1 citations · 1 across the 5 of their papers we have counts for
5 papers
ReCast: Reliability-aware Codebook Assisted Lightweight Time Series Forecasting
Xiang Ma, Taihua Chen, Pengcheng Wang +2
Time series forecasting is crucial for applications in various domains. Conventional methods often rely on global decomposition into trend, seasonal, and residual components, which…
Reliable Cross-modal Alignment via Prototype Iterative Construction
Xiang Ma, Litian Xu, Lexin Fang +2
Cross-modal alignment is an important multi-modal task, aiming to bridge the semantic gap between different modalities. The most reliable fundamention for achieving this objective…
Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation
Lexin Fang, Yunyang Xu, Xiang Ma +2
Deep learning has achieved significant advancements in medical image segmentation, but existing models still face challenges in accurately segmenting lesion regions. The main reaso…
Bridging the Modality Gap: Dimension Information Alignment and Sparse Spatial Constraint for Image-Text Matching
Xiang Ma, Xuemei Li, Lexin Fang +1
Many contrastive learning based models have achieved advanced performance in image-text matching tasks. The key of these models lies in analyzing the correlation between image-text…
U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting
Xiang Ma, Xuemei Li, Lexin Fang +2
Time series forecasting is a crucial task in various domains. Caused by factors such as trends, seasonality, or irregular fluctuations, time series often exhibits non-stationary. I…