Deep learning has revolutionized human society, yet the black-box nature of deep neural networks hinders further application to reliability-demanded industries. In the attempt to unpack them, many works observe or impact internal variables to improve the model's comprehensibility and transparency. However, existing methods rely on intuitive assumptions and lack mathematical guarantees. To bridge this gap, we introduce Bort, an optimizer for improving model explainability with boundedness and orthogonality constraints on model parameters, derived from the sufficient conditions of model comprehensibility and transparency. We perform reconstruction and backtracking on the model representations optimized by Bort and observe an evident improvement in model explainability. Based on Bort, we are able to synthesize explainable adversarial samples without additional parameters and training. Surprisingly, we find Bort constantly improves the classification accuracy of various architectures including ResNet and DeiT on MNIST, CIFAR-10, and ImageNet.

Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators. In this work, we propose the Denoising Diffusion Null-Space Model (DDNM), a novel zero-shot framework for arbitrary linear IR problems, including but not limited to image super-resolution, colorization, inpainting, compressed sensing, and deblurring. DDNM only needs a pre-trained off-the-shelf diffusion model as the generative prior, without any extra training or network modifications. By refining only the null-space contents during the reverse diffusion process, we can yield diverse results satisfying both data consistency and realness. We further propose an enhanced and robust version, dubbed DDNM+, to support noisy restoration and improve restoration quality for hard tasks. Our experiments on several IR tasks reveal that DDNM outperforms other state-of-the-art zero-shot IR methods. We also demonstrate that DDNM+ can solve complex real-world applications, e.g., old photo restoration.

Robotic force-based compliance control is a preferred approach to achieve high-precision assembly tasks. When the geometric features of assembly objects are asymmetric or irregular, reinforcement learning (RL) agents are gradually incorporated into the compliance controller to adapt to complex force-pose mapping which is hard to model analytically. Since force-pose mapping is strongly dependent on geometric features, a compliance controller is only optimal for current geometric features. To reduce the learning cost of assembly objects with different geometric features, this paper is devoted to answering how to reconfigure existing controllers for new assembly objects with different geometric features. In this paper, model-based parameters are first reconfigured based on the proposed Equivalent Theory of Compliance Law (ETCL). Then the RL agent is transferred based on the proposed Weighted Dimensional Policy Distillation (WDPD) method. The experiment results demonstrate that the control reconfiguration method costs less time and achieves better control performance, which confirms the validity of proposed methods.

与周围摄像机的3D对象检测是自动驾驶的有希望的方向。在本文中，我们提出了Simmod，这是用于解决问题的多相对象检测的简单基线。为了合并多视图信息，并基于以前对单眼3D对象检测的努力，该框架建立在样本的对象建议基础上，并旨在以两阶段的方式工作。首先，我们提取多尺度特征，并在每个单眼图像上生成透视对象建议。其次，多视图提案进行了汇总，然后在DETR3D式中使用多视图和多尺度视觉特征进行迭代完善。精制的提案被端到端解码为检测结果。为了进一步提高性能，我们将辅助分支与提案生成并列以增强特征学习。此外，我们设计了目标过滤和教师强迫的方法，以促进两阶段训练的一致性。我们对Nuscenes的3D对象检测基准进行了广泛的实验，以证明Simmod的有效性并实现新的最新性能。代码将在https://github.com/zhangyp15/simmod上找到。

与常规知识蒸馏（KD）不同，自我KD允许网络在没有额外网络的任何指导的情况下向自身学习知识。本文提议从图像混合物（Mixskd）执行自我KD，将这两种技术集成到统一的框架中。 Mixskd相互蒸馏以图形和概率分布在随机的原始图像和它们的混合图像之间以有意义的方式。因此，它通过对混合图像进行监督信号进行建模来指导网络学习跨图像知识。此外，我们通过汇总多阶段功能图来构建一个自学老师网络，以提供软标签以监督骨干分类器，从而进一步提高自我增强的功效。图像分类和转移学习到对象检测和语义分割的实验表明，混合物KD优于其他最先进的自我KD和数据增强方法。该代码可在https://github.com/winycg/self-kd-lib上找到。

