启发自masked signal modeling方法在NLP和CV领域中的成功应用,本文希望设计一种统一的多模态mask方法。作者认为在多模态数据中进行联合mask,其挑战主要来自图像和文本信号本身的巨大差异 —— 图像是连续、low-level、高度冗余的自然信号,而文本是离散、high-level、极度压缩的人造概念。这就带来两个问题:(1)如何设计一...
Besides training our model using masked signal modeling, we also apply two commonly used training strategies in video text pre-training community, i.e. video-text contrastive learning (VTC) and video-text matching (VTM). We empirically observe that these two schemes work pretty well on masked ...
DreamDiffusion leverages pre-trained text-to-image models and employs temporal masked signal modeling to pre-train the EEG encoder for effective and robust EEG representations. Additionally, the method further leverages the CLIP image encoder to provide extra supervision to better align EEG, text, ...
This paper introduces DreamDiffusion, a novel method for generating high-quality images directly from brain electroencephalogram (EEG) signals, without the need to translate thoughts into text. DreamDiffusion leverages pre-trained text-to-image models and employs temporal masked signal modeling to pre-...
By scaling up vision-centric foundation models with MIM pre-training to achieve strong performance on broad down- stream tasks, we hope EVA would bridge the gap between vision and language with masked signal modeling, and con- tributes to the big convergence across...
受bert启发,设计了a Masked Point Modeling (MPM) task 预训练 point cloud Transformers。首先将点云分割为several local point patches,设计了一个带有discrete Variational AutoEncoder (dVAE)的a point cloud Tokenizer——生成discrete point tokens包含了局部信息。然后,随机mask out一些输入点云的patches,feed ...
The backscattered signal propagating along the same path is captured by the transceiver, passed through the circulator, and detected by the Single Photon Avalanche Detector (SPAD). This detector, set at a 5% efficiency, a 4.5 ns gate, a dark count rate of 2 kHz, and a 0.2 \upmus dead...
Contrastive learning (CL) for Vision Transformers (ViTs) in image domains has achieved performance comparable to CL for traditional convolutional backbones. However, in 3D point cloud pretraining with ViTs, masked autoencoder (MAE) modeling remains dominant. This raises the question: Can we take th...
(single word boundary paradigm:M= 6.76,range: 3–12; masked priming paradigm:M= 5.55,range: 3–12). The corrected EEG signal was then segmented from 300 ms prior to and 700 ms after the first fixation onset on the target character and baseline-corrected by subtracting the voltages during ...
Our method is also related to self-supervised machine learning, in particular to masking. Masking, in the form of masked language modeling, was popularized for the pre-training transformer-based models likeBert(Devlin et al.2018). Masking image patches also results in state-of-the-art pre-trai...