在深度学习的生态系统中，嘈杂的标签是不可避免的，但很麻烦，因为模型可以轻松地过度拟合它们。标签噪声有许多类型，例如对称，不对称和实例依赖性噪声（IDN），而IDN是唯一取决于图像信息的类型。鉴于标签错误很大程度上是由于图像中存在的视觉类别不足或模棱两可的信息引起的，因此对图像信息的这种依赖性使IDN成为可研究标签噪声的关键类型。为了提供一种有效的技术来解决IDN，我们提出了一种称为InstanceGM的新图形建模方法，该方法结合了判别和生成模型。实例GM的主要贡献是：i）使用连续的Bernoulli分布来培训生成模型，提供了重要的培训优势，ii）探索最先进的噪声标签歧视分类器来生成清洁标签来自实例依赖性嘈杂标签样品。 InstanceGM具有当前嘈杂的学习方法的竞争力，尤其是在使用合成和现实世界数据集的IDN基准测试中，我们的方法比大多数实验中的竞争对手都表现出更好的准确性。

对于机器人来说，拾取透明的对象仍然是一项具有挑战性的任务。透明对象（例如反射和折射）的视觉属性使依赖相机传感的当前抓握方法无法检测和本地化。但是，人类可以通过首先观察其粗剖面，然后戳其感兴趣的区域以获得良好的抓握轮廓来很好地处理透明的物体。受到这一点的启发，我们提出了一个新颖的视觉引导触觉框架，以抓住透明的物体。在拟议的框架中，首先使用分割网络来预测称为戳戳区域的水平上部区域，在该区域中，机器人可以在该区域戳入对象以获得良好的触觉读数，同时导致对物体状态的最小干扰。然后，使用高分辨率胶触觉传感器进行戳戳。鉴于触觉阅读有所改善的当地概况，计划掌握透明物体的启发式掌握。为了减轻对透明对象的现实世界数据收集和标记的局限性，构建了一个大规模逼真的合成数据集。广泛的实验表明，我们提出的分割网络可以预测潜在的戳戳区域，平均平均精度（地图）为0.360，而视觉引导的触觉戳戳可以显着提高抓地力成功率，从38.9％到85.2％。由于其简单性，我们提出的方法也可以被其他力量或触觉传感器采用，并可以用于掌握其他具有挑战性的物体。本文中使用的所有材料均可在https://sites.google.com/view/tactilepoking上获得。

透明的物体在我们的日常生活中广泛使用，因此机器人需要能够处理它们。但是，透明的物体遭受了光反射和折射的影响，这使得获得执行操控任务所需的准确深度图的挑战。在本文中，我们提出了一个基于负担能力的新型框架，用于深度重建和操纵透明物体，称为A4T。层次负担能力首先用于检测透明对象及其相关的负担，以编码对象不同部分的相对位置。然后，鉴于预测的负担映射，多步深度重建方法用于逐步重建透明对象的深度图。最后，使用重建的深度图用于基于负担的透明物体操纵。为了评估我们提出的方法，我们构建了一个真实的数据集trans-frans-frans-fans-and-trans-trans-frastance和透明对象的深度图，这是同类物体中的第一个。广泛的实验表明，我们提出的方法可以预测准确的负担能图，并显着改善了与最新方法相比的透明物体的深度重建，其根平方平方误差在0.097米中显着降低至0.042。此外，我们通过一系列机器人操纵实验在透明物体上进行了提出的方法的有效性。请参阅https://sites.google.com/view/affordance4trans的补充视频和结果。

在本文中，我们采用了最大化的互信息（MI）方法来解决无监督的二进制哈希代码的问题，以实现高效的跨模型检索。我们提出了一种新颖的方法，被称为跨模型信息最大散列（CMIMH）。首先，要学习可以保留模跨和跨间相似性的信息的信息，我们利用最近估计MI的变分的进步，以最大化二进制表示和输入特征之间的MI以及不同方式的二进制表示之间的MI。通过在假设由多变量Bernoulli分布模型的假设下联合最大化这些MIM，我们可以学习二进制表示，该二进制表示，其可以在梯度下降中有效地以微量批量方式有效地保留帧内和模态的相似性。此外，我们发现尝试通过学习与来自不同模式的相同实例的类似二进制表示来最小化模态差距，这可能导致更少的信息性表示。因此，在减少模态间隙和失去模态 - 私人信息之间平衡对跨模型检索任务很重要。标准基准数据集上的定量评估表明，该方法始终如一地优于其他最先进的跨模型检索方法。

