7 citations · 8 across the 7 of their papers we have counts for
6 papers · 1 filter
MUSE: Resolving Manifold Misalignment in Visual Tokenization via Topological Orthogonality
Panqi Yang, Haodong Jing, Jiahao Chao +5
Unified visual tokenization faces a fundamental trade-off between high-fidelity pixel reconstruction (spatial equivariance) and semantic abstraction (conceptual invariance). We att…
UniHOI: Unified Human-Object Interaction Understanding via Unified Token Space
Panqi Yang, Haodong Jing, Nanning Zheng +1
In the field of human-object interaction (HOI), detection and generation are two dual tasks that have traditionally been addressed separately, hindering the development of comprehe…
EVA: Mixture-of-Experts Semantic Variant Alignment for Compositional Zero-Shot Learning
Xiao Zhang, Yongqiang Ma, Haodong Jing +1
Compositional Zero-Shot Learning (CZSL) investigates compositional generalization capacity to recognize unknown state-object pairs based on learned primitive concepts. Existing CZS…
See Through Their Minds: Learning Transferable Neural Representation from Cross-Subject fMRI
Yulong Liu, Yongqiang Ma, Guibo Zhu +2
Deciphering visual content from functional Magnetic Resonance Imaging (fMRI) helps illuminate the human vision system. However, the scarcity of fMRI data and noise hamper brain dec…
BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding
Yulong Liu, Yongqiang Ma, Wei Zhou +2
Due to the lack of paired samples and the low signal-to-noise ratio of functional MRI (fMRI) signals, reconstructing perceived natural images or decoding their semantic contents fr…
A Novel Brain Decoding Method: a Correlation Network Framework for Revealing Brain Connections
Siyu Yu, Nanning Zheng, Yongqiang Ma +2
Brain decoding is a hot spot in cognitive science, which focuses on reconstructing perceptual images from brain activities. Analyzing the correlations of collected data from human…