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Found 8 papers, 2 papers with code
HyperEditor: Achieving Both Authenticity and Cross-Domain Capability in Image Editing via Hypernetworks
Editing real images authentically while also achieving cross-domain editing remains a challenge.
Chaos to Order: A Label Propagation Perspective on Source-Free Domain Adaptation
Inspired by this insight, we present Chaos to Order (CtO), a novel approach for SFDA that strives to constrain semantic credibility and propagate label information among target subpopulations.
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification
We construct a prime-dual network structure for few-shot learning which establishes a commutative relationship between the support set and the query set, as well as a new self- supervision constraint for highly effective few-shot learning.
Invariance-Guided Feature Evolution for Few-Shot Learning
Few-shot learning (FSL) aims to characterize the inherent visual relationship between support and query samples which can be well generalized to unseen classes so that we can accurately infer the labels of query samples from very few support samples.
Dual-Attention Enhanced BDense-UNet for Liver Lesion Segmentation
In this work, we propose a new segmentation network by integrating DenseUNet and bidirectional LSTM together with attention mechanism, termed as DA-BDense-UNet.
Spatial Assembly Networks for Image Representation Learning
It has been long recognized that deep neural networks are sensitive to changes in spatial configurations or scene structures.
Inner-Imaging Networks: Put Lenses into Convolutional Structure
To deal with these problems, a novel Inner-Imaging architecture is proposed in this paper, which allows relationships between channels to meet the above requirement.
Metabolize Neural Network
It is self-evident that the significance of metabolize neuronal network(MetaNet) in model construction.
