5 research outputs found
Towards holistic scene understanding:Semantic segmentation and beyond
This dissertation addresses visual scene understanding and enhances
segmentation performance and generalization, training efficiency of networks,
and holistic understanding. First, we investigate semantic segmentation in the
context of street scenes and train semantic segmentation networks on
combinations of various datasets. In Chapter 2 we design a framework of
hierarchical classifiers over a single convolutional backbone, and train it
end-to-end on a combination of pixel-labeled datasets, improving
generalizability and the number of recognizable semantic concepts. Chapter 3
focuses on enriching semantic segmentation with weak supervision and proposes a
weakly-supervised algorithm for training with bounding box-level and
image-level supervision instead of only with per-pixel supervision. The memory
and computational load challenges that arise from simultaneous training on
multiple datasets are addressed in Chapter 4. We propose two methodologies for
selecting informative and diverse samples from datasets with weak supervision
to reduce our networks' ecological footprint without sacrificing performance.
Motivated by memory and computation efficiency requirements, in Chapter 5, we
rethink simultaneous training on heterogeneous datasets and propose a universal
semantic segmentation framework. This framework achieves consistent increases
in performance metrics and semantic knowledgeability by exploiting various
scene understanding datasets. Chapter 6 introduces the novel task of part-aware
panoptic segmentation, which extends our reasoning towards holistic scene
understanding. This task combines scene and parts-level semantics with
instance-level object detection. In conclusion, our contributions span over
convolutional network architectures, weakly-supervised learning, part and
panoptic segmentation, paving the way towards a holistic, rich, and sustainable
visual scene understanding.Comment: PhD Thesis, Eindhoven University of Technology, October 202