IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
Spherical images or videos, as typical non-euclidean data, are usually stored in the form of 2D panoramas obtained through an equirectangular projection, which is neither equal area nor conformal. The distortion caused by the projection limits the pe...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
Text spotting in natural scene images is of great importance for many image understanding tasks. It includes two sub-tasks: text detection and recognition. In this work, we propose a unified network that simultaneously localizes and recognizes text w...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we p...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
One of the most prominent attributes of Neural Networks (NNs) constitutes their capability of learning to extract robust and descriptive features from high dimensional data, like images. Hence, such an ability renders their exploitation as feature ex...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
In this paper, we consider how to incorporate psychophysical measurements of human visual perception into the loss function of a deep neural network being trained for a recognition task, under the assumption that such information can reduce errors. A...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
In this work, we propose a novel Convolutional Neural Network (CNN) architecture for the joint detection and matching of feature points in images acquired by different sensors using a single forward pass. The resulting feature detector is tightly cou...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
This paper serves as a survey of recent advances in large margin training and its theoretical foundations, mostly for (nonlinear) deep neural networks (DNNs) that are probably the most prominent machine learning models for large-scale data in the com...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices, because even the powerful modern mobile devices are considered as "resource-constrained" when executing large-scale DNNs. It necessitates the ...
IEEE transactions on pattern analysis and machine intelligence
Sep 14, 2022
Action segmentation is the task of predicting the actions for each frame of a video. As obtaining the full annotation of videos for action segmentation is expensive, weakly supervised approaches that can learn only from transcripts are appealing. In ...
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...