual semantic segmentation: Catastrophic forgetting: BACS uses masked knowledge distillation and dark experience replay to mitigate catastrophic forgetting. Background shift: BACS introduces a Backward Background Shift Detector (BACS) to identify pixels belonging to previously observed classes that have collapsed into the background. This information is then used to down-weight the loss for such pixels. Initialization of new class heads: BACS uses a transformer-based decoder that can dynamically extend to new classes without the need for initializing new classification heads. The key components of BACS are: Backward Background Shift Detector (BACS): This module compares the latent representation of each pixel with stored prototypes of previous foreground classes to detect if the pixel belongs to an old class that has collapsed into the background. Background-aware loss function: The cross-entropy loss is modified to down-weight the contribution of pixels detected as belonging to old classes by the BACS module. Masked knowledge distillation: Knowledge distillation is performed only on the features of pixels detected as belonging to old classes by BACS, to preserve the learned representations. Transformer-based decoder: A transformer-based decoder is used, which can dynamically extend to accommodate new classes without the need for initializing new classification heads. The proposed BACS framework is evaluated on the challenging overlap mode of continual semantic segmentation on the Pascal VOC and Cityscapes datasets. BACS outperforms existing state-of-the-art methods, especially in setups starting with a small number of classes.

Stats

The model is evaluated on the Pascal VOC and Cityscapes datasets. The most challenging overlap mode is considered, which involves both forward and backward background shifts. Experiments are conducted on various setups, including starting with as few as 2 or 5 classes and incrementing by 1 or 3 classes.

Quotes

"One of the main challenges of class-incremental learning is the catastrophic forgetting of previously observed data points distributions while learning distributions with data from new classes." "We distinguish between two types of background shifts, forward and backward. In the forward background shift, the distribution of the background class is shifting towards the current step's new classes. On the other hand, in the backward background shift, the distribution of old classes is moving to the current step's background class."

Key Insights Distilled From

BACS: Background Aware Continual Semantic Segmentation

by Mostafa ElAr... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13148.pdf

Deeper Inquiries

How can the proposed BACS framework be extended to handle more complex scenarios, such as when the background class contains a mixture of old and new classes

The BACS framework can be extended to handle scenarios where the background class contains a mixture of old and new classes by incorporating a more sophisticated background shift detection mechanism. One approach could involve enhancing the backward background shift detector to not only differentiate between the actual background and foreground classes but also to identify pixels that belong to a mixture of old and new classes. This can be achieved by training the detector to recognize patterns in the latent space that indicate a combination of features from both old and new classes. By adjusting the training process and loss function to account for this mixed scenario, the model can learn to distinguish between these complex cases and prevent the collapse of old and new classes into the background label.

What other techniques, beyond knowledge distillation and experience replay, could be explored to further improve the stability-plasticity trade-off in continual semantic segmentation

Beyond knowledge distillation and experience replay, several other techniques can be explored to further improve the stability-plasticity trade-off in continual semantic segmentation. One approach is to incorporate regularization methods that penalize significant changes in the model parameters during training on new tasks. Techniques like Elastic Weight Consolidation (EWC) or Synaptic Intelligence can help constrain the learning process to retain important information from previous tasks while adapting to new data. Additionally, techniques such as parameter isolation, where specific parameters are dedicated to certain tasks and not updated during new task learning, can help maintain stability in the model. Continual learning strategies like Gradient Episodic Memory (GEM) or Progressive Neural Networks (PNN) can also be beneficial in preserving previously learned knowledge while adapting to new tasks.

How can the BACS framework be adapted to handle continual learning in other computer vision tasks, such as object detection or instance segmentation

Adapting the BACS framework to handle continual learning in other computer vision tasks, such as object detection or instance segmentation, involves modifying the architecture and loss functions to suit the specific requirements of these tasks. For object detection, the framework can be extended by incorporating region-based detectors like Faster R-CNN or YOLO, where the model needs to detect and classify objects within an image. The background shift detection mechanism in BACS can be adjusted to account for the presence of multiple objects and their interactions in the scene. In the case of instance segmentation, where the task involves identifying and delineating individual instances of objects, the framework can be enhanced to segment each object separately while considering the background shift challenges. By customizing the model architecture and loss functions to address the unique characteristics of object detection and instance segmentation tasks, the BACS framework can effectively support continual learning in these domains.