在过去的几年中，短视频在淘宝等电子商务平台上见证了迅速的增长。为了确保内容的新鲜感，平台需要每天发布大量新视频，从而使传统的点击率（CTR）预测方法遇到了该项目冷启动问题。在本文中，我们提出了一种有效的图形引导功能传输系统的礼物，以完全利用加热视频的丰富信息，以补偿冷启动的视频。具体而言，我们建立了一个异质图，其中包含物理和语义链接，以指导从热视频到冷启动视频的功能传输过程。物理链接代表明确的关系，而语义链接衡量了两个视频的多模式表示的接近性。我们精心设计功能传输功能，以使图表上不同Metapaths的不同类型的转移功能（例如，ID表示和历史统计）。我们在大型现实世界数据集上进行了广泛的实验，结果表明，我们的礼品系统的表现明显优于SOTA方法，并在TAOBAO APP的主页上为CTR带来了6.82％的提升。

图形神经网络（GNN）在各种高桩预测任务中实现了最先进的性能，但是具有不规则结构的图表上的多层聚合使得GNN成为一种更不可解释的模型。先前的方法使用更简单的子图来模拟完整模型，或识别预测原因的完整模型或反事实。这两个方法旨在瞄准两个不同的目标，“模拟性”和“反事实相关”，但目前尚不清楚目标如何共同影响人类理解解释。我们设计用户学习，以调查这些关节效果，并使用该研究结果设计多目标优化（MOO）算法，以查找帕累托最佳解释，可在模拟性和反事实方面得到良好平衡。由于目标模型可以是任何GNN变体，并且由于隐私问题可能无法访问，因此我们使用零顺序信息设计一个搜索算法而不访问目标模型的架构和参数。来自四个应用的九个图表的定量实验表明，帕累托有效的解释主导使用一阶连续优化或离散组合搜索的单目标基线。在鲁棒性和敏感性中进一步评估了解释，以表明他们揭示令人信服的令人信服的能力，同时对可能的混乱持谨慎态度。各种主导的反事件可以证明算法追索权的可行性，这可能促进人类参与使用GNN决策的算法公平性。

图表在许多应用中普遍存在，例如社交网络，知识图形，智能电网等。图形神经网络（GNN）是这些应用的当前最先进的，但对人类来说仍然是模糊的。解释GNN预测可以添加透明度。然而，随着许多图表不是静态而是不断发展，解释了两个图形快照之间的预测的变化是不同的，而同样重要的。现有方法仅解释静态预测或生成用于动态预测的粗略或无关的解释。我们定义解释不断发展的GNN预测的问题，并提出了一种唯一地将预测的改变唯一地分解到计算图中的路径。涉及高度节点的许多路径的归属仍然不可解释，同时简单地选择顶部的重要路径可以是近似变化的次优。我们制定了一种新颖的凸优化问题，以最佳地选择解释预测演化的路径。从理论上讲，我们证明了基于层相关性 - 传播（LRP）的现有方法是当与空图进行比较时所提出的算法的特殊情况。经验上，在七个图形数据集上，具有用于评估预测变化的解释的新型度量，我们展示了所提出的方法对现有方法的优越性，包括LRP，DEEPLIFT和其他路径选择方法。

长篇小说（LSG）是自然语言处理中的垂灾目标之一。与大多数文本生成任务不同，LSG要求基于更短的文本输入输出丰富的内容的长话，并且通常存在信息稀疏性。在本文中，我们提出了\ emph {topnet}来缓解这个问题，通过利用神经主题建模的最新进步来获得高质量的骨架词来补充短输入。特别是，而不是直接生成故事，首先学会将简短的文本输入映射到低维主题分布（由主题模型预先分配）。基于此潜在主题分布，我们可以使用主题模型的重建解码器来对与故事的骨骼相互相关的单词序列。两个基准数据集的实验表明，我们的框架在骨架词选择中非常有效，在自动评估和人类评估中显着优于最先进的模型。

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.

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.

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.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.