Learning effective motion features is an essential pursuit of video representation learning. This paper presents a simple yet effective sample construction strategy to boost the learning of motion features in video contrastive learning. The proposed method, dubbed Motion-focused Quadruple Construction (MoQuad), augments the instance discrimination by meticulously disturbing the appearance and motion of both the positive and negative samples to create a quadruple for each video instance, such that the model is encouraged to exploit motion information. Unlike recent approaches that create extra auxiliary tasks for learning motion features or apply explicit temporal modelling, our method keeps the simple and clean contrastive learning paradigm (i.e.,SimCLR) without multi-task learning or extra modelling. In addition, we design two extra training strategies by analyzing initial MoQuad experiments. By simply applying MoQuad to SimCLR, extensive experiments show that we achieve superior performance on downstream tasks compared to the state of the arts. Notably, on the UCF-101 action recognition task, we achieve 93.7% accuracy after pre-training the model on Kinetics-400 for only 200 epochs, surpassing various previous methods

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Explainability of Graph Neural Networks (GNNs) is critical to various GNN applications but remains an open challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining approaches focus on only one of the two aspects, necessity or sufficiency, or a trade-off between the two. To search for the most necessary and sufficient explanation, the Probability of Necessity and Sufficiency (PNS) can be applied since it can mathematically quantify the necessity and sufficiency of an explanation. Nevertheless, the difficulty of obtaining PNS due to non-monotonicity and the challenge of counterfactual estimation limits its wide use. To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound. Specifically, we employ nearest neighbor matching to generate counterfactual samples for the features, which is different from the random perturbation. In particular, NSEG combines the edges and node features to generate an explanation, where the common edge explanation is a special case of the combined explanation. Empirical study shows that NSEG achieves excellent performance in generating the most necessary and sufficient explanations among a series of state-of-the-art methods.

We revisit a simple Learning-from-Scratch baseline for visuo-motor control that uses data augmentation and a shallow ConvNet. We find that this baseline has competitive performance with recent methods that leverage frozen visual representations trained on large-scale vision datasets.

We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various video synthesis tasks with a single model. We introduce a 3D tokenizer to quantize a video into spatial-temporal visual tokens and propose an embedding method for masked video token modeling to facilitate multi-task learning. We conduct extensive experiments to demonstrate the quality, efficiency, and flexibility of MAGVIT. Our experiments show that (i) MAGVIT performs favorably against state-of-the-art approaches and establishes the best-published FVD on three video generation benchmarks, including the challenging Kinetics-600. (ii) MAGVIT outperforms existing methods in inference time by two orders of magnitude against diffusion models and by 60x against autoregressive models. (iii) A single MAGVIT model supports ten diverse generation tasks and generalizes across videos from different visual domains. The source code and trained models will be released to the public at https://magvit.cs.cmu.edu.

With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead. In this paper, we propose a contactless approach for monitoring patients' blood oxygen at home, simply by analyzing the radio signals in the room, without any wearable devices. We extract the patients' respiration from the radio signals that bounce off their bodies and devise a novel neural network that infers a patient's oxygen estimates from their breathing signal. Our model, called \emph{Gated BERT-UNet}, is designed to adapt to the patient's medical indices (e.g., gender, sleep stages). It has multiple predictive heads and selects the most suitable head via a gate controlled by the person's physiological indices. Extensive empirical results show that our model achieves high accuracy on both medical and radio datasets.

In this paper we present a novel multi-attribute face manipulation method based on textual descriptions. Previous text-based image editing methods either require test-time optimization for each individual image or are restricted to single attribute editing. Extending these methods to multi-attribute face image editing scenarios will introduce undesired excessive attribute change, e.g., text-relevant attributes are overly manipulated and text-irrelevant attributes are also changed. In order to address these challenges and achieve natural editing over multiple face attributes, we propose a new decoupling training scheme where we use group sampling to get text segments from same attribute categories, instead of whole complex sentences. Further, to preserve other existing face attributes, we encourage the model to edit the latent code of each attribute separately via an entropy constraint. During the inference phase, our model is able to edit new face images without any test-time optimization, even from complex textual prompts. We show extensive experiments and analysis to demonstrate the efficacy of our method, which generates natural manipulated faces with minimal text-irrelevant attribute editing. Code and pre-trained model will be released.

实体对齐（EA）的目的是在不同的知识图（kgs）中找到指代现实世界中同一对象的实体。最近的研究结合了时间信息，以增强KGS的表示。暂时KGS（TKG）之间的EA的现有方法利用时间感知的注意机制将关系和时间信息纳入实体嵌入中。该方法通过使用时间信息优于先前的方法。但是，我们认为，由于大多数TKG具有统一的时间表示，因此不必学习kgs中的时间信息的嵌入。因此，我们提出了一个简单的图形神经网络（GNN）模型，并结合了时间信息匹配机制，该模型以更少的时间和更少的参数实现了更好的性能。此外，由于对齐种子很难在现实世界应用中标记，因此我们还提出了一种通过TKG的时间信息生成无监督比对种子的方法。公共数据集的广泛实验表明，我们的监督方法显着优于先前的方法，而无监督的方法具有竞争性能。

深度学习取得了长足的进步，用于图像中的对象检测。对象检测的检测准确性和计算成本取决于图像的空间分辨率，这可能会受到相机和存储注意事项的约束。压缩通常是通过减少空间或幅度分辨率或有时两者都对性能的众所周知的影响来实现的。检测精度还取决于感兴趣的对象与摄像机的距离。我们的工作研究了空间和振幅分辨率以及对象距离对物体检测准确性和计算成本的影响。我们开发了Yolov5（ra-Yolo）的分辨率 - 自适应变体，该变体基于输入图像的空间分辨率，它在特征金字塔和检测头中变化。为了训练和评估这种新方法，我们通过结合TJU和Eurocity数据集的图像来创建具有不同空间和振幅分辨率的图像数据集，并通过应用空间调整和压缩来生成不同的分辨率。我们首先表明Ra-Yolo在各种空间分辨率上实现了检测准确性和推理时间之间的良好权衡。然后，我们使用拟议的RA-YOLO模型评估空间和振幅分辨率对物体检测准确性的影响。我们证明，导致最高检测精度的最佳空间分辨率取决于“耐受性”图像大小。我们进一步评估了对象到摄像机对检测准确性的影响，并表明较高的空间分辨率可实现更大的检测范围。这些结果为选择图像空间分辨率和压缩设置提供了重要的指南，这些分辨率和压缩设置基于可用的带宽，存储，所需的推理时间和/或所需的检测范围，在实际应用中。

本文提出了一个简单而有效的框架蒙版，该框架将新提出的掩盖自distillation纳入对比的语言图像预处理中。掩盖自distillation的核心思想是将表示从完整的图像提取到蒙版图像预测的表示形式。这种合并享有两个重要的好处。首先，掩盖的自我验证目标是本地贴片表示学习，这与视觉对比度的互补，专注于与文本相关的表示。二，掩盖的自我验证也与视觉语言对比符合训练目标的视野对比是一致的。视觉编码器用于功能对齐，因此能够学习本地语义从该语言中获得间接监督。我们提供了专门设计的实验，并进行了全面的分析，以验证这两个好处。从经验上讲，我们表明，当MaskClip应用于各种具有挑战性的下游任务时，可以在线性探测，填充和零拍摄中取得卓越的结果，并在语言编码器的指导下取得了卓越的结果。