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APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation
Jiacheng Chen, Bin-Bin Gao, Zongqing Lu +3
Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning…
Dimension Reduction for Data with Heterogeneous Missingness
Yurong Ling, Zijing Liu, Jing-Hao Xue
Dimension reduction plays a pivotal role in analysing high-dimensional data. However, observations with missing values present serious difficulties in directly applying standard di…
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments
Xiaoxu Li, Xiaochen Yang, Zhanyu Ma +1
Few-shot image classification is a challenging problem that aims to achieve the human level of recognition based only on a small number of training images. One main solution to few…
SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes
Jiacheng Chen, Bin-Bin Gao, Zongqing Lu +3
Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples in support images. Most of advanced solutions exploit a metri…
Towards Open-World Text-Guided Face Image Generation and Manipulation
Weihao Xia, Yujiu Yang, Jing-Hao Xue +1
The existing text-guided image synthesis methods can only produce limited quality results with at most \mbox{} resolution and the textual instructions are constrained…
GAN Inversion: A Survey
Weihao Xia, Yulun Zhang, Yujiu Yang +3
GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model, for the image to be faithfully reconstructed from the inverted code by the generato…