CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning (1903.02351v1)

Abstract: Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets. However, data labeling for pixel-wise segmentation is tedious and costly. Moreover, a trained model can only make predictions within a set of pre-defined classes. In this paper, we present CANet, a class-agnostic segmentation network that performs few-shot segmentation on new classes with only a few annotated images available. Our network consists of a two-branch dense comparison module which performs multi-level feature comparison between the support image and the query image, and an iterative optimization module which iteratively refines the predicted results. Furthermore, we introduce an attention mechanism to effectively fuse information from multiple support examples under the setting of k-shot learning. Experiments on PASCAL VOC 2012 show that our method achieves a mean Intersection-over-Union score of 55.4% for 1-shot segmentation and 57.1% for 5-shot segmentation, outperforming state-of-the-art methods by a large margin of 14.6% and 13.2%, respectively.

• The paper introduces CANet, a novel approach that employs iterative refinement and attention mechanisms to perform few-shot semantic segmentation on novel classes.
• The model integrates a Dense Comparison Module and an Iterative Optimization Module to achieve competitive IoU scores of 55.4% for 1-shot and 57.1% for 5-shot tasks.
• The design reduces reliance on extensive pixel-level annotations, offering a cost-effective and flexible solution for class-agnostic segmentation in dynamic environments.

Overview of CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning

The paper introduces CANet, a sophisticated method designed to tackle the challenges of few-shot semantic segmentation. Traditional approaches to semantic segmentation rely heavily on large datasets with pixel-level annotations, which are both time-consuming and costly to produce. Furthermore, models trained on such datasets are often limited to pre-defined classes, lacking flexibility to accommodate new objects. To address these challenges, CANet offers a class-agnostic segmentation approach that leverages few-shot learning, enabling models to perform segmentation on novel classes with minimal annotated examples.

Key Components and Methodology

CANet is built upon two primary modules: the Dense Comparison Module (DCM) and the Iterative Optimization Module (IOM). Together, these modules facilitate the segmentation process while iteratively refining the outputs for enhanced accuracy.

Dense Comparison Module (DCM):

The DCM uses a shared feature extraction backbone based on ResNet-50 to perform multi-level feature comparisons between support and query images. Rather than a simplistic pixel-by-pixel comparison, which would be computationally prohibitive, DCM employs global average pooling over the annotated foreground area in support images to filter out irrelevant background information. This allows for an efficient comparison of global representations against each position within the query image. The result is a dense metric learning extension adept at discerning similarities, crucial for few-shot learning scenarios.

Iterative Optimization Module (IOM):

Recognizing the limitations of the DCM in managing object variances within a class, IOM addresses this by iteratively refining segmentation predictions. The process leverages residual connections to integrate prior iteration predictions, thereby enhancing the model's ability to generate more accurate segmentation maps over successive cycles of optimization.

Attention Mechanism for k-Shot Learning:

In addition to 1-shot segmentation tasks, CANet extends to k-shot scenarios through a learnable attention mechanism. This mechanism adeptly fuses features from multiple support instances rather than relying on simplistic averaging strategies, resulting in superior segmentation outcomes.

Empirical Results and Evaluation

CANet's performance was validated on standard datasets such as PASCAL VOC 2012 and COCO. The results are compelling, showing a mean Intersection-over-Union (IoU) score of 55.4% for 1-shot and 57.1% for 5-shot segmentation on PASCAL VOC 2012. These scores represent a significant improvement over existing state-of-the-art methods, with margins of 14.6% and 13.2%, respectively. Additionally, even with weaker bounding box annotations, the model maintained competitive segmentation performance, showcasing its robustness and practical applicability.

Theoretical and Practical Implications

The emergence of CANet marks a notable advance in the field of semantic segmentation. Its design enables application across unseen classes without compromising on accuracy, aligning with one of few-shot learning's core principles—generalizability. Practically, this alleviates the need for extensive data labeling, making it a cost-effective solution for dynamic environments where class definitions frequently change.

Future Directions

The innovations introduced by CANet open several avenues for future research and application. Extending the concept to handle even fewer annotations or incorporating domain adaptation techniques could broaden its utility across diverse and challenging datasets. Furthermore, integrating CANet within larger multi-task learning frameworks may enrich other computer vision applications by leveraging its class-agnostic capabilities.

In conclusion, CANet represents a significant contribution to the domain of semantic segmentation, effectively bridging the gap between model specificity and the adaptable nature of human visual perception. Its approach not only improves the technical aspects of segmentation but also offers practical benefits that could reshape data-driven methodologies in visual cognition tasks.