6 Result(s)
within
Chapter
HongGuang Zhang
Image Processing and Computer Vision
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Chapter and Conference Paper
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer
Current few-shot learning models capture visual object relations in the so-called meta-learning setting under a fixed-resolution input. However, such models have a limited generalization ability under the scal...
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Chapter and Conference Paper
Fine-Grained Video Deblurring with Event Camera
Despite CNN-based deblurring models have shown their superiority on solving motion blurs, how to restore photorealistic images from severe motion blurs remains an ill-posed problem due to the loss of temporal ...
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Chapter and Conference Paper
ANF: Attention-Based Noise Filtering Strategy for Unsupervised Few-Shot Classification
How to learn concepts from few-shot samples remains an open challenge in the deep learning era. The previous meta-learning methods require a large number of annotated samples in the training phase, which still...
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Chapter and Conference Paper
Few-Shot Action Recognition with Permutation-Invariant Attention
Many few-shot learning models focus on recognising images. In contrast, we tackle a challenging task of few-shot action recognition from videos. We build on a C3D encoder for spatio-temporal video blocks to ca...
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Chapter and Conference Paper
Model Selection for Generalized Zero-Shot Learning
In the problem of generalized zero-shot learning, the datapoints from unknown classes are not available during training. The main challenge for generalized zero-shot learning is the unbalanced data distributio...
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Chapter and Conference Paper
Museum Exhibit Identification Challenge for the Supervised Domain Adaptation and Beyond
We study an open problem of artwork identification and propose a new dataset dubbed Open Museum Identification Challenge (Open MIC). It contains photos of exhibits captured in 10 distinct exhibition spaces of ...
