Semi-supervised Learning for Dense Object Detection in Retail Scenes

Abstract

Retail scenes usually contain densely packed high number of objects in each image. Standard object detection techniques use fully supervised training methodology. This is highly costly as annotating a large dense retail object detection dataset involves an order of magnitude more effort compared to standard datasets. Hence, we propose semi-supervised learning to effectively use the large amount of unlabeled data available in the retail domain. We adapt a popular self supervised method called noisy student initially proposed for object classification to the task of dense object detection. We show that using unlabeled data with the noisy student training methodology, we can improve the state of the art on precise detection of objects in densely packed retail scenes. We also show that performance of the model increases as you increase the amount of unlabeled data.

References in corpus (14)

• Bootstrap your own latent: A new approach to self-supervised Learning
• FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
• Improved Baselines with Momentum Contrastive Learning
• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
• A Survey of Deep Learning-based Object Detection
• MMDetection: Open MMLab Detection Toolbox and Benchmark
• The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale
• A Simple Semi-Supervised Learning Framework for Object Detection
• Self-training with Noisy Student improves ImageNet classification
• Learning Invariant Representations for Reinforcement Learning without Reconstruction
• Region Proposal by Guided Anchoring
• VarifocalNet: An IoU-aware Dense Object Detector
• Contextual Imagined Goals for Self-Supervised Robotic Learning
• Learning Gaussian Maps for Dense Object Detection