10,825 research outputs found
FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding
Although Domain Adaptation in Semantic Scene Segmentation has shown
impressive improvement in recent years, the fairness concerns in the domain
adaptation have yet to be well defined and addressed. In addition, fairness is
one of the most critical aspects when deploying the segmentation models into
human-related real-world applications, e.g., autonomous driving, as any unfair
predictions could influence human safety. In this paper, we propose a novel
Fairness Domain Adaptation (FREDOM) approach to semantic scene segmentation. In
particular, from the proposed formulated fairness objective, a new adaptation
framework will be introduced based on the fair treatment of class
distributions. Moreover, to generally model the context of structural
dependency, a new conditional structural constraint is introduced to impose the
consistency of predicted segmentation. Thanks to the proposed Conditional
Structure Network, the self-attention mechanism has sufficiently modeled the
structural information of segmentation. Through the ablation studies, the
proposed method has shown the performance improvement of the segmentation
models and promoted fairness in the model predictions. The experimental results
on the two standard benchmarks, i.e., SYNTHIA Cityscapes and GTA5
Cityscapes, have shown that our method achieved State-of-the-Art (SOTA)
performance.Comment: Accepted to CVPR'2
Weakly-supervised Caricature Face Parsing through Domain Adaptation
A caricature is an artistic form of a person's picture in which certain
striking characteristics are abstracted or exaggerated in order to create a
humor or sarcasm effect. For numerous caricature related applications such as
attribute recognition and caricature editing, face parsing is an essential
pre-processing step that provides a complete facial structure understanding.
However, current state-of-the-art face parsing methods require large amounts of
labeled data on the pixel-level and such process for caricature is tedious and
labor-intensive. For real photos, there are numerous labeled datasets for face
parsing. Thus, we formulate caricature face parsing as a domain adaptation
problem, where real photos play the role of the source domain, adapting to the
target caricatures. Specifically, we first leverage a spatial transformer based
network to enable shape domain shifts. A feed-forward style transfer network is
then utilized to capture texture-level domain gaps. With these two steps, we
synthesize face caricatures from real photos, and thus we can use parsing
ground truths of the original photos to learn the parsing model. Experimental
results on the synthetic and real caricatures demonstrate the effectiveness of
the proposed domain adaptation algorithm. Code is available at:
https://github.com/ZJULearning/CariFaceParsing .Comment: Accepted in ICIP 2019, code and model are available at
https://github.com/ZJULearning/CariFaceParsin
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