IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
Binarized neural networks (BNNs) have drawn significant attention in recent years, owing to great potential in reducing computation and storage consumption. While it is attractive, traditional BNNs usually suffer from slow convergence speed and drama...
IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
Dynamic neural network is an emerging research topic in deep learning. Compared to static models which have fixed computational graphs and parameters at the inference stage, dynamic networks can adapt their structures or parameters to different input...
IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
When viewing omnidirectional images (ODIs), viewers can access different viewports via head movement (HM), which sequentially forms head trajectories in spatial-temporal domain. Thus, head trajectories play a key role in modeling human attention on O...
IEEE transactions on pattern analysis and machine intelligence
Oct 4, 2022
Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples. Research has fragmented into various interconnected approaches, each of which make trade-offs including run-time, diversit...
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...