Browsing by Author "Kutvonen, Konsta"

With modern computer vision algorithms, it is possible to solve many different kinds of problems, such as object detection, image classification, and image segmentation. In some cases, like in the case of a camera-based self-driving car, the task can't yet be adequately solved as a direct mapping from image to action with a single model. In such situations, we need more complex systems that can solve multiple computer vision tasks to understand the environment and act based on it for acceptable results. Training each task on their own can be expensive in terms of storage required for all weights and especially for the inference time as the output of several large models is needed. Fortunately, many state-of-the-art solutions to these problems use Convolutional Neural Networks and often feature some ImageNet backbone in their architecture. With multi-task learning, we can combine some of the tasks into a single model, sharing the convolutional weights in the network. Sharing the weights allows for training smaller models that produce outputs faster and require less computational resources, which is essential, especially when the models are run on embedded devices with constrained computation capability and no ability to rely on the cloud. In this thesis, we will present some state-of-the-art models to solve image classification and object detection problems. We will define multi-task learning, how we can train multi-task models, and take a look at various multi-task models and how they exhibit the benefits of multi-task learning. Finally, to evaluate how training multi-task models changes the basic training paradigm and to find what issues arise, we will train multiple multi-task models. The models will mainly focus on image classification and object detection using various data sets. They will combine multiple tasks into a single model, and we will observe the impact of training the tasks in a multi-task setting.