An essential task for computer vision-based assistive technologies is to help visually impaired people to recognize objects in constrained environments, for instance, recognizing food items in grocery stores. In this paper, we introduce a novel dataset with natural images of groceries—fruits, vegetables, and packaged products—where all images have been taken inside grocery stores to resemble a shopping scenario. Additionally, we download iconic images and text descriptions for each item that can be utilized for better representation learning of groceries. We select a multi-view generative model, which can combine the different item information into lower-dimensional representations. The experiments show that utilizing the additional information yields higher accuracies on classifying grocery items than only using the natural images. We observe that iconic images help to construct representations separated by visual differences of the items, while text descriptions enable the model to distinguish between visually similar items by different ingredients.

Image classification models built into visual support systems and other assistive devices need to provide accurate predictions about their environment. We focus on an application of assistive technology for people with visual impairments, for daily activities such as shopping or cooking. In this paper, we provide a new benchmark dataset for a challenging task in this application – classification of fruits, vegetables, and refrigerated products, e.g. milk packages and juice cartons, in grocery stores. To enable the learning process to utilize multiple sources of structured information, this dataset not only contains a large volume of natural images but also includes the corresponding information of the product from an online shopping website. Such information encompasses the hierarchical structure of the object classes, as well as an iconic image of each type of object. This dataset can be used to train and evaluate image classification models for helping visually impaired people in natural environments. Additionally, we provide benchmark results evaluated on pretrained convolutional neural networks often used for image understanding purposes, and also a multi-view variational autoencoder, which is capable of utilizing the rich product information in the dataset.

Replay-based continual learning have shown to be successful in mitigating catastrophic forgetting despite having limited access to historical data. However, storing historical data is cheap in many real-world applications, yet replaying all seen data would be prohibited due to processing time constraints. In such settings, we propose learning the time to learn for a continual learning system, in which we learn replay schedules over which tasks to replay at different time steps. To demonstrate the importance of learning the time to learn, we use Monte Carlo tree search in an ideal continual learning scenario to find the proper replay schedule. We perform extensive evaluations to show the benefits of replay scheduling in various memory settings and in combination with different replay methods. Moreover, the results indicate that the found schedules are consistent with human learning insights. Our findings opens up for new research directions that can bring current continual learning research closer to real-world needs.

Scheduling over which tasks to select for replay at different times have been demonstrated to be important in continual learning. However, a replay scheduling policy that can be applied in any continual learning scenario is currently missing, which makes replay scheduling infeasible in real-world scenarios. To this end, we propose using reinforcement learning to enable learning policies that can be applied in new continual learning scenarios without additional computational cost. In our experiments, we show that the learned policies can propose replay schedules that efficiently mitigate catastrophic forgetting in environments with previously unseen task orders and datasets. The proposed approach opens up for new research directions in replay-based continual learning that stems well with real-world needs.