From the 1 of 6 linked papers with an AI index.
6 papers
Towards Spatial Supersensing in the Wild
Tianjun Gu, Tianyu Xin, Kuan Zhang +12
The paper introduces VSI‑Super‑Wild, a large benchmark of real‑world long videos with human‑verified QA pairs to evaluate how well multimodal models can track and reason about agen…
Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse
Kuan Zhang, Dongchen Liu, Qiyue Zhao +12
The real world unfolds along a single set of physics laws, yet human intelligence demonstrates a remarkable capacity to generalize experiences from this singular physical existence…
Training and Inference within 1 Second -- Tackle Cross-Sensor Degradation of Real-World Pansharpening with Efficient Residual Feature Tailoring
Tianyu Xin, Jin-Liang Xiao, Zeyu Xia +2
Deep learning methods for pansharpening have advanced rapidly, yet models pretrained on data from a specific sensor often generalize poorly to data from other sensors. Existing met…
SWIFT: A General Sensitive Weight Identification Framework for Fast Sensor-Transfer Pansharpening
Zeyu Xia, Chenxi Sun, Tianyu Xin +3
Pansharpening aims to fuse high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) images to generate high-resolution multispectral (HRMS) images. Althou…
Is Your LLM-Based Multi-Agent a Reliable Real-World Planner? Exploring Fraud Detection in Travel Planning
Junchi Yao, Jianhua Xu, Tianyu Xin +4
The rise of Large Language Model-based Multi-Agent Planning has leveraged advanced frameworks to enable autonomous and collaborative task execution. Some systems rely on platforms…
CAT: A Conditional Adaptation Tailor for Efficient and Effective Instance-Specific Pansharpening on Real-World Data
Tianyu Xin, Jin-Liang Xiao, Zeyu Xia +2
Pansharpening is a crucial remote sensing technique that fuses low-resolution multispectral (LRMS) images with high-resolution panchromatic (PAN) images to generate high-resolution…