In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget. Due to the limited feedback information, existing query-based black-box attack methods often require many queries for attacking each benign example. To reduce query cost, we propose to utilize the feedback information across historical attacks, dubbed example-level adversarial transferability. Specifically, by treating the attack on each benign example as one task, we develop a meta-learning framework by training a meta-generator to produce perturbations conditioned on benign examples. When attacking a new benign example, the meta generator can be quickly fine-tuned based on the feedback information of the new task as well as a few historical attacks to produce effective perturbations. Moreover, since the meta-train procedure consumes many queries to learn a generalizable generator, we utilize model-level adversarial transferability to train the meta-generator on a white-box surrogate model, then transfer it to help the attack against the target model. The proposed framework with the two types of adversarial transferability can be naturally combined with any off-the-shelf query-based attack methods to boost their performance, which is verified by extensive experiments.

Recently, many deep learning based beamformers have been proposed for multi-channel speech separation. Nevertheless, most of them rely on extra cues known in advance, such as speaker feature, face image or directional information. In this paper, we propose an end-to-end beamforming network for direction guided speech separation given merely the mixture signal, namely MIMO-DBnet. Specifically, we design a multi-channel input and multiple outputs architecture to predict the direction-of-arrival based embeddings and beamforming weights for each source. The precisely estimated directional embedding provides quite effective spatial discrimination guidance for the neural beamformer to offset the effect of phase wrapping, thus allowing more accurate reconstruction of two sources' speech signals. Experiments show that our proposed MIMO-DBnet not only achieves a comprehensive decent improvement compared to baseline systems, but also maintain the performance on high frequency bands when phase wrapping occurs.

Recently, a surge of high-quality 3D-aware GANs have been proposed, which leverage the generative power of neural rendering. It is natural to associate 3D GANs with GAN inversion methods to project a real image into the generator's latent space, allowing free-view consistent synthesis and editing, referred as 3D GAN inversion. Although with the facial prior preserved in pre-trained 3D GANs, reconstructing a 3D portrait with only one monocular image is still an ill-pose problem. The straightforward application of 2D GAN inversion methods focuses on texture similarity only while ignoring the correctness of 3D geometry shapes. It may raise geometry collapse effects, especially when reconstructing a side face under an extreme pose. Besides, the synthetic results in novel views are prone to be blurry. In this work, we propose a novel method to promote 3D GAN inversion by introducing facial symmetry prior. We design a pipeline and constraints to make full use of the pseudo auxiliary view obtained via image flipping, which helps obtain a robust and reasonable geometry shape during the inversion process. To enhance texture fidelity in unobserved viewpoints, pseudo labels from depth-guided 3D warping can provide extra supervision. We design constraints aimed at filtering out conflict areas for optimization in asymmetric situations. Comprehensive quantitative and qualitative evaluations on image reconstruction and editing demonstrate the superiority of our method.

由于深层网络的计算复杂性和功率约束的移动硬件的计算复杂性，因此在移动设备上实现神经视频编解码器的潜力是一项巨大的技术挑战。我们通过利用高通公司的技术和创新来证明可行性，从而弥合了从基于神经网络的编解码器模拟在壁式工作站运行的差距，再到由Snapdragon技术供电的移动设备上的实时操作。我们显示有史以来第一个在商用手机上运行的框架间神经视频解码器，实时解码高清视频，同时保持低比特率和高视觉质量。

最近，几种基于空间内存的方法已经验证了将中间框架及其面具作为内存有助于将视频中的目标对象细分目标对象。但是，它们主要集中于当前帧和内存框架之间的更好匹配，而无需明确关注内存质量。因此，较差的分割面罩的框架容易被记住，这导致了分割掩盖误差问题并进一步影响分割性能。此外，随着帧数的增长，内存框架的线性增加还限制了模型处理长视频的能力。为此，我们提出了一个质量感知的动态内存网络（QDMN）来评估每个帧的分割质量，从而使内存库可以选择性地存储准确的分段框架，以防止误差积累问题。然后，我们将细分质量与时间一致性相结合，以动态更新内存库以提高模型的实用性。我们的QDMN没有任何铃铛和哨子，在戴维斯和YouTube-Vos基准测试中都取得了新的最新性能。此外，广泛的实验表明，提议的质量评估模块（QAM）可以作为通用插件应用于基于内存的方法，并显着提高性能。我们的源代码可在https://github.com/workforai/qdmn上找到。

