在拒绝的环境中进行搜索对于群体机器人来说是具有挑战性的，因为不允许GNSS，映射，数据共享和中央处理的帮助。但是，使用嗅觉和听觉像动物一样合作可能是改善群体合作的重要方法。在本文中，提出了一群自主机器人来探索拒绝环境的嗅觉审计算法算法（OA-BUG）。构建了一个模拟环境，以衡量OA-BUG的性能。使用OA-BUG的搜索任务覆盖范围可以达到96.93％，与类似的算法SGBA相比，最大的40.55％提高了40.55％。此外，在实际的群机器人上进行了实验，以证明OA-BUG的有效性。结果表明，OA-BUG可以在被拒绝的环境中改善群体机器人的性能。

工业组件的质量对于生产机器人等特殊设备至关重要。对这些组件的缺陷检查是确保质量的有效方法。在本文中，我们提出了一个混合网络，即SSD-Fast Net，用于对铁轨，绝缘体，换向器等的工业缺陷检查。SSD-FASTER网络是一个两阶段网络，包括用于快速定位有缺陷块的SSD，以及更快的改进速度R-CNN用于缺陷分割。对于前者，我们提出了一种新颖的切片定位机制，以帮助SSD迅速扫描。第二阶段是基于改进的更快的R-CNN，使用FPN，可变形的内核（DK）来增强表示能力。它融合了多尺度信息，并自动加入了接受场。我们还提出了一种新颖的损失功能，并使用ROI对齐来提高准确性。实验表明，我们的SSD速度网的平均准确性为84.03％，基于更快的R-CNN比最近的竞争对手高13.42％，比基于GAN的方法高4.14％，比DNN高10％以上基于基于检测器。并且计算速度提高了近7％，这证明了其稳健性和出色的性能。

作为自然宝藏，海洋拥有丰富的资源。但是对于海洋生物的可持续发展至关重要的珊瑚礁由于存在COT和其他生物而面临巨大的危​​机。通过体力劳动来保护社会的效率有限且效率低下。海洋环境的不可预测的本质也使手动操作冒险。在水下操作中使用机器人已成为一种趋势。但是，水下图像采集具有弱光，低分辨率和许多干扰等缺陷，而现有的目标检测算法无效。基于此，我们提出了一种基于注意的Yolov5（称为UTD-YOLOV5）的水下目标检测算法。它可以快速有效地检测COT，这又为复杂的水下操作提供了前提。我们在多个阶段调整了Yolov5的原始网络体系结构，包括：用两阶段的级联CSP（CSP2）代替原始骨干；引入视觉通道注意机构模块SE；设计随机锚点相似性计算方法等。这些操作使UTD-Yolov5能够更灵活地检测并更准确地捕获功能。为了提高网络的效率，我们还提出了优化方法，例如WBF和迭代改进机制。本文根据CSIRO数据集进行了许多实验[1]。结果表明，我们的UTD-Yolov5的平均准确性达到78.54％，与基线相比，这是一个很大的提高。

我们将简要介绍本文Trecvid2021中WHU-nercms的实验方法和结果。今年，我们参加了实例搜索的自动和交互式任务（INS）。对于自动任务，检索目标分为两个部分，人检索和动作检索。我们采用了两阶段方法，包括对人检索的面部检测和面部识别以及由三种基于框架的人类对象相互作用检测方法和两种基于视频的一般动作检测方法组成的两种动作检测方法。在那之后，人的检索结果和动作检索结果被融合以初始化结果排名列表。此外，我们尝试使用互补方法进一步提高搜索性能。对于交互式任务，我们在融合结果上测试了两种不同的交互策略。我们分别为自动和交互式任务提交4次运行。每次运行的引入显示在表1中。官方评估表明，所提出的策略在自动和交互式轨道中排名第一。

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.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.