Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation (2112.08996v1)

Abstract: Image-level weakly supervised semantic segmentation (WSSS) is a fundamental yet challenging computer vision task facilitating scene understanding and automatic driving. Most existing methods resort to classification-based Class Activation Maps (CAMs) to play as the initial pseudo labels, which tend to focus on the discriminative image regions and lack customized characteristics for the segmentation task. To alleviate this issue, we propose a novel activation modulation and recalibration (AMR) scheme, which leverages a spotlight branch and a compensation branch to obtain weighted CAMs that can provide recalibration supervision and task-specific concepts. Specifically, an attention modulation module (AMM) is employed to rearrange the distribution of feature importance from the channel-spatial sequential perspective, which helps to explicitly model channel-wise interdependencies and spatial encodings to adaptively modulate segmentation-oriented activation responses. Furthermore, we introduce a cross pseudo supervision for dual branches, which can be regarded as a semantic similar regularization to mutually refine two branches. Extensive experiments show that AMR establishes a new state-of-the-art performance on the PASCAL VOC 2012 dataset, surpassing not only current methods trained with the image-level of supervision but also some methods relying on stronger supervision, such as saliency label. Experiments also reveal that our scheme is plug-and-play and can be incorporated with other approaches to boost their performance.

• The paper presents an AMR scheme that improves segmentation accuracy by combining spotlight and compensation branches for refined image-level supervision.
• A dual-branch design, featuring an attention modulation module, recalibrates feature activations to overcome the shortcomings of conventional CAM methods.
• Experimental results on PASCAL VOC 2012 show the method outperforms existing weakly supervised techniques, offering a plug-and-play solution for segmentation tasks.

Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation

In exploring the domain of weakly supervised semantic segmentation (WSSS), the challenges inherent in image-level supervision have arrested much attention within the computer vision and artificial intelligence research community. The paper "Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation" presents a detailed exploration of this issue, introducing a novel methodology for enhancing semantic segmentation tasks through a specially designed activation modulation and recalibration (AMR) scheme.

At the core of semantic segmentation is the need to perform pixel-level predictions that cluster segments of an image to a predefined object class. Traditional approaches in semantic segmentation often rely on fully supervised settings, demanding significant manual effort for granular pixel-level annotations. This necessitates the exploration of methods that leverage less precise supervision, such as bounding boxes or scribbles. In this paper, the authors focus on image-level supervision as a viable and efficient way to scale semantic segmentation systems, especially in practical scenarios where labels are readily available yet coarse.

The methodology proposed by the authors hinges upon the construction of Class Activation Maps (CAMs) from classification networks to serve as initial pseudo labels for segmentation tasks. Conventional approaches fall short because CAMs usually highlight the most discriminative regions, missing less conspicuous but contextually relevant portions of target objects. To navigate this limitation, the AMR scheme introduces two novel branches: a spotlight branch and a compensation branch, facilitating the creation of weighted CAMs capable of task-specific concept recalibration for segmentation.

The spotlight branch aligns with traditional practices, focusing on discriminative regions that CAMs commonly represent. In contrast, the compensation branch offers innovative auxiliary supervision, digging out critical regions that might be overlooked. This pivotal branch utilizes an attention modulation module (AMM) that rearranges feature importance distributions from a channel-spatial perspective, thereby accentuating once-minute segmentation-oriented activations which typically go unnoticed.

Critically, the AMR scheme features a cross pseudo supervision mechanism between the branches, acting as a semantic similar regularization that refines the spotlight and compensation branches synergistically. The experiments conducted reveal that AMR achieves notable results, setting a new performance benchmark on the PASCAL VOC 2012 dataset. Impressively, it not only surpasses existing methods reliant on image-level supervision but competes favorably against those deploying stronger supervisory signals, such as saliency labels.

The implications of this research extend both practical and theoretical elements of AI. Practically, the plug-and-play nature of the AMR scheme suggests it can be smoothly integrated into existing systems, potentially boosting segmentation accuracy without the costly overhead associated with fine-grained annotations. Theoretically, this work opens up avenues for further exploration into weak supervision methods that intelligently leverage compensation mechanisms to offset deficiencies in traditional CAM approaches.

Future developments in AI may witness a broader adoption of similar compensation-based approaches across various modalities of supervision and segmentation tasks, pushing boundaries not just in image retrieval, automatic driving, and video understanding, but also in nuanced areas of scene understanding which demand sophisticated feature interpretations and segmentation granularity.

In summation, this paper presents a sophisticated and comprehensive methodology for advancing the state of semantic segmentation under weakly supervised settings, making valuable contributions to both the efficiency and efficacy of computer vision systems.