基于内容的图像检索（CIR）旨在通过同时理解示例图像和互补文本的组成来搜索目标图像，这可能会影响各种各样的现实世界应用，例如互联网搜索和时尚检索。在这种情况下，输入图像是搜索的直观上下文和背景，而相应的语言明确请求有关如何修改查询图像的特定特征以获取预期目标图像的新特征。此任务具有挑战性，因为它需要通过合并跨粒度语义更新来学习和理解复合图像文本表示。在本文中，我们通过小说\下划线{\ textbf {b}}来解决此任务\ textbf {s}} ition（\ textbf {boss}）带有混合反事实训练框架，通过从两个先前被忽视的角度研究它，从而为CIR任务提供了新的启示：\ emph {隐式自下而上的自下而上的sisitiol语言表示}和sisiol语言表示}和\ emph {显式晶状体构造的明显细粒度对应}。一方面，我们利用了从底部本地特征到顶部全局语义的跨模式嵌入的隐式相互作用和组成，从而保留和转换视觉表示在多个连续步骤中以语言语义为条件的视觉表示，以进行有效的目标图像搜索。另一方面，我们设计了一种混合反事实培训策略，可以减少模型对类似查询的歧义。

深度神经网络用于图像识别任务（例如预测笑脸）的性能会以代表性不足的敏感属性类别降低。我们通过基于人口统计学奇偶校验，均衡赔率和新型的联合会措施的批估计估计来引入公平意识的正规化损失来解决这个问题。对Celeba，UTKFACE和SIIM-ISIC黑色素瘤分类挑战的面部和医学图像进行的实验表明，我们提出的公平性损失对偏置缓解的有效性，因为它们可以改善模型公平，同时保持高分类性能。据我们所知，我们的工作是首次尝试将这些类型的损失纳入端到端培训方案，以减轻视觉属性预测指标的偏见。我们的代码可在https://github.com/nish03/fvap上找到。

最近的工作表明，二值化的神经网络（BNN）能够大大降低计算成本和内存占用空间，促进在资源受限设备上进行模型部署。然而，与其全精密对应物相比，BNN患有严重的精度降解。旨在降低这种精度差距的研究已经很大程度上主要集中在具有少量或没有1x1卷积层的特定网络架构上，标准二值化方法不起作用。由于1x1卷积在现代架构的设计中是常见的（例如，Googlenet，Reset，DenSenet），开发一种方法以有效地为BNN进行更广泛采用的方法是至关重要的。在这项工作中，我们提出了一个“弹性链路”（EL）模块，通过自适应地将实值的输入特征自适应地添加到后续卷积输出功能来丰富了BNN内的信息流。所提出的EL模块很容易实现，并且可以与BNN的其他方法结合使用。我们证明将EL添加到BNNS对挑战大规模想象数数据集产生显着改进。例如，我们将二值化resnet26的前1个精度从57.9％提高到64.0％。 EL也有助于培训二值化Mobilenet的趋同，为此实现了56.4％的前1个精度。最后，随着RESTNET的整合，它产生了新的最新的最新性，最新的171.9％的前1个精度。

我们提出了一个无监督的卷积神经网络（CNN），用于放松参数估计。该网络包含信号松弛和Bloch模拟，同时利用邻近voxels的剩余学习和空间关系。与数值模拟中的标准参数估计方法和多回波T2和T2 *映射的标准参数估计方法相比，显示了对噪声的量化精度和稳健性。所提出的网络与子空间建模的组合和来自高度提高数据的子空间建模和MR指纹识别（MRF）允许高质量的T1和T2映射。

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.