视网膜血管的拓扑和几何分析是早期检测许多常见疾病的成本效益。同时，在泛化能力方面仍然缺乏自动化血管分割和血管树分析。在这项工作中，我们构建了一种新的基准Reta，其中81个标记的船舶掩模，旨在促进视网膜血管分析。提出了一种半自动粗到精细的工作流到注释血管像素。在数据集建设期间，我们通过对自发专用软件进行多级注释和标签歧义来控制注释器间可变性和内部注入者的变化。除了二元血管面罩外，我们还获得了血管标记期间含有动脉/静脉面罩，血管骨骼，分叉，树木和异常的血管注释。标记船面罩的主观和客观质量验证都表现出在其他公共数据集上的显着提高。注释软件也公开可用于船舶注释可视化。用户可以使用我们的数据集开发船只分段算法或评估船只分段性能。此外，我们的数据集可能是跨型号管状结构分割的良好研究来源。

The massive growth of self-supervised learning (SSL) has been witnessed in language, vision, speech, and audio domains over the past few years. While discrete label prediction is widely adopted for other modalities, the state-of-the-art audio SSL models still employ reconstruction loss for pre-training. Compared with reconstruction loss, semantic-rich discrete label prediction encourages the SSL model to abstract the high-level audio semantics and discard the redundant details as in human perception. However, a semantic-rich acoustic tokenizer for general audio pre-training is usually not straightforward to obtain, due to the continuous property of audio and unavailable phoneme sequences like speech. To tackle this challenge, we propose BEATs, an iterative audio pre-training framework to learn Bidirectional Encoder representation from Audio Transformers, where an acoustic tokenizer and an audio SSL model are optimized by iterations. In the first iteration, we use random projection as the acoustic tokenizer to train an audio SSL model in a mask and label prediction manner. Then, we train an acoustic tokenizer for the next iteration by distilling the semantic knowledge from the pre-trained or fine-tuned audio SSL model. The iteration is repeated with the hope of mutual promotion of the acoustic tokenizer and audio SSL model. The experimental results demonstrate our acoustic tokenizers can generate discrete labels with rich audio semantics and our audio SSL models achieve state-of-the-art results across various audio classification benchmarks, even outperforming previous models that use more training data and model parameters significantly. Specifically, we set a new state-of-the-art mAP 50.6% on AudioSet-2M for audio-only models without using any external data, and 98.1% accuracy on ESC-50. The code and pre-trained models are available at https://aka.ms/beats.

最近，蒙面的预测预训练在自我监督的学习（SSL）方面取得了显着的进展，以进行语音识别。它通常需要以无监督的方式获得的代码簿，从而使其准确和难以解释。我们提出了两种监督指导的代码书生成方法，以提高自动语音识别（ASR）的性能以及预训练效率，要么通过使用混合ASR系统来解码以生成音素级别对准（命名为PBERT），要么通过在上进行集群进行聚类。从端到端CTC模型（命名CTC聚类）提取的监督语音功能。混合动力和CTC模型均经过与微调相同的少量标记语音训练。实验表明，我们的方法对各种SSL和自我训练基准的优势具有显着优势，相对减少了17.0％。我们的预训练模型在非ASR语音任务中还显示出良好的可传递性。

最近，即使预训练目标是为语音识别而设计的，自我监督学习（SSL）即使在说话者的识别方面表现出了很强的表现。在本文中，我们研究了哪些因素导致对与说话者相关的任务的自我监督学习成功，例如扬声器验证（SV）通过一系列精心设计的实验。我们对Voxceleb-1数据集的经验结果表明，SSL对SV任务的好处是来自蒙版语音预测丢失，数据量表和模型大小的组合，而SSL量化器具有较小的影响。我们进一步采用了综合梯度归因方法和损失景观可视化，以了解说话者识别性能的自我监督学习的有效性。

最近，先驱工作发现，演讲预训练模型可以解决全堆栈语音处理任务，因为该模型利用底层学习扬声器相关信息和顶层以编码与内容相关的信息。由于网络容量有限，我们认为如果模型专用于音频内容信息学习，则可以进一步提高语音识别性能。为此，我们向自我监督学习（ILS-SSL）提出中间层监督，这将模型通过在中间层上添加额外的SSL丢失来尽可能地专注于内容信息。 LibrisPeech测试 - 其他集合的实验表明，我们的方法显着优于Hubert，这实现了基数/大型模型的W / O语言模型设置的相对字错误率降低了23.5％/ 11.6％。详细分析显示我们模型的底层与拼音单元具有更好的相关性，这与我们的直觉一致，并解释了我们对ASR的方法的成功。

根据图像回答语义复杂的问题是在视觉问题应答（VQA）任务中的具有挑战性。虽然图像可以通过深度学习来良好代表，但是始终简单地嵌入问题，并且不能很好地表明它的含义。此外，视觉和文本特征具有不同模式的间隙，很难对齐和利用跨模块信息。在本文中，我们专注于这两个问题，并提出了一种匹配关注（GMA）网络的图表。首先，它不仅为图像构建图形，而且在句法和嵌入信息方面构建了该问题的图表。接下来，我们通过双级图形编码器探讨了模特内的关系，然后呈现双边跨模型图匹配注意力以推断图像与问题之间的关系。然后将更新的跨模式特征发送到答案预测模块中以进行最终答案预测。实验表明，我们的网络在GQA数据集和VQA 2.0数据集上达到了最先进的性能。消融研究验证了GMA网络中每个模块的有效性。

自我监督学习（SSL）在语音识别方面取得了巨大的成功，而有限的探索已尝试完成其他语音处理任务。由于语音信号包含多方面的信息，包括说话者身份，副语言学，口语内容等，学习所有语音任务的通用表示都具有挑战性。为了解决该问题，我们提出了一个新的预培训模型WAVLM，以解决全堆栈的下游语音任务。 Wavlm共同学习了蒙面的语音预测和预训练。通过这种方式，WAVLM不仅可以通过掩盖的语音预测来保持语音内容建模能力，而且还可以通过语音denoing来提高非ASR任务的潜力。此外，WAVLM还采用封闭式的变压器结构的封闭相对位置偏置，以更好地捕获输入语音的序列排序。我们还将培训数据集从60k小时扩展到94K小时。 WAVLM大型在精湛的基准上实现了最先进的性能，并在其代表性基准上为各种语音处理任务带来了重大改进。代码和预培训模型可在https://aka.ms/wavlm上找到。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.