Scaling and Beyond: Advancing Spatial Reasoning in MLLMs Requires New Recipes
By dynamically retrieving relevant information from an external knowledge base, our TimeRAF enhances prediction accuracy, leading to more precise zero-shot forecasting performance.
Apr 21, 2025
MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?
Jan 1, 2025
Imagine while Reasoning in Space: Multimodal Visualization-of-Thought
Jan 1, 2025
TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting
By dynamically retrieving relevant information from an external knowledge base, our TimeRAF enhances prediction accuracy, leading to more precise zero-shot forecasting performance.
Dec 30, 2024
LogoRA: Local-Global Representation Alignment for Robust Time Series Classification (TKDE 2024)
We propose the Local-Global Representation Alignment framework (LogoRA), which combines multi-scale convolutional and transformer encoders, integrates representations with a fusion module, and employs advanced alignment strategies, achieving state-of-the-art performance on four time-series datasets.
Sep 30, 2024