本研究提出了一种高效的Perceiver架构（Long LoRA Perceiver - LLP），有效解决了Transformer在处理长序列时的复杂度问题，实验结果表明其性能显著优于最新的Transformer模型。

本研究提出了一种基于Perceiver架构的跨模态变换器DiVR，旨在解决虚拟环境中用户特征被忽视的问题，从而提高人类轨迹预测的准确性，具有重要的应用前景。

通过引入 Perceiver-Prompt 方法，利用 P-Tuning 对 Whisper 大规模模型进行微调，并通过可训练的 Perceiver 从可变长度输入中生成固定长度的说话人提示，以提高对中国发音障碍语音的模型识别性能。我们的实验结果表明，Perceiver-Prompt 在中国发音障碍语音数据集中获得了持续的识别性能改善，CER 相对减少高达 13.04%。

We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents while also maintaining end-to-end causal...

We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents while also maintaining end-to-end causal...

We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents while also maintaining end-to-end causal...