Contrastive Learning for Label-Efficient Semantic Segmentation (2012.06985v4)

Abstract: Collecting labeled data for the task of semantic segmentation is expensive and time-consuming, as it requires dense pixel-level annotations. While recent Convolutional Neural Network (CNN) based semantic segmentation approaches have achieved impressive results by using large amounts of labeled training data, their performance drops significantly as the amount of labeled data decreases. This happens because deep CNNs trained with the de facto cross-entropy loss can easily overfit to small amounts of labeled data. To address this issue, we propose a simple and effective contrastive learning-based training strategy in which we first pretrain the network using a pixel-wise, label-based contrastive loss, and then fine-tune it using the cross-entropy loss. This approach increases intra-class compactness and inter-class separability, thereby resulting in a better pixel classifier. We demonstrate the effectiveness of the proposed training strategy using the Cityscapes and PASCAL VOC 2012 segmentation datasets. Our results show that pretraining with the proposed contrastive loss results in large performance gains (more than 20% absolute improvement in some settings) when the amount of labeled data is limited. In many settings, the proposed contrastive pretraining strategy, which does not use any additional data, is able to match or outperform the widely-used ImageNet pretraining strategy that uses more than a million additional labeled images.

• The paper introduces a novel pretraining strategy using pixel-wise, label-based contrastive loss to enhance label efficiency in semantic segmentation.
• This contrastive pretraining yields significant performance gains (up to 30 percentage points) on datasets like PASCAL VOC 2012 with limited labeled data, often surpassing standard ImageNet pretraining.
• The proposed method reduces reliance on extensive pixel-level labeling, making semantic segmentation models more practical for real-world applications with constrained data.

Contrastive Learning for Label Efficient Semantic Segmentation

The paper "Contrastive Learning for Label Efficient Semantic Segmentation" introduces a methodology aimed at addressing the challenge of label efficiency in semantic segmentation, a fundamental problem in computer vision. Semantic segmentation involves partitioning an image into segments corresponding to different semantic categories. While Convolutional Neural Networks (CNNs) have achieved impressive results in semantic segmentation tasks with large amounts of labeled data, their performance significantly deteriorates when trained with limited labeled data due to overfitting challenges associated with the standard cross-entropy loss.

The authors of this paper propose a novel training strategy that leverages contrastive learning to improve label efficiency in semantic segmentation models. The strategy involves pretraining a CNN with a pixel-wise, label-based contrastive loss before fine-tuning it with a cross-entropy loss. This dual-stage approach enhances the intra-class compactness and inter-class separability, yielding better pixel classification performance.

Key Findings

• Contrastive Loss Implementation: The authors extended supervised contrastive learning to semantic segmentation by proposing three variants of pixel-wise, label-based contrastive loss: within-image loss, cross-image loss, and a batch loss variant. The within-image loss computes the loss for each pixel within the same image, whereas the cross-image loss utilizes positive samples from another image as harder positives without incorporating additional negatives.
• Performance Improvements: Utilizing the Cityscapes and PASCAL VOC 2012 datasets, the authors demonstrate that models pretrained using contrastive loss exhibit significant performance gains of up to 30 percentage points on PASCAL VOC 2012 with limited labeled data. Across various settings, the proposed contrastive pretraining matches or surpasses the traditionally employed ImageNet pretraining strategy, which uses millions of additional labeled images. For instance, training with only 1059 images can outperform a model trained on 5295 images without contrastive pretraining.
• Comparison with Other Techniques: The paper compares its approach with semi-supervised methods and region-based loss functions, showing superior label efficiency results without relying on extra data forms such as bounding boxes or image-level labels. Contrastive pretraining also proves competitive against methods employing self-supervised learning with ImageNet unlabeled data.

Implications and Future Work

This research contributes valuable insights into the effectiveness of supervised contrastive learning in enhancing CNN’s robustness against overfitting with limited labeled semantic segmentation data. Practically, the proposed method could reduce the cost and time required for collecting extensive pixel-level labeled datasets, making deep learning models more feasible for real-world applications where labeling resources are constrained.

Future research in this area could explore hybrid contrastive loss architectures that combine the benefits of pixel relationships within and across multiple images while scaling this framework to other pixel-level tasks like object detection. Additionally, a careful investigation of distortion techniques suitable for semantic segmentation, rather than image recognition, could further refine the efficacy of pretraining strategies.

In conclusion, the contrastive learning approach presented in this paper offers an effective avenue for improving semantic segmentation models under limited labeled data conditions, showing potential to shift methodologies in both academic research and industrial applications.