2 citations · 3 across the 8 of their papers we have counts for
8 papers
Scale-Aware Pretraining of Time Series Foundation Models via Multi-Patch Token Alignment and Hybrid Masking
Taihua Chen, Xiang Ma, Yixin Zhang +3
Pretraining time series foundation models across heterogeneous datasets necessitates effective handling of varying sampling frequencies. Current methods either employ dataset-speci…
Aligning the True Semantics: Constrained Decoupling and Distribution Sampling for Cross-Modal Alignment
Xiang Ma, Lexin Fang, Litian Xu +1
Cross-modal alignment is a crucial task in multimodal learning aimed at achieving semantic consistency between vision and language. This requires that image-text pairs exhibit simi…
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…
MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting
Tianlong Zhao, Xiang Ma, Xuemei Li +1
Time series forecasting has received wide interest from existing research due to its broad applications and inherent challenging. The research challenge lies in identifying effecti…