深度神经网络（DNN）已显示在许多现实生活中提供极好的性能，但它们的大量计算成本和存储要求已阻止它们部署到许多边缘和内部内容（IOT）设备。稀疏的深神经网络，其大多数重量参数是零，可以大大降低模型的计算复杂性和存储器消耗。在实际使用场景中，设备可能遭受不同环境下的可用计算和存储器资源的大波动，并且由于具有大延迟的长尾延长而难以维持服务质量（QoS）。面对现实生活挑战，我们建议培训支持多个稀疏水平的稀疏模型。也就是说，满足权重的分层结构，使得较少稀疏子模型的较少稀疏子模型区域子集的位置和非零参数的位置。以这种方式，可以在推理期间动态地选择适当的稀疏度水平，而存储成本被最小稀疏子模型覆盖。我们已经在各种DNN模型和任务中验证了我们的方法，包括Reset-50，PointNet ++，GNMT和图表注意网络。我们获得稀疏的子模型，平均重量为13.38％，拖鞋14.97％，而准确性也与他们的密集对应物一样好。具有5.38％权重和4.47％的更稀疏的子模型，跨越少量稀疏的跨，只能获得3.25％的相对精度损耗。

已经证明，深度神经网络（DNN）在解决许多现实问题方面是有效的，但其高计算成本禁止将这些模型部署到边缘设备。修剪，作为将零的方法引入模型重量的方法，已显示是在模型精度和计算效率之间提供良好权衡的有效方法，并且是一种生成压缩模型的广泛使用的方法。然而，修剪的粒度使得重要的权衡。在相同的稀疏性水平上，粗粒结构的稀疏图案在传统硬件上更有效，但导致更差的精度，而细粒度的非结构化稀疏模式可以实现更好的精度，但在现有硬件上效率低下。另一方面，一些现代处理器配备了快速的片上刻痕存储器和聚集/散射引擎，用于在这种存储器上执行间接负载和存储操作。在这项工作中，我们提出了一系列新颖的稀疏模式，命名为聚光散射（GS）模式，以利用Scratchpad存储器和收集/散射引擎来加速神经网络推论。相应地，我们呈现了一种紧凑的稀疏格式。提出的稀疏模式，以及一种新颖的修剪方法，解决了负载不平衡问题，并导致质量接近非结构化稀疏模型的型号，以及靠近结构化稀疏型号的计算效率。我们的实验表明，与传统结构稀疏模式相比，GS模式在精度和计算效率之间始终如一地进行折衷。 GS模式可以以相同的精度级别将DNN组件的运行时间减少两到三次。这是在三个不同的深度学习任务和流行模型中确认，即机器翻译的GNMT，用于图像识别的Reset50，以及用于声学语音识别的Japser。

迄今为止，纳米级的活细胞成像仍然具有挑战性。尽管超分辨率显微镜方法使得能够在光学分辨率下方的亚细胞结构的可视化，但空间分辨率仍然足够远，对于体内生物分子的结构重建仍然足够远（即24nm厚度的微管纤维）。在这项研究中，我们提出了一种A-Net网络，并显示通过基于劣化模型的DWDC算法组合A-Net DeeD学习网络，可以显着改善由共聚焦显微镜捕获的细胞骨架图像的分辨率。利用DWDC算法构建新数据集并利用A-Net神经网络的特征（即，层数较少），我们成功地消除了噪声和絮凝结构，最初干扰了原始图像中的蜂窝结构，并改善了空间分辨率使用相对较小的数据集10次。因此，我们得出结论，将A-Net神经网络与DWDC方法结合的所提出的算法是一种合适的和普遍的方法，用于从低分辨率图像中严格的生物分子，细胞和器官的结构细节。

最大限度的训练原则，最大限度地减少最大的对抗性损失，也称为对抗性培训（AT），已被证明是一种提高对抗性鲁棒性的最先进的方法。尽管如此，超出了在对抗环境中尚未经过严格探索的最小最大优化。在本文中，我们展示了如何利用多个领域的最小最大优化的一般框架，以推进不同类型的对抗性攻击的设计。特别是，给定一组风险源，最小化最坏情况攻击损失可以通过引入在域集的概率单纯x上最大化的域权重来重新重整为最小最大问题。我们在三次攻击生成问题中展示了这个统一的框架 - 攻击模型集合，在多个输入下设计了通用扰动，并制作攻击对数据转换的弹性。广泛的实验表明，我们的方法导致对现有的启发式策略以及对培训的最先进的防御方法而言，鲁棒性改善，培训对多种扰动类型具有稳健。此外，我们发现，从我们的MIN-MAX框架中学到的自调整域权重可以提供整体工具来解释跨域攻击难度的攻击水平。代码可在https://github.com/wangjksjtu/minmaxsod中获得。

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.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.