最近的面部生成方法试图根据给定的轮廓条件（例如低分辨率图像或草图）合成面部。但是，身份歧义的问题仍未解决，通常在轮廓太模糊而无法提供可靠的身份信息时（例如，当其分辨率极低时）发生。因此，可行的图像恢复解决方案可能是无限的。在这项工作中，我们提出了一个新颖的框架，该框架采用轮廓和一个额外的图像，将身份指定为输入，轮廓可以是各种方式，包括低分辨率图像，草图和语义标签映射。具体而言，我们提出了一种新颖的双重编码架构，其中身份编码器提取与身份相关的特征，并伴随着主编码器，以获取粗糙的轮廓信息并将所有信息进一步融合在一起。编码器输出迭代地馈入预训练的样式Generator，直到获得令人满意的结果为止。据我们所知，这是在多模式轮廓图像中获得身份引导的面部生成的第一部作品。此外，我们的方法可以以1024 $ \ times $ 1024的分辨率产生照片现实效果。

在这项工作中，我们解决了主动摄像机定位的问题，该问题可积极控制相机运动以实现精确的相机姿势。过去的解决方案主要基于马尔可夫定位，从而减少了定位的位置摄像头的不确定性。这些方法将摄像机定位在离散姿势空间中，并且对定位驱动的场景属性不可知，从而限制了相机姿势的精度。我们建议通过由被动和主动定位模块组成的新型活动相机定位算法克服这些局限性。前者通过建立对点的摄像头通信来优化连续姿势空间中的相机姿势。后者明确对场景和相机不确定性组件进行建模，以计划正确的摄像头姿势估计的正确路径。我们在合成和扫描现实世界室内场景的挑战性本地化场景上验证了算法。实验结果表明，我们的算法表现优于基于马尔可夫定位的最先进的方法和优质相机姿势精度的其他方法。代码和数据在https://github.com/qhfang/accurateacl上发布。

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^d$, we obtain the following results (up to polylogarithmic factors). We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text{opt}} \in [d^{-1}, d^{-1/4}]$, our algorithm matches the state-of-the-art oracle depth of [BJLLS19] while maintaining the optimal total work of stochastic gradient descent. We give an $(\epsilon_{\text{dp}}, \delta)$-differentially private algorithm which, given $n$ samples of Lipschitz loss functions, obtains near-optimal optimization error and makes $\min(n, n^2\epsilon_{\text{dp}}^2 d^{-1}) + \min(n^{4/3}\epsilon_{\text{dp}}^{1/3}, (nd)^{2/3}\epsilon_{\text{dp}}^{-1})$ queries to the gradients of these functions. In the regime $d \le n \epsilon_{\text{dp}}^{2}$, where privacy comes at no cost in terms of the optimal loss up to constants, our algorithm uses $n + (nd)^{2/3}\epsilon_{\text{dp}}^{-1}$ queries and improves recent advancements of [KLL21, AFKT21]. In the moderately low-dimensional setting $d \le \sqrt n \epsilon_{\text{dp}}^{3/2}$, our query complexity is near-linear.

New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This technology provides more flexibility for users to support both deep learning training and inference workloads, but efficiently utilizing it can still be challenging. The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts. To achieve this vision, the paper presents MIGPerf, an open-source tool that streamlines the benchmark study for MIG. Using MIGPerf, the authors conduct a series of experiments, including deep learning training and inference characterization on MIG, GPU sharing characterization, and framework compatibility with MIG. The results of these experiments provide new insights and guidance for users to effectively employ MIG, and lay the foundation for further research on the orchestration of hybrid training and inference workloads on MIGs. The code and results are released on https://github.com/MLSysOps/MIGProfiler. This work is still in progress and more results will be published soon.