最近，基于骨架的动作识别已经取得了快速进步和卓越的性能。在本文中，我们在跨数据集设置下调查了这个问题，这是现实情况下的新，务实且具有挑战性的任务。遵循无监督的域适应（UDA）范式，该动作标签仅在源数据集上可用，但在训练阶段的目标数据集中无法使用。与UDA的常规基于对抗性学习的方法不同，我们利用一个自学计划来减少两个基于骨架的动作数据集之间的域移动。我们的灵感来自Compism，Compism是20世纪初期的艺术类型，它破坏并重新组装了物体以传达更大的背景。通过分割和定制时间段或人体部位，我们设计了两个自制的学习分类任务，以探索基于骨架的动作的时间和空间依赖性，并提高模型的概括能力。我们在六个基于骨架的动作识别的数据集上进行实验，包括三个大规模数据集（NTU RGB+D，PKU-MMD和动力学），在其中建立了新的跨数据库设置和基准。广泛的结果表明，我们的方法优于最先进的方法。我们的模型和所有比较方法的源代码均可在https://github.com/shanice-l/st-cubism上获得。

视觉和语言导航（VLN）是一个任务，代理在人类指令下的体现室内环境中导航。以前的作品忽略了样本难度的分布，我们认为这可能会降低他们的代理表现。为了解决这个问题，我们为VLN任务提出了一种基于课程的基于课程的培训范式，可以平衡人类的先验知识和特工关于培训样本的学习进度。我们开发课程设计原则，并重新安排基准房间到室（R2R）数据集，以使其适用于课程培训。实验表明，我们的方法是模型 - 不可知的，可以显着提高当前最先进的导航剂的性能，概括性和培训效率而不会增加模型复杂性。

最近，生成的数据无量子化作为一种​​实用的方法，将神经网络压缩到低位宽度而不访问真实数据。它通过利用其全精密对应物的批量归一化（BN）统计来生成数据来量化网络。然而，我们的研究表明，在实践中，BN统计的合成数据在分布和样品水平时严重均匀化，这导致量化网络的严重劣化。本文提出了一种通用不同的样本生成（DSG）方案，用于生成无数据的训练后量化和量化感知培训，以减轻有害的均质化。在我们的DSG中，我们首先将统计对齐缩写为BN层中的功能，以放宽分配约束。然后，我们加强特定BN层对不同样品的损失影响，并抑制了生成过程中样品之间的相关性，分别从统计和空间角度分别多样化样本。广泛的实验表明，对于大规模的图像分类任务，我们的DSG可以始终如一地优于各种神经结构上的现有数据无数据量化方法，尤其是在超低比特宽度下（例如，在W4A4设置下的22％的增益下）。此外，由我们的DSG引起的数据多样化引起了各种量化方法的一般增益，证明了多样性是无数据量化的高质量合成数据的重要特性。

With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision. While a variety of new tasks and algorithms have been proposed recently, there are growing hunger for data resources involved in high-quality data, fine-grained labels, and diverse environments. In this paper, we present FLAG3D, a large-scale 3D fitness activity dataset with language instruction containing 180K sequences of 60 categories. FLAG3D features the following three aspects: 1) accurate and dense 3D human pose captured from advanced MoCap system to handle the complex activity and large movement, 2) detailed and professional language instruction to describe how to perform a specific activity, 3) versatile video resources from a high-tech MoCap system, rendering software, and cost-effective smartphones in natural environments. Extensive experiments and in-depth analysis show that FLAG3D contributes great research value for various challenges, such as cross-domain human action recognition, dynamic human mesh recovery, and language-guided human action generation. Our dataset and source code will be publicly available at https://andytang15.github.io/FLAG3D.

With the rising industrial attention to 3D virtual modeling technology, generating novel 3D content based on specified conditions (e.g. text) has become a hot issue. In this paper, we propose a new generative 3D modeling framework called Diffusion-SDF for the challenging task of text-to-shape synthesis. Previous approaches lack flexibility in both 3D data representation and shape generation, thereby failing to generate highly diversified 3D shapes conforming to the given text descriptions. To address this, we propose a SDF autoencoder together with the Voxelized Diffusion model to learn and generate representations for voxelized signed distance fields (SDFs) of 3D shapes. Specifically, we design a novel UinU-Net architecture that implants a local-focused inner network inside the standard U-Net architecture, which enables better reconstruction of patch-independent SDF representations. We extend our approach to further text-to-shape tasks including text-conditioned shape completion and manipulation. Experimental results show that Diffusion-SDF is capable of generating both high-quality and highly diversified 3D shapes that conform well to the given text descriptions. Diffusion-SDF has demonstrated its superiority compared to previous state-of-the-art text-to-shape approaches.