1 citations · 1 across the 4 of their papers we have counts for
6 papers
ReMAP-PET: Beyond Visual Understanding -- Learning Region-Guided Metabolic Alignment Semantics from Brain PET
Dasen Dai, Yanteng Zhang, Shuoqi Li +6
Positron Emission Tomography (PET) reveals brain metabolism and is clinically central to neurodegenerative disease assessment, yet existing 3D brain foundation models treat PET as…
KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions
Tingyu Wu, Zhisheng Chen, Ziyan Weng +8
Existing long-horizon memory benchmarks mostly use multi-turn dialogues or synthetic user histories, which makes retrieval performance an imperfect proxy for person understanding.…
UIPress: Bringing Optical Token Compression to UI-to-Code Generation
Dasen Dai, Shuoqi Li, Ronghao Chen +3
UI-to-Code generation requires vision-language models (VLMs) to produce thousands of tokens of structured HTML/CSS from a single screenshot, making visual token efficiency critical…
Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning
Zhisheng Chen, Yingwei Zhang, Qizhen Lan +7
Current foundation models for electroencephalography (EEG) rely on architectures adapted from computer vision or natural language processing, typically treating neural signals as p…
Lost in Distortion: Uncovering the Domain Gap Between Computer Vision and Brain Imaging -- A Study on Pretraining for Age Prediction
Yanteng Zhang, Songheng Li, Zeyu Shen +4
Large-scale brain imaging datasets provide unprecedented opportunities for developing domain foundation models through pretraining. However, unlike natural image datasets in comput…
Remote Sensing-Oriented World Model
Yuxi Lu, Biao Wu, Zhidong Li +7
World models have shown potential in artificial intelligence by predicting and reasoning about world states beyond direct observations. However, existing approaches are predominant…