实例歧视对比学习（CL）在学习可转移表示方面取得了重大成功。与CL损失的温度$ \ tau $相关的硬度感知的属性被确定为在自动集中在硬性阴性样品上起着至关重要的作用。但是，先前的工作还证明了CL损失的均匀性困境（UTD）存在，这将导致意外的性能降解。具体而言，较小的温度有助于学习可分离的嵌入，但对语义相关样品的耐受性较小，这可能导致次优的嵌入空间，反之亦然。在本文中，我们提出了一种模型感的对比学习（MACL）策略来逃避UTD。对于训练不足的阶段，锚固的高相似性区域包含潜在的阳性样品的可能性较小。因此，在这些阶段采用较小的温度可以对硬性阴性样品施加更大的惩罚强度，以改善CL模型的歧视。相反，由于对潜在的阳性样品的耐受性，训练有素的相位较高的温度有助于探索语义结构。在实施过程中，MACL中的温度旨在适应反映CL模型置信度的对齐属性。此外，我们重新审查了为什么对比度学习需要在统一梯度降低的视角中大量负面样本。基于MACL和这些分析，在这项工作中提出了新的CL损失，以改善批量尺寸少量的学说和培训。

少数拍摄识别旨在在低数据制度下识别新型类别。由于图像的稀缺性，机器不能获得足够的有效信息，并且模型的泛化能力极弱。通过使用辅助语义模式​​，基于最近的公制学习的少量学习方法已经取得了有希望的表现。但是，这些方法仅增强了支持类的表示，而查询图像没有语义模态信息以增强表示。相反，我们提出了属性形状的学习（ASL），其可以将可视化表示标准化以预测查询图像的属性。我们进一步设计了一个属性 - 视觉注意力模块（Avam），它利用属性来生成更多辨别特征。我们的方法使视觉表示能够专注于具有属性指导的重要区域。实验表明，我们的方法可以在幼崽和太阳基准上实现竞争结果。我们的代码可用于{https://github.com/chenhaoxing/asl}。

从有限的数据学习是一个具有挑战性的任务，因为数据的稀缺导致训练型模型的较差。经典的全局汇总表示可能会失去有用的本地信息。最近，许多射击学习方法通​​过使用深度描述符和学习像素级度量来解决这一挑战。但是，使用深描述符作为特征表示可能丢失图像的上下文信息。这些方法中的大多数方法独立地处理支持集中的每个类，这不能充分利用鉴别性信息和特定于特定的嵌入。在本文中，我们提出了一种名为稀疏空间变压器（SSFormers）的新型变压器的神经网络架构，可以找到任务相关的功能并抑制任务无关的功能。具体地，我们首先将每个输入图像划分为不同大小的几个图像斑块，以获得密集的局部特征。这些功能在表达本地信息时保留上下文信息。然后，提出了一种稀疏的空间变压器层以在查询图像和整个支持集之间找到空间对应关系，以选择任务相关的图像斑块并抑制任务 - 无关的图像斑块。最后，我们建议使用图像补丁匹配模块来计算密集的本地表示之间的距离，从而确定查询图像属于支持集中的哪个类别。广泛的少量学习基准测试表明，我们的方法实现了最先进的性能。

少量学习致力于在少数样品上培训模型。这些方法中的大多数基于像素级或全局级别特征表示学习模型。但是，使用全局功能可能会丢失本地信息，并且使用像素级别功能可能会丢失图像的上下文语义。此外，这些作品只能在单个级别上衡量它们之间的关系，这并不全面而有效。如果查询图像可以通过三个不同的水平相似度量同时分类很好，则类内的查询图像可以在较小的特征空间中更紧密地分布，产生更多辨别特征映射。由此激励，我们提出了一种新的零件级别嵌入适应图形（PEAG）方法来生成特定于任务特征。此外，提出了一种多级度量学习（MML）方法，其不仅可以计算像素级相似度，而且还考虑了部分级别特征和全局级别特征的相似性。对流行的少量图像识别数据集进行了广泛的实验，证明了与最先进的方法相比的方法的有效性。我们的代码可用于\ url {https:/github.com/chenhaoxing/m2l}。

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.