Recently, VIPL's paper on robust SVP
prediction was accepted by IEEE TPAMI. IEEE PAMI, i.e, IEEE Transactions on
Pattern Analysis and Machine Intelligence is a CCF-ranked-A top-tier Artificial
Intelligence journal with a high IF score as 17.86. Below is a brief
introduction of this work.
Qianqian Xu, Zhiyong Yang, Yangbangyan Jiang，Xiaochun Cao，Yuan Yao， Qingming Huang*. “Not All Samples are Trustworthy: Towards Deep
Robust SVP Prediction”, IEEE Transactions on Pattern Analysis and Machine
Intelligence (TPAMI), 2020. (Accepted)
In this paper, we study the problem of
estimating subjective visual properties (SVP) for images, which is an emerging task
in Computer Vision. Generally speaking, collecting SVP datasets involves a
crowdsourcing process where annotations are obtained from a wide range of
online users. Since the process is done without quality control, SVP datasets
are known to suffer from noise. This leads to the issue that not all samples
are trustworthy. Facing this problem, we need to develop robust models for
learning SVP from noisy crowdsourced annotations. In this paper, we construct
two general robust learning frameworks for this application. Specifically, in
the first framework, we propose a probabilistic framework to explicitly model
the sparse unreliable patterns that exist in the dataset. It is noteworthy that
we then provide an alternative framework that could reformulate the sparse
unreliable patterns as a “contraction” operation over the original loss
function. The latter framework leverages not only efficient end-to-end training
but also rigorous theoretical analyses. To apply these frameworks, we further
provide two models as implementations of the frameworks, where the sparse noise
parameters could be interpreted with the HodgeRank theory. Finally, extensive
theoretical and empirical studies show the effectiveness of our proposed
framework.
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