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Chapter and Conference Paper
Learning Instance and Task-Aware Dynamic Kernels for Few-Shot Learning
Learning and generalizing to novel concepts with few samples (Few-Shot Learning) is still an essential challenge to real-world applications. A principle way of achieving few-shot learning is to realize a model...
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Chapter and Conference Paper
Blind Image Decomposition
We propose and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components i...
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Chapter and Conference Paper
Channel Recurrent Attention Networks for Video Pedestrian Retrieval
Full attention, which generates an attention value per element of the input feature maps, has been successfully demonstrated to be beneficial in visual tasks. In this work, we propose a fully attentional netwo...
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Chapter and Conference Paper
An Objects Detection Framework in UAV Videos
Unmanned aerial vehicles equipped with surveillance system have begun to play an increasingly important role in recent years, which has provided a wealth of valuable information for national security and defen...