对比度学习（CL）在任何监督的多级分类或无监督的学习中显示出令人印象深刻的图像表示学习进步。但是，这些CL方法无法直接适应多标签图像分类，因为难以定义正面和负面实例以对比多标签场景中给定的锚图像对比给定的锚图像，让标签单独丢失，这意味着借用了借用的标签通常，从对比度多级学习来定义它们的常用方式将产生许多不利的虚假负面实例。在本文中，通过引入标签校正机制来识别缺失的标签，我们首先优雅地产生了锚映像的单个语义标签的阳性和负面因素，然后定义了带有缺少标签的多标签图像分类的独特对比度损失（CLML） ），损失能够准确地使图像接近其真实的正面图像和虚假的负面图像，远离其真实的负面图像。与现有的多标签CL损失不同，CLML还保留了潜在表示空间中的低排名全球和局部标签依赖关系，在这些空间中，已证明此类依赖性有助于处理缺失的标签。据我们所知，这是在缺失标签方案中的第一个一般多标签CL损失，因此可以通过单个超参数与任何现有多标签学习方法的损失无缝配对。已提出的策略已被证明可以在三个标准数据集（MSCOCO，VOC和NUS范围内）提高RESNET101模型的分类性能，分别为1.2％，1.6％和1.3％。代码可在https://github.com/chuangua/contrastivelossmlml上找到。

Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter optimization as a pure-exploration nonstochastic infinite-armed bandit problem where a predefined resource like iterations, data samples, or features is allocated to randomly sampled configurations. We introduce a novel algorithm, Hyperband, for this framework and analyze its theoretical properties, providing several desirable guarantees. Furthermore, we compare Hyperband with popular Bayesian optimization methods on a suite of hyperparameter optimization problems. We observe that Hyperband can provide over an order-of-magnitude speedup over our competitor set on a variety of deep-learning and kernel-based learning problems.

元学习在有限的监督数据中表现出了几次学习的巨大成功。在这些设置中，元模型通常被过度参数化。尽管常规的统计学习理论表明，过度参数化的模型倾向于过度合适，但经验证据表明，过度参数化的元学习方法仍然很好地工作 - 这种现象通常称为``良性过度拟合''。我们了解这种现象，我们专注于元学习设置，我们将具有挑战性的嵌套结构称为嵌套的元学习，并在过度参数化的元学习模型下分析其泛化性能。尽管我们的分析使用了相对可牵引的线性模型，但我们的理论有助于理解数据异质性，模型适应和良性过度适应嵌套元学习任务之间的微妙相互作用。我们通过数值模拟证实了我们的理论主张。

模型不合时宜的元学习（MAML）目前是少量元学习的主要方法之一。尽管它具有有效性，但由于先天的二聚体问题结构，MAML的优化可能具有挑战性。具体而言，MAML的损失格局比其经验风险最小化的对应物更为复杂，可能的鞍点和局部最小化可能更复杂。为了应对这一挑战，我们利用了最近发明的清晰度最小化的最小化，并开发出一种清晰感的MAML方法，我们称其为Sharp MAML。我们从经验上证明，Sharp-MAML及其计算有效的变体可以胜过流行的现有MAML基准（例如，Mini-Imagenet上的$+12 \％$ $精度）。我们通过收敛速率分析和尖锐MAML的概括结合进行了经验研究。据我们所知，这是在双层学习背景下对清晰度感知最小化的第一个经验和理论研究。该代码可在https://github.com/mominabbass/sharp-maml上找到。

车辆重新识别（RE-ID）旨在通过不同的摄像机检索具有相同车辆ID的图像。当前的零件级特征学习方法通​​常通过统一的部门，外部工具或注意力建模来检测车辆零件。但是，此部分功能通常需要昂贵的额外注释，并在不可靠的零件遮罩预测的情况下导致次优性能。在本文中，我们提出了一个针对车辆重新ID的弱监督零件注意网络（Panet）和零件式网络（PMNET）。首先，Panet通过与零件相关的通道重新校准和基于群集的掩模生成无需车辆零件监管信息来定位车辆零件。其次，PMNET利用教师指导的学习来从锅et中提取特定于车辆的特定功能，并进行多尺度的全球零件特征提取。在推断过程中，PMNET可以自适应提取歧视零件特征，而无需围绕锅et定位，从而防止了不稳定的零件掩模预测。我们将重新ID问题作为一个多任务问题，并采用同质的不确定性来学习最佳的ID损失权衡。实验是在两个公共基准上进行的，这表明我们的方法优于最近的方法，这不需要额外的注释，即CMC@5的平均增加3.0％，而Veri776的MAP中不需要超过1.4％。此外，我们的方法可以扩展到遮挡的车辆重新ID任务，并具有良好的概括能力。

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.