Learning from changing tasks and sequential experience without forgetting the obtained knowledge is a challenging problem for artificial neural networks. In this work, we focus on two challenging problems in the paradigm of Continual Learning (CL) without involving any old data: (i) the accumulation of catastrophic forgetting caused by the gradually fading knowledge space from which the model learns the previous knowledge; (ii) the uncontrolled tug-of-war dynamics to balance the stability and plasticity during the learning of new tasks. In order to tackle these problems, we present Progressive Learning without Forgetting (PLwF) and a credit assignment regime in the optimizer. PLwF densely introduces model functions from previous tasks to construct a knowledge space such that it contains the most reliable knowledge on each task and the distribution information of different tasks, while credit assignment controls the tug-of-war dynamics by removing gradient conflict through projection. Extensive ablative experiments demonstrate the effectiveness of PLwF and credit assignment. In comparison with other CL methods, we report notably better results even without relying on any raw data.

Video captioning aims to generate natural language sentences that describe the given video accurately. Existing methods obtain favorable generation by exploring richer visual representations in encode phase or improving the decoding ability. However, the long-tailed problem hinders these attempts at low-frequency tokens, which rarely occur but carry critical semantics, playing a vital role in the detailed generation. In this paper, we introduce a novel Refined Semantic enhancement method towards Frequency Diffusion (RSFD), a captioning model that constantly perceives the linguistic representation of the infrequent tokens. Concretely, a Frequency-Aware Diffusion (FAD) module is proposed to comprehend the semantics of low-frequency tokens to break through generation limitations. In this way, the caption is refined by promoting the absorption of tokens with insufficient occurrence. Based on FAD, we design a Divergent Semantic Supervisor (DSS) module to compensate for the information loss of high-frequency tokens brought by the diffusion process, where the semantics of low-frequency tokens is further emphasized to alleviate the long-tailed problem. Extensive experiments indicate that RSFD outperforms the state-of-the-art methods on two benchmark datasets, i.e., MSR-VTT and MSVD, demonstrate that the enhancement of low-frequency tokens semantics can obtain a competitive generation effect. Code is available at https://github.com/lzp870/RSFD.

Let $\mathcal{D}$ be a dataset of smooth 3D-surfaces, partitioned into disjoint classes $\mathit{CL}_j$, $j= 1, \ldots, k$. We show how optimized diffeomorphic registration applied to large numbers of pairs $S,S' \in \mathcal{D}$ can provide descriptive feature vectors to implement automatic classification on $\mathcal{D}$, and generate classifiers invariant by rigid motions in $\mathbb{R}^3$. To enhance accuracy of automatic classification, we enrich the smallest classes $\mathit{CL}_j$ by diffeomorphic interpolation of smooth surfaces between pairs $S,S' \in \mathit{CL}_j$. We also implement small random perturbations of surfaces $S\in \mathit{CL}_j$ by random flows of smooth diffeomorphisms $F_t:\mathbb{R}^3 \to \mathbb{R}^3$. Finally, we test our automatic classification methods on a cardiology data base of discretized mitral valve surfaces.

我们研究Claire（一种差异性多形状，多-GPU图像注册算法和软件）的性能 - 在具有数十亿素素的大规模生物医学成像应用中。在这样的分辨率下，大多数用于差异图像注册的软件包非常昂贵。结果，从业人员首先要大量删除原始图像，然后使用现有工具进行注册。我们的主要贡献是对降采样对注册性能的影响的广泛分析。我们通过将用Claire获得的全分辨率注册与合成和现实成像数据集的低分辨率注册进行比较，研究了这种影响。我们的结果表明，完全分辨率的注册可以产生卓越的注册质量 - 但并非总是如此。例如，将合成图像从$ 1024^3 $减少到$ 256^3 $将骰子系数从92％降低到79％。但是，对于嘈杂或低对比度的高分辨率图像，差异不太明显。克莱尔不仅允许我们在几秒钟内注册临床相关大小的图像，而且还可以在合理的时间内以前所未有的分辨率注册图像。考虑的最高分辨率是$ 2816 \ times3016 \ times1162 $的清晰图像。据我们所知，这是有关此类决议中图像注册质量的首次研究。

