4 research outputs found
Learning Cross-domain Semantic-Visual Relation for Transductive Zero-Shot Learning
Zero-Shot Learning (ZSL) aims to learn recognition models for recognizing new
classes without labeled data. In this work, we propose a novel approach dubbed
Transferrable Semantic-Visual Relation (TSVR) to facilitate the cross-category
transfer in transductive ZSL. Our approach draws on an intriguing insight
connecting two challenging problems, i.e. domain adaptation and zero-shot
learning. Domain adaptation aims to transfer knowledge across two different
domains (i.e., source domain and target domain) that share the identical
task/label space. For ZSL, the source and target domains have different
tasks/label spaces. Hence, ZSL is usually considered as a more difficult
transfer setting compared with domain adaptation. Although the existing ZSL
approaches use semantic attributes of categories to bridge the source and
target domains, their performances are far from satisfactory due to the large
domain gap between different categories. In contrast, our method directly
transforms ZSL into a domain adaptation task through redrawing ZSL as
predicting the similarity/dissimilarity labels for the pairs of semantic
attributes and visual features. For this redrawn domain adaptation problem, we
propose to use a domain-specific batch normalization component to reduce the
domain discrepancy of semantic-visual pairs. Experimental results over diverse
ZSL benchmarks clearly demonstrate the superiority of our method
WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting
Recent CNN and Transformer-based models tried to utilize frequency and
periodicity information for long-term time series forecasting. However, most
existing work is based on Fourier transform, which cannot capture fine-grained
and local frequency structure. In this paper, we propose a Wavelet-Fourier
Transform Network (WFTNet) for long-term time series forecasting. WFTNet
utilizes both Fourier and wavelet transforms to extract comprehensive
temporal-frequency information from the signal, where Fourier transform
captures the global periodic patterns and wavelet transform captures the local
ones. Furthermore, we introduce a Periodicity-Weighted Coefficient (PWC) to
adaptively balance the importance of global and local frequency patterns.
Extensive experiments on various time series datasets show that WFTNet
consistently outperforms other state-of-the-art baseline