最近，由于受监督人员重新识别（REID）的表现不佳，域名概括（DG）人REID引起了很多关注，旨在学习一个不敏感的模型，并可以抵抗域的影响偏见。在本文中，我们首先通过实验验证样式因素是域偏差的重要组成部分。基于这个结论，我们提出了一种样式变量且无关紧要的学习方法（SVIL）方法，以消除样式因素对模型的影响。具体来说，我们在SVIL中设计了样式的抖动模块（SJM）。 SJM模块可以丰富特定源域的样式多样性，并减少各种源域的样式差异。这导致该模型重点关注与身份相关的信息，并对样式变化不敏感。此外，我们将SJM模块与元学习算法有机结合，从而最大程度地提高了好处并进一步提高模型的概括能力。请注意，我们的SJM模块是插件和推理，无需成本。广泛的实验证实了我们的SVIL的有效性，而我们的方法的表现优于DG-REID基准测试的最先进方法。

对比学习在视频表示学习中表现出了巨大的潜力。但是，现有方法无法充分利用短期运动动态，这对于各种下游视频理解任务至关重要。在本文中，我们提出了运动敏感的对比度学习（MSCL），该学习将光学流捕获的运动信息注入RGB帧中，以增强功能学习。为了实现这一目标，除了剪辑级全球对比度学习外，我们还开发了局部运动对比度学习（LMCL），具有两种模式的框架级对比目标。此外，我们引入流动旋转增强（FRA），以生成额外的运动除件负面样品和运动差分采样（MDS）以准确筛选训练样品。对标准基准测试的广泛实验验证了该方法的有效性。以常用的3D RESNET-18为骨干，我们在UCF101上获得了91.5 \％的前1个精度，而在视频分类中进行了一些v2的v2，以及65.6 \％的top-1 top-1召回ucf1011对于视频检索，特别是改善了最新的。

多对象跟踪（MOT）需要通过帧检测和关联对象。与通过检测到的边界框或将对象作为点跟踪不同，我们建议跟踪对象作为像素分布。我们将此想法实例化，以基于变压器的体系结构P3Aformer，并具有像素的传播，预测和关联。P3Aformer通过流量信息引导的Pixel-Pixel特征，以传递帧之间的消息。此外，P3Aformer采用元结构结构来生成多尺度对象特征图。在推断期间，提出了一个像素关联过程，以基于像素的预测来通过帧恢复对象连接。P3Aformer在MOT17基准上的MOTA中产生81.2 \％，这是所有变压器网络中第一个达到文献中80 \％MOTA。P3AFORMER在MOT20和Kitti基准测试上也优于最先进的。

基于自然语言（NL）的车辆检索旨在搜索给定文本描述的特定车辆。不同于基于图像的车辆检索，基于NL的车辆检索不仅需要考虑车辆外观，还需要考虑周围环境和时间关系。在本文中，我们提出了一个具有空间关系建模（SSM）方法的对称网络，用于基于NL的车辆检索。具体而言，我们设计了一个对称网络，以学习文本描述和车辆图像之间的统一跨模式表示，其中保留了车辆外观细节和车辆轨迹全球信息。此外，为了更好地利用位置信息，我们提出了一种空间关系建模方法，以考虑周围环境和相互关系的考虑。定性和定量实验验证了所提出的方法的有效性。我们在第六届AI城市挑战赛的测试集上获得了43.92％的MRR准确性，该挑战是基于自然语言的车辆检索轨道，在公共排行榜上所有有​​效的提交中排名第一。该代码可从https://github.com/hbchen121/aicity2022_track2_ssm获得。

人体对象交互（HOI）检测是高级人以人为中心的场景理解的基本任务。我们提出了短语，其中包含了Hoi分支和一个新型短语分支，以利用语言和改进关系表达。具体而言，短语分支由语义嵌入式监督，其基础事实自动从原始的Hoi注释自动转换，而无需额外的人力努力。同时，提出了一种新颖的标签组合方法来处理会安的长尾问题，由语义邻居复合新型短语标签。此外，为了优化短语分支，提出了由蒸馏损失和平衡三态损耗组成的损失。进行了广泛的实验，以证明拟议的短语疗养的有效性，这使得对基线的显着改善，并超越了以前的最先进的方法，以满足的HICO-DET基准。

人员搜索是一个有关的任务，旨在共同解决人员检测和人员重新识别（RE-ID）。虽然最先前的方法侧重于学习稳健的个人功能，但由于照明，大构成方差和遮挡，仍然很难区分令人困惑的人。上下文信息实际上是人们搜索任务，这些任务在减少混淆方面搜索。为此，我们提出了一个名为注意上下文感知嵌入（ACAE）的新颖的上下文特征头，这增强了上下文信息。 Acae反复审查图像内部和图像内的该人员，以查找类似的行人模式，允许它隐含地学会找到可能的共同旅行者和有效地模范上下文相关的实例的关系。此外，我们提出了图像记忆库来提高培训效率。实验上，ACAE在基于不同的一步法时显示出广泛的促销。我们的整体方法实现了最先进的结果与先前的一步法。

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.

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.