IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
This paper tackles the problem of training a deep convolutional neural network of both low-bitwidth weights and activations. Optimizing a low-precision network is very challenging due to the non-differentiability of the quantizer, which may result in...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
Domain adaptation techniques learn transferable knowledge from a source domain to a target domain and train models that generalize well in the target domain. Unfortunately, a majority of the existing techniques are only applicable to scenarios that t...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
Current multi-object tracking and segmentation (MOTS) methods follow the tracking-by-detection paradigm and adopt 2D or 3D convolutions to extract instance embeddings for instance association. However, due to the large receptive field of deep convolu...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain, given labeled data on a source domain whose distribution diverges from the target one. Mainstream UDA methods strive t...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious c...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
Designing effective architectures is one of the key factors behind the success of deep neural networks. Existing deep architectures are either manually designed or automatically searched by some Neural Architecture Search (NAS) methods. However, even...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
In reality, learning from multi-view multi-label data inevitably confronts three challenges: missing labels, incomplete views, and non-aligned views. Existing methods mainly concern the first two and commonly need multiple assumptions to attack them,...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
We study generalization under labeled shift for categorical and general normed label spaces. We propose a series of methods to estimate the importance weights from labeled source to unlabeled target domain and provide confidence bounds for these esti...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
Mammogram mass detection is crucial for diagnosing and preventing the breast cancers in clinical practice. The complementary effect of multi-view mammogram images provides valuable information about the breast anatomical prior structure and is of gre...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
Text is a new way to guide human image manipulation. Albeit natural and flexible, text usually suffers from inaccuracy in spatial description, ambiguity in the description of appearance, and incompleteness. We in this paper address these issues. To o...