IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
In this article, we model a set of pixelwise object segmentation tasks - automatic video segmentation (AVS), image co-segmentation (ICS) and few-shot semantic segmentation (FSS) - in a unified view of segmenting objects from relational visual data. T...
IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. As GNNs tightly entangle the input graph into the neural network structure, common explainable AI approaches are not applicable. To a large extent, GNNs have re...
IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
This paper introduces versatile filters to construct efficient convolutional neural networks that are widely used in various visual recognition tasks. Considering the demands of efficient deep learning techniques running on cost-effective hardware, a...
IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
Spiking neural networks (SNNs) have shown clear advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency, due to their event-driven nature and sparse communication. However, the training of deep ...
IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
Pretrained deep models hold their learnt knowledge in the form of model parameters. These parameters act as "memory" for the trained models and help them generalize well on unseen data. However, in absence of training data, the utility of a trained m...
IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
Driven by the outstanding performance of neural networks in the structured euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The graph is leveraged at each layer of the...
IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
Conventional machine learning algorithms suffer the problem that the model trained on existing data fails to generalize well to the data sampled from other distributions. To tackle this issue, unsupervised domain adaptation (UDA) transfers the knowle...
IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
CNN-based salient object detection (SOD) methods achieve impressive performance. However, the way semantic information is encoded in them and whether they are category-agnostic is less explored. One major obstacle in studying these questions is the f...
IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
Classification of 3D objects - the selection of a category in which each object belongs - is of great interest in the field of machine learning. Numerous researchers use deep neural networks to address this problem, altering the network architecture ...
IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but different well-labeled source domain to a new unlabeled target domain. Most existing UDA methods require access to the source data, and thus are not applicable when th...