We summarize our TRECVID 2022 Ad-hoc Video Search (AVS) experiments. Our solution is built with two new techniques, namely Lightweight Attentional Feature Fusion (LAFF) for combining diverse visual / textual features and Bidirectional Negation Learning (BNL) for addressing queries that contain negation cues. In particular, LAFF performs feature fusion at both early and late stages and at both text and video ends to exploit diverse (off-the-shelf) features. Compared to multi-head self attention, LAFF is much more compact yet more effective. Its attentional weights can also be used for selecting fewer features, with the retrieval performance mostly preserved. BNL trains a negation-aware video retrieval model by minimizing a bidirectionally constrained loss per triplet, where a triplet consists of a given training video, its original description and a partially negated description. For video feature extraction, we use pre-trained CLIP, BLIP, BEiT, ResNeXt-101 and irCSN. As for text features, we adopt bag-of-words, word2vec, CLIP and BLIP. Our training data consists of MSR-VTT, TGIF and VATEX that were used in our previous participation. In addition, we automatically caption the V3C1 collection for pre-training. The 2022 edition of the TRECVID benchmark has again been a fruitful participation for the RUCMM team. Our best run, with an infAP of 0.262, is ranked at the second place teamwise.

图像操纵检测的关键研究问题是如何学习对新型数据中的操纵敏感的宽大功能，而特定于防止在真实图像上的误报。目前的研究强调了敏感性，特异性主要忽略了。在本文中，我们通过多视图特征学习和多尺度监督来解决两个方面。通过利用篡改区域周围的噪声分布和边界伪影，前者旨在学习语义 - 不可知，更广泛的特征。后者允许我们从真实的图像中学习以通过依赖于语义分割损耗的现有技术来考虑非凡的图像。我们的想法是由我们术语MVSS-Net及其增强版MVSS-Net ++的新网络实现。六个公共基准数据集的综合实验证明了MVSS-Net系列的可行性，以实现像素级和图像级操作检测。

视觉位置识别（VPR）不仅对于自动驾驶车辆的定位和映射至关重要，而且对于视力受损的人群的辅助导航至关重要。为了大规模启用长期VPR系统，需要解决一些挑战。首先，不同的应用程序可能需要不同的图像视图方向，例如自动驾驶汽车的前视图，而低视力人的侧视图。其次，由于行人和车辆身份信息的成像，大都市场景中的VPR通常会引起隐私问题，呼吁在VPR查询和数据库构建之前需要数据匿名化。这两个因素都可能导致VPR性能变化，而尚未得到很好的理解。 To study their influences, we present the NYU-VPR dataset that contains more than 200,000 images over a 2km by 2km area near the New York University campus, taken within the whole year of 2016. We present benchmark results on several popular VPR algorithms showing that对于当前的VPR方法，侧视观点明显更具挑战性，而数据匿名的影响几乎可以忽略不计，以及我们的假设解释和深入的分析。

Humans constantly interact with objects in daily life tasks. Capturing such processes and subsequently conducting visual inferences from a fixed viewpoint suffers from occlusions, shape and texture ambiguities, motions, etc. To mitigate the problem, it is essential to build a training dataset that captures free-viewpoint interactions. We construct a dense multi-view dome to acquire a complex human object interaction dataset, named HODome, that consists of $\sim$75M frames on 10 subjects interacting with 23 objects. To process the HODome dataset, we develop NeuralDome, a layer-wise neural processing pipeline tailored for multi-view video inputs to conduct accurate tracking, geometry reconstruction and free-view rendering, for both human subjects and objects. Extensive experiments on the HODome dataset demonstrate the effectiveness of NeuralDome on a variety of inference, modeling, and rendering tasks. Both the dataset and the NeuralDome tools will be disseminated to the community for further development.

强化学习方法作为一种有前途的技术在自由浮动太空机器人的运动计划中取得了卓越的成果。但是，由于计划维度的增加和系统动态耦合的加剧，双臂自由浮动太空机器人的运动计划仍然是一个开放的挑战。特别是，由于缺乏最终效果的姿势约束，当前的研究无法处理捕获非合作对象的任务。为了解决该问题，我们提出了一种新型算法，即有效的算法，以促进基于RL的方法有效提高计划准确性。我们的核心贡献是通过先验知识指导构建一项混合政策，并引入无限规范以构建更合理的奖励功能。此外，我们的方法成功地捕获了具有不同旋转速度的旋转对象。

现代神经网络能够在涉及对象分类和图像生成的许多任务中执行至少和人类。然而，人类难以察觉的小扰动可能会显着降低训练有素的深神经网络的性能。我们提供了分布稳健的优化（DRO）框架，其集成了基于人的图像质量评估方法，以设计对人类来说难以察觉而难以察觉的最佳攻击，而是针对深度神经网络造成显着损害。通过广泛的实验，我们表明我们的攻击算法比其他最先进的人类难以察觉的攻击方法产生更好的质量（对人类）的攻击。此外，我们证明了使用我们最佳设计的人类难以察觉的攻击的DRO培训可以改善图像分类中的群体公平。在最后，我们提供了一种算法实现，以显着加速DRO训练，这可能是独立的兴趣。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.