了解人类情绪是智能机器人提供更好的人类机器人相互作用的关键能力。现有作品仅限于修剪视频级别的情感分类，无法找到与情感相对应的时间窗口。在本文中，我们介绍了一项新任务，称为视频中的时间情感本地化（TEL），该任务旨在检测人类的情感并将其相应的时间边界定位在带有校准字幕的未修剪视频中。与时间动作本地化相比，TEL提出了三个独特的挑战：1）情绪的时间动态极为多样； 2）情绪提示都嵌入了外观和复杂的情节中； 3）细粒度的时间注释是复杂且劳动密集型的。为了应对前两个挑战，我们提出了一个新颖的扩张上下文集成网络，该网络与粗细的两流体系结构。粗流通过建模多粒性时间上下文来捕获各种时间动力学。细流通过推理从粗流的多晶格时间上下文之间的依赖性来实现复杂的理解，并将它们自适应地集成到细粒度的视频段特征中。为了应对第三个挑战，我们引入了跨模式共识学习范式，该范式利用了对齐视频和字幕之间的固有语义共识，以实现弱监督的学习。我们为新的测试集提供了3,000个手动注释的时间边界，因此可以对TEL问题进行未来的研究进行定量评估。广泛的实验显示了我们方法对时间情绪定位的有效性。这项工作的存储库位于https://github.com/yyjmjc/temporal-emotion-localization-in-videos。

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。

虽然注释大量的数据以满足复杂的学习模型，但对于许多现实世界中的应用程序可能会过于良好。主动学习（AL）和半监督学习（SSL）是两个有效但经常被隔离的方法，可以减轻渴望数据的问题。最近的一些研究探索了将AL和SSL相结合以更好地探测未标记数据的潜力。但是，几乎所有这些当代的SSL-AL作品都采用了简单的组合策略，忽略了SSL和AL的固有关系。此外，在处理大规模，高维数据集时，其他方法则遭受高计算成本。通过标记数据的行业实践的激励，我们提出了一种基于创新的基于不一致的虚拟对抗性积极学习（理想）算法，以进一步研究SSL-AL的潜在优势，并实现Al和SSL的相互增强，即SSL，即SSL宣传标签信息，以使标签信息无标记的样本信息并为Al提供平滑的嵌入，而AL排除了具有不一致的预测和相当不确定性的样品。我们通过不同粒度的增强策略（包括细粒度的连续扰动探索和粗粒数据转换）来估计未标记的样品的不一致。在文本和图像域中，广泛的实验验证了所提出的算法的有效性，并将其与最先进的基线进行了比较。两项实际案例研究可视化应用和部署所提出的数据采样算法的实际工业价值。

基于文本的图像标题（TextCAP）需要同时对视觉内容的理解并读取图像文本以生成自然语言描述。虽然一项任务可以教导机器来了解复杂的人类环境进一步鉴于我们日常环境中的文本是全部的，但它在正常标题中提出了额外的挑战。基于文本的图像直观地包含丰富和复杂的多模式关系内容，即可以从多视图而不是单个字幕来扩散图像细节。当然，我们可以介绍额外的配对训练数据以显示图像描述的多样性，这一过程是具有额外文本的文本映射对注释的劳动密集型和耗时。基于上述洞察力，我们调查如何使用未配对的培训范例来生成专注于不同图像零件的不同标题。我们提出了多模式关系图对抗性推论（魔法）框架，用于多样化和未配对的Textcap。该框架可以自适应地构建图形之间的图像和模型复杂关系的多个多模式关系图来表示描述性分集。此外，从建模的图表中开发了一种级联的生成对抗性网络，以推断图像句子特征对齐和语言相干水平中的未配对字幕。我们验证了魔法在从图像的不同关系信息项目生成不同标题时的有效性。实验结果表明，魔法可以在不使用任何图像标题训练对的情况下产生非常有前途的结果。

当代视觉标题模型通常是幻觉的对象，其实际上并不是一种场景，因为目视错误分类或过度依赖导致视觉信息与目标词汇词之间的语义不一致。最常见的方式是鼓励标题模型将生成的对象字或短语动态链接到图像的适当区域，即接地图像标题（GIC）。然而，GIC利用辅助任务（接地对象），这些任务（接地对象）没有解决对象幻觉的关键问题，即语义不一致。在本文中，我们对上面的问题进行了一种小说 - 利用视觉和语言模式之间的语义一致性。具体而言，我们提出了与GIC的共识RRAPH表示学习框架（CGRL），其纳入接地标题管道的共识表示。通过将可视图（例如，场景图）对准到图表中的节点和边的语言图来学习共识。通过对齐的共识，标题模型可以捕获正确的语言特征和视觉相关性，然后进一步接地适当的图像区域。我们验证了我们模型的有效性，对象幻觉（-9％主席）在Flickr30k实体数据集中显着下降。此外，我们的CGR还通过多种自动度量和人体评估评估，结果表明，该方法可以同时提高图像标题（+2.9苹果酒）和接地的性能（+2.3 f1loc）。

接地视频描述（GVD）促使标题模型动态地参加适当的视频区域（例如，对象）并生成描述。这样的设置可以帮助解释标题模型的决策，并防止模型在其描述中幻觉的对象词。然而，这种设计主要侧重于对象词生成，因此可能忽略细粒度信息并遭受缺失的视觉概念。此外，关系词（例如，“左转或右”）是通常的时空推断结果，即，这些单词不能在某些空间区域接地。为了解决上述限制，我们设计了GVD的新型关系图学习框架，其中旨在探索细粒度的视觉概念。此外，精细图可以被视为关系归纳知识，以帮助标题模型选择所需的相关信息来生成正确的单词。我们通过自动指标和人类评估验证我们模型的有效性，结果表明，我们的方法可以产生更细粒度和准确的描述，并解决了物体幻觉的问题。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

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.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.