Recent Team Achievements - Projection Module for Small Sample Image Processing Based on Shape Space Theory

Our team published the paper “A projection module based on the shape space theory for small-sample image processing”. The School of Computer Engineering and Science of Shanghai University is the first unit of the paper, Hu Gan is the first author, and Han Yuexing is the corresponding author.

The “pre-training” plus “fine-tuning” paradigm provides an effective tool for neural network image processing with limited datasets. This approach compensates for the lack of information in small target datasets by pre-training models on large source datasets. However, when the target dataset is further reduced to a small sample size, the existing method is difficult to maintain the performance of the migration model, resulting in a sharp deterioration of the effect. To overcome this shortcoming, this paper proposes PMSS, a projection module based on shape space theory, to enhance the capability of migration models in small sample scenarios.

We first pre-train the model on the source dataset and save it, and subsequently extract features from the target dataset using the pre-trained model. These features, which are originally in Euclidean space, are projected to the pre-shape space for subsequent training via PMSS. In addition, a class-aware attention mechanism is introduced during the learning process to enhance the model’s ability to handle small-sample tasks through enhanced feature representation. Extensive experiments on 10 backbone networks and 5 datasets demonstrate the effectiveness of PMSS: 6%, 8%, and 13% accuracy improvements are achieved on CIFAR10, CIFAR100, and their small-sample subsets, respectively.PMSS adopts a plug-and-play design, which allows it to be directly applied to real-world finite-data systems without the need to change the network architecture. PMSS achieves state-of-the-art performance on small-sample tasks compared to the latest stream learning methods and robust migration learning methods.

Essay: A projection module based on the shape space theory for small-sample image processing