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, ...
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...
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 ...
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and/or password to establish your account. After you have done that, then you'd connect to your computer which has the wireless internet connection on and configure it to detect the wireless internet signal and prompts you for your username and password to login to your century link account....
3. Proposed Method We first revisit the current masked video modeling task for video representation learning (cf. Section 3.1). Then, we introduce our masked motion encoding (MME), where we change the task from recovering appearance to recover- ing motion trajectory (cf....
= Mask.size(); ++i) {+int M = Mask[i];+if (M < 0)+continue;+int Src = M >= (int)NumElts;+int Diff = (int)i - (M % NumElts);+bool Match = false;+for (int j = 0; j < 2; j++) {+if (SrcInfo[j].first == -1) {+assert(SrcInfo[j].second == SignalValue)...
3. Approach Our masked autoencoder (MAE) is a simple autoencod- ing approach that reconstructs the original signal given its partial observation. Like all autoencoders, our approach has an encoder that maps the observed signal to a latent representation, and a decoder that reconstructs the ...