Zobrazeno 1 - 10
of 353
pro vyhledávání: '"He, Huiguang"'
Deep Neural Networks (DNNs) have demonstrated exceptional recognition capabilities in traditional computer vision (CV) tasks. However, existing CV models often suffer a significant decrease in accuracy when confronted with out-of-distribution (OOD) d
Externí odkaz:
http://arxiv.org/abs/2408.14950
The brain basis of emotion has consistently received widespread attention, attracting a large number of studies to explore this cutting-edge topic. However, the methods employed in these studies typically only model the pairwise relationship between
Externí odkaz:
http://arxiv.org/abs/2408.00525
Autor:
Du, Changde, Fu, Kaicheng, Wen, Bincheng, Sun, Yi, Peng, Jie, Wei, Wei, Gao, Ying, Wang, Shengpei, Zhang, Chuncheng, Li, Jinpeng, Qiu, Shuang, Chang, Le, He, Huiguang
The conceptualization and categorization of natural objects in the human mind have long intrigued cognitive scientists and neuroscientists, offering crucial insights into human perception and cognition. Recently, the rapid development of Large Langua
Externí odkaz:
http://arxiv.org/abs/2407.01067
Emotion decoding plays an important role in affective human-computer interaction. However, previous studies ignored the dynamic real-world scenario, where human experience a blend of multiple emotions which are incrementally integrated into the model
Externí odkaz:
http://arxiv.org/abs/2405.20600
Drawing inspiration from the hierarchical processing of the human auditory system, which transforms sound from low-level acoustic features to high-level semantic understanding, we introduce a novel coarse-to-fine audio reconstruction method. Leveragi
Externí odkaz:
http://arxiv.org/abs/2405.18726
Decoding language information from brain signals represents a vital research area within brain-computer interfaces, particularly in the context of deciphering the semantic information from the fMRI signal. However, many existing efforts concentrate o
Externí odkaz:
http://arxiv.org/abs/2405.07840
Reconstructing human dynamic vision from brain activity is a challenging task with great scientific significance. The difficulty stems from two primary issues: (1) vision-processing mechanisms in the brain are highly intricate and not fully revealed,
Externí odkaz:
http://arxiv.org/abs/2405.03280
The study of decoding visual neural information faces challenges in generalizing single-subject decoding models to multiple subjects, due to individual differences. Moreover, the limited availability of data from a single subject has a constraining i
Externí odkaz:
http://arxiv.org/abs/2402.08994
A Temporal-Spectral Fusion Transformer with Subject-Specific Adapter for Enhancing RSVP-BCI Decoding
The Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient technology for target retrieval using electroencephalography (EEG) signals. The performance improvement of traditional decoding methods relies on a subst
Externí odkaz:
http://arxiv.org/abs/2401.06340
Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task. Especially, the achievement of precise and controllable image reconstruction bears great significance in propelling the progress and utilization of brain-
Externí odkaz:
http://arxiv.org/abs/2308.04249