42 citations · 83 across the 12 of their papers we have counts for
12 papers
MAD: Multi-Alignment MEG-to-Text Decoding
Yiqian Yang, Hyejeong Jo, Yiqun Duan +6
Deciphering language from brain activity is a crucial task in brain-computer interface (BCI) research. Non-invasive cerebral signaling techniques including electroencephalography (…
Are EEG-to-Text Models Working?
Hyejeong Jo, Yiqian Yang, Juhyeok Han +3
This work critically analyzes existing models for open-vocabulary EEG-to-Text translation. We identify a crucial limitation: previous studies often employed implicit teacher-forcin…
DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation
Yiqun Duan, Jinzhao Zhou, Zhen Wang +2
The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs). With the swift advancement of large language models, such as ChatGPT, the n…
BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision
Jinzhao Zhou, Yiqun Duan, Yu-Cheng Chang +2
This paper presents BELT, a novel model and learning framework for the pivotal topic of brain-to-language translation research. The translation from noninvasive brain signals into…
Domain-Specific Denoising Diffusion Probabilistic Models for Brain Dynamics
Yiqun Duan, Jinzhao Zhou, Zhen Wang +3
The differences in brain dynamics across human subjects, commonly referred to as human artifacts, have long been a challenge in the field, severely limiting the generalizability of…
A Closer Look at the Explainability of Contrastive Language-Image Pre-training
Yi Li, Hualiang Wang, Yiqun Duan +2
Contrastive language-image pre-training (CLIP) is a powerful vision-language model that has shown great benefits for various tasks. However, we have identified some issues with its…
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation
Yiqun Duan, Xianda Guo, Zheng Zhu
Monocular depth estimation is a challenging task that predicts the pixel-wise depth from a single 2D image. Current methods typically model this problem as a regression or classifi…
Generalizing Multimodal Variational Methods to Sets
Jinzhao Zhou, Yiqun Duan, Zhihong Chen +2
Making sense of multiple modalities can yield a more comprehensive description of real-world phenomena. However, learning the co-representation of diverse modalities is still a lon…
Cross Task Neural Architecture Search for EEG Signal Classifications
Yiqun Duan, Zhen Wang, Yi Li +3
Electroencephalograms (EEGs) are brain dynamics measured outside the brain, which have been widely utilized in non-invasive brain-computer interface applications. Recently, various…
Exploring Visual Interpretability for Contrastive Language-Image Pre-training
Yi Li, Hualiang Wang, Yiqun Duan +2
Contrastive Language-Image Pre-training (CLIP) learns rich representations via readily available supervision of natural language. It improves the performance of downstream vision t…
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation
Yi Li, Yiqun Duan, Zhanghui Kuang +3
Weakly-Supervised Semantic Segmentation (WSSS) segments objects without a heavy burden of dense annotation. While as a price, generated pseudo-masks exist obvious noisy pixels, whi…
Progressive Open-Domain Response Generation with Multiple Controllable Attributes
Haiqin Yang, Xiaoyuan Yao, Yiqun Duan +3
It is desirable to include more controllable attributes to enhance the diversity of generated responses in open-domain dialogue systems. However, existing methods can generate resp…