Constrained Convolutional Neural Networks for Weakly Supervised Segmentation (1506.03648v2)

Abstract: We present an approach to learn a dense pixel-wise labeling from image-level tags. Each image-level tag imposes constraints on the output labeling of a Convolutional Neural Network (CNN) classifier. We propose Constrained CNN (CCNN), a method which uses a novel loss function to optimize for any set of linear constraints on the output space (i.e. predicted label distribution) of a CNN. Our loss formulation is easy to optimize and can be incorporated directly into standard stochastic gradient descent optimization. The key idea is to phrase the training objective as a biconvex optimization for linear models, which we then relax to nonlinear deep networks. Extensive experiments demonstrate the generality of our new learning framework. The constrained loss yields state-of-the-art results on weakly supervised semantic image segmentation. We further demonstrate that adding slightly more supervision can greatly improve the performance of the learning algorithm.

• The paper introduces Constrained CNN (CCNN), a novel method for weakly supervised semantic segmentation using image-level tags instead of expensive pixel-level annotations.
• CCNN proposes a new constrained loss function enabling CNN optimization under linear constraints derived from weak supervision, naturally integrated into SGD.
• Experiments on Pascal VOC 2012 show CCNN achieves state-of-the-art weakly supervised results, demonstrating high accuracy (IoU) and scalability for real-world applications.

Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

The paper "Constrained Convolutional Neural Networks for Weakly Supervised Segmentation" introduces a novel method named Constrained CNN (CCNN) aimed at improving the semantic segmentation performance of convolutional neural networks when only weak supervision is available. Traditional approaches often heavily rely on fully supervised data which involves dense annotations on pixel level, constituting a significant bottleneck due to the high labeling costs involved. This paper diverges from that paradigm by focusing on developing methodologies utilizing weaker forms of supervision, specifically image-level tags, instead of requiring a full pixel-level annotated dataset.

The central contribution of the paper is the formulation of a new loss function that efficiently optimizes a CNN under a set of linear constraints. These constraints are imposed by the weaker supervision in the form of image-level tags, which offers significant cost savings and scalability benefits. The approach reimagines the problem as constituting biconvex optimization for linear models, later adapting it for non-linear deep networks. The non-convex optimization challenge is addressed through a novel constrained loss integrated naturally into stochastic gradient descent (SGD) procedures.

The constrained loss function effectively establishes constraints on network outputs, particularly in terms of expected label distribution. Here, the introduction of a latent "ground truth" distribution comes to the fore, ensuring this latent distribution conforms to specified label constraints, thereby simplifying optimization. This clever scheme allows the model to learn pixel-wise labels despite the sparse availability of such information, only relying on presence-absence cues inherent in image-level tags.

Extensive experiments on the Pascal VOC 2012 dataset are presented, showcasing CCNN's capacity to yield state-of-the-art results in weakly supervised segmentation tasks. The empirical evaluation demonstrates substantial accuracy as measured by Intersection over Union (IoU), outperforming other contemporary weakly supervised methods. Moreover, the paper indicates that adding minimal additional supervision, such as rough object size details, can further uplift the segmentation performance, emphasizing the method's scalability and adaptability.

From a theoretical standpoint, the paper bridges the constraint satisfaction paradigm with neural network optimization, extending insights into how arbitrary linear constraints can govern neural network learning processes. Practically, this bears implications for real-world applications where obtaining exhaustive labels is infeasible. By lowering dependency on pixel-level annotations, the model naturally aligns with settings seen in large-scale deployments where labeling is sparse, inconsistent, or partially absent.

Future directions for research as extrapolated from the paper's findings include exploring other forms of weak supervision and refining the approach to reduce sensitivity under different conditions of supervision and dataset characteristics. Integrating CCNN with additional network architectures or exploring alternative loss formulations may also uncover further opportunities for accelerating the performance of weakly supervised learning in segmentation tasks. This work opens avenues for augmenting machine learning models in environments that prioritize cost-effectiveness and scalability over dense annotation fidelity.