该航运业是全球贸易和经济的重要组成部分，但为了确保遵守法律遵守和安全，需要监控。在本文中，我们提出了一种新的船型分类模型，将船舶传输数据与船舶图像组合在一起。我们的方法的主要组成部分是R-CNN深神经网络的速度更快，具有IF-THE-DEL规则的神经模糊系统。我们使用现实世界数据评估我们的模型，并展示这种组合的优势，同时也将其与其他方法进行比较。结果表明，与我们考虑的下一个最佳模型相比，我们的模型可以将预测分数增加到15.4 \％，同时也保持与普通黑匣子方法相反的解释性。

本文推动了在图像中分解伪装区域的信封，成了有意义的组件，即伪装的实例。为了促进伪装实例分割的新任务，我们将在数量和多样性方面引入DataSet被称为Camo ++，该数据集被称为Camo ++。新数据集基本上增加了具有分层像素 - 明智的地面真理的图像的数量。我们还为伪装实例分割任务提供了一个基准套件。特别是，我们在各种场景中对新构造的凸轮++数据集进行了广泛的评估。我们还提出了一种伪装融合学习（CFL）伪装实例分割框架，以进一步提高最先进的方法的性能。数据集，模型，评估套件和基准测试将在我们的项目页面上公开提供：https://sites.google.com/view/ltnghia/research/camo_plus_plus

Most existing text-video retrieval methods focus on cross-modal matching between the visual content of offline videos and textual query sentences. However, in real scenarios, online videos are frequently accompanied by relevant text information such as titles, tags, and even subtitles, which can be utilized to match textual queries. This inspires us to generate associated captions from offline videos to help with existing text-video retrieval methods. To do so, we propose to use the zero-shot video captioner with knowledge of pre-trained web-scale models (e.g., CLIP and GPT-2) to generate captions for offline videos without any training. Given the captions, one question naturally arises: what can auxiliary captions do for text-video retrieval? In this paper, we present a novel framework Cap4Video, which makes use of captions from three aspects: i) Input data: The video and captions can form new video-caption pairs as data augmentation for training. ii) Feature interaction: We perform feature interaction between video and caption to yield enhanced video representations. iii) Output score: The Query-Caption matching branch can be complementary to the original Query-Video matching branch for text-video retrieval. We conduct thorough ablation studies to demonstrate the effectiveness of our method. Without any post-processing, our Cap4Video achieves state-of-the-art performance on MSR-VTT (51.4%), VATEX (66.6%), MSVD (51.8%), and DiDeMo (52.0%).

The problem of detecting the Out-of-Distribution (OoD) inputs is of paramount importance for Deep Neural Networks. It has been previously shown that even Deep Generative Models that allow estimating the density of the inputs may not be reliable and often tend to make over-confident predictions for OoDs, assigning to them a higher density than to the in-distribution data. This over-confidence in a single model can be potentially mitigated with Bayesian inference over the model parameters that take into account epistemic uncertainty. This paper investigates three approaches to Bayesian inference: stochastic gradient Markov chain Monte Carlo, Bayes by Backpropagation, and Stochastic Weight Averaging-Gaussian. The inference is implemented over the weights of the deep neural networks that parameterize the likelihood of the Variational Autoencoder. We empirically evaluate the approaches against several benchmarks that are often used for OoD detection: estimation of the marginal likelihood utilizing sampled model ensemble, typicality test, disagreement score, and Watanabe-Akaike Information Criterion. Finally, we introduce two simple scores that demonstrate the state-of-the-art performance.

With the proliferation of deep generative models, deepfakes are improving in quality and quantity everyday. However, there are subtle authenticity signals in pristine videos, not replicated by SOTA GANs. We contrast the movement in deepfakes and authentic videos by motion magnification towards building a generalized deepfake source detector. The sub-muscular motion in faces has different interpretations per different generative models which is reflected in their generative residue. Our approach exploits the difference between real motion and the amplified GAN fingerprints, by combining deep and traditional motion magnification, to detect whether a video is fake and its source generator if so. Evaluating our approach on two multi-source datasets, we obtain 97.17% and 94.03% for video source detection. We compare against the prior deepfake source detector and other complex architectures. We also analyze the importance of magnification amount, phase extraction window, backbone network architecture, sample counts, and sample lengths. Finally, we report our results for different skin tones to assess the bias.

During training, reinforcement learning systems interact with the world without considering the safety of their actions. When deployed into the real world, such systems can be dangerous and cause harm to their surroundings. Often, dangerous situations can be mitigated by defining a set of rules that the system should not violate under any conditions. For example, in robot navigation, one safety rule would be to avoid colliding with surrounding objects and people. In this work, we define safety rules in terms of the relationships between the agent and objects and use them to prevent reinforcement learning systems from performing potentially harmful actions. We propose a new safe epsilon-greedy algorithm that uses safety rules to override agents' actions if they are considered to be unsafe. In our experiments, we show that a safe epsilon-greedy policy significantly increases the safety of the agent during training, improves the learning efficiency resulting in much faster convergence, and achieves better performance than the base model.