Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding

Recent trends in image understanding have pushed for holistic scene understanding models that jointly reason about various tasks such as object detection, scene recognition, shape analysis, contextual reasoning, and local appearance based classifiers. In this work, we are interested in understanding the roles of these different tasks in improved scene understanding, in particular semantic segmentation, object detection and scene recognition. Towards this goal, we "plug-in" human subjects for each of the various components in a state-of-the-art conditional random field model. Comparisons among various hybrid human-machine CRFs give us indications of how much "head room" there is to improve scene understanding by focusing research efforts on various individual tasks

Deep Structured Multi-Task Learning for Computer Vision in Autonomous Driving

The field of computer vision is currently dominated by deep learning advances. Convolutional Neural Networks (CNNs) have become the predominant tool for solving almost any computer vision task, so state-of-the-art systems have been built by using the predictive capabilities of Convolutional Neural Networks (CNNs). Many of those systems use simple encoder–decoder based design, where an off-the-shelf CNN architecture is combined with a task-specific decoder and loss function in order to create an end-to-end trainable model. This ultimately raises the question of whether these kinds of models are the future of computer vision. In this thesis we argue that this is not the case. We start off by discussing three limitations of simple end-to-end training. We proceed by showing how it is possible to overcome those limitations by using an approach that we call structured modelling. The idea is to use CNNs to compute a rich semantic intermediate representation which is then used to solve the actual problem by applying a geometric and task-related structure. In this work we solve the localization, segmentation and landmark recognition task using structured modelling, and we show that this approach can improve generalization, interpretability and robustness. We also discuss how this approach is particularly useful for real-time applications such as autonomous driving. Visual perception is a multi-module problem that requires several different computer vision tasks to be solved. We discuss how, by sharing computations, we can improve not only the inference speed but also the prediction performance by using the structural relationship between the tasks. Lastly, we demonstrate that structured modelling is able to achieve state-of-the-art performance, making it a very relevant approach for solving current and future computer vision problems.Trinity College, ESPCR, Qualcom

Efficient Hybrid Transformer: Learning Global-local Context for Urban Scene Segmentation

Semantic segmentation of fine-resolution urban scene images plays a vital role in extensive practical applications, such as land cover mapping, urban change detection, environmental protection and economic assessment. Driven by rapid developments in deep learning technologies, the convolutional neural network (CNN) has dominated the semantic segmentation task for many years. Convolutional neural networks adopt hierarchical feature representation, demonstrating strong local information extraction. However, the local property of the convolution layer limits the network from capturing global context that is crucial for precise segmentation. Recently, Transformer comprise a hot topic in the computer vision domain. Transformer demonstrates the great capability of global information modelling, boosting many vision tasks, such as image classification, object detection and especially semantic segmentation. In this paper, we propose an efficient hybrid Transformer (EHT) for real-time urban scene segmentation. The EHT adopts a hybrid structure with and CNN-based encoder and a transformer-based decoder, learning global-local context with lower computation. Extensive experiments demonstrate that our EHT has faster inference speed with competitive accuracy compared with state-of-the-art lightweight models. Specifically, the proposed EHT achieves a 66.9% mIoU on the UAVid test set and outperforms other benchmark networks significantly. The code will be available soon