本文研究了行人图像的新型隐私匿名问题，该问题保留了授权模型的个人身份信息（PII），并防止PII被第三方认可。常规的匿名方法不可避免地会导致语义信息丢失，从而导致数据实用性有限。此外，现有的学习匿名技术，同时保留各种身份 - 艾尔特尔维坦公用事业，将改变行人身份，因此不适合培训强大的重新识别模型。为了探索行人图像的隐私 - 实用性权衡取舍，我们提出了一个联合学习可逆的匿名框架，该框架可以可逆地生成全身匿名图像，而对人员重新识别任务的性能很小。核心思想是，我们采用常规方法生成的脱敏图像作为初始隐私的监督，并共同训练具有恢复解码器和身份不变模型的匿名编码器。我们进一步提出了一种渐进培训策略来改善绩效，迭代地升级了最初的匿名监督。实验进一步证明了我们的匿名行人图像对隐私保护的有效性，这在保留隐私时提高了重新识别性能。代码可在\ url {https://github.com/whuzjw/privacy-reid}中获得。

在电子商务平台中，如果赞助搜索显示出意外的广告项目，则用户不太可能使用有机搜索，这将是该平台的隐藏成本。为了将隐藏成本纳入拍卖机制，这有助于为该平台创造积极的增长，我们转向储备价设计，以决定我们是否出售流量，并在收入和用户体验之间建立健康的关系。我们提出了一个动态的储备价格设计框架，以更有效地销售流量，并以最低的用户体验成本销售流量，同时向广告商保留长期激励措施，以真实地揭示其估值。还提出了分布式算法在生产环境中使用十亿个比例数据计算储备价。离线评估和在线AB测试的实验表明，这是一种简单有效的方法，可适当地用于工业生产中。它已经完全部署在Lazada赞助的搜索的生产中。

视听扬声器日复速度旨在检测使用听觉和视觉信号时的``谁说话。现有的视听深度数据集主要专注于会议室或新闻工作室等室内环境，这些工作室与电影，纪录片和观众情景喜剧等许多情景中的野外视频完全不同。要创建一个能够有效地比较野外视频的日复速度方法的测试平台，我们向AVA电影数据集注释说话者深度标签，并创建一个名为AVA-AVD的新基准。由于不同的场景，复杂的声学条件和完全偏离屏幕扬声器，该基准是挑战。然而，如何处理偏离屏幕和屏幕上的扬声器仍然是一个关键挑战。为了克服它，我们提出了一种新的视听关系网络（AVR-Net），它引入了有效的模态掩模，以基于可见性捕获辨别信息。实验表明，我们的方法不仅可以优于最先进的方法，而且可以更加强大，因为改变屏幕扬声器的比率。消融研究证明了拟议的AVR-NET和尤其是日复一化的模态掩模的优点。我们的数据和代码将公开可用。

我们的工作针对自动分析，以量化细菌细菌群体的生长动力学。我们提出了一种创新的方法，通过自动化新的，特定的成本功能的自动化最小化对可变形细胞运动的框架跟踪。这种最小化由专用的玻尔兹曼机器（随机复发神经网络）实现。通过连续的两个成本函数的最小化，对细胞分裂的自动检测进行了类似的处理，从而交替地识别儿童对和父母的识别。我们使用（i）记录模拟细胞菌落的记录来验证提出的自动细胞跟踪算法，这些算法与微流体陷阱和（ii）真实数据密切模仿大肠杆菌的生长动力学。在一批1100个模拟图像框架上，每帧的单元格登记精度范围从94.5％到100％，平均水平很高。我们使用大肠杆菌菌落的实验图像序列（即实际数据）进行的初始测试也产生令人信服的结果，注册精度范围从90％到100％。

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for FOUR different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. Finally, some important yet under-investigated open issues are discussed.

This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the instance-wise supervision to approximate these properties, which aims at learning data augmentation invariant and instance spreadout features. To achieve this goal, we propose a novel instance based softmax embedding method, which directly optimizes the 'real' instance features on top of the softmax function. It achieves significantly faster learning speed and higher accuracy than all existing methods. The proposed method performs well for both seen and unseen testing categories with cosine similarity. It also achieves competitive performance even without pre-trained network over samples from fine-grained categories.