  Classifying pill categories from real-world images is crucial for various
smart healthcare applications. Although existing approaches in image
classification might achieve a good performance on fixed pill categories, they
fail to handle novel instances of pill categories that are frequently presented
to the learning algorithm. To this end, a trivial solution is to train the
model with novel classes. However, this may result in a phenomenon known as
catastrophic forgetting, in which the system forgets what it learned in
previous classes. In this paper, we address this challenge by introducing the
class incremental learning (CIL) ability to traditional pill image
classification systems. Specifically, we propose a novel incremental
multi-stream intermediate fusion framework enabling incorporation of an
additional guidance information stream that best matches the domain of the
problem into various state-of-the-art CIL methods. From this framework, we
consider color-specific information of pill images as a guidance stream and
devise an approach, namely "Color Guidance with Multi-stream intermediate
fusion"(CG-IMIF) for solving CIL pill image classification task. We conduct
comprehensive experiments on real-world incremental pill image classification
dataset, namely VAIPE-PCIL, and find that the CG-IMIF consistently outperforms
several state-of-the-art methods by a large margin in different task settings.
Our code, data, and trained model are available at
https://github.com/vinuni-vishc/CG-IMIF.
