Online Temporal Action Localization (On-TAL) aims to immediately provide action instances from untrimmed streaming videos. The model is not allowed to utilize future frames and any processing techniques to modify past predictions, making On-TAL much more challenging. In this paper, we propose a simple yet effective framework, termed SimOn, that learns to predict action instances using the popular Transformer architecture in an end-to-end manner. Specifically, the model takes the current frame feature as a query and a set of past context information as keys and values of the Transformer. Different from the prior work that uses a set of outputs of the model as past contexts, we leverage the past visual context and the learnable context embedding for the current query. Experimental results on the THUMOS14 and ActivityNet1.3 datasets show that our model remarkably outperforms the previous methods, achieving a new state-of-the-art On-TAL performance. In addition, the evaluation for Online Detection of Action Start (ODAS) demonstrates the effectiveness and robustness of our method in the online setting. The code is available at https://github.com/TuanTNG/SimOn

在线立体声适应解决了由合成（训练）和真实（测试）数据集之间的不同环境引起的域移位问题，以迅速适应动态现实世界应用程序（例如自动驾驶）中的立体声模型。但是，以前的方法通常无法抵消与动态物体有关的特定区域，并具有更严重的环境变化。为了减轻此问题，我们建议将辅助点选择性网络纳入称为PointFix的元学习框架中，以提供对在线立体声适应的立体声模型的强大初始化。简而言之，我们的辅助网络学会通过通过元级别有效地反向传播局部信息来固定局部变体，从而实现基线模型的稳健初始化。该网络是模型 - 不合时宜的，因此可以以任何插件的方式以任何形式的架构使用。我们进行了广泛的实验，以在三个适应设置（例如短期，中和长期序列）下验证我们的方法的有效性。实验结果表明，辅助网络对基本立体声模型的适当初始化使我们的学习范式在推理时达到了最新的性能。

本文解决了单幅图像下雨的问题，即从一张多雨工件遮挡的单个图像中恢复清洁和无雨背景场景的任务。虽然最近的进步采用现实世界的延期数据来克服对雨水清洁图像的需要，但它们仅限于充分利用时间流逝数据。主要原因是，在网络架构方面，由于缺乏内存组件，它们无法在训练期间在训练期间捕获长期雨条纹信息。为了解决这个问题，我们提出了一种基于内存网络的新颖网络架构，该内存网络明确有助于在时间流逝数据中捕获长期雨条纹信息。我们的网络包括编码器 - 解码器网络和存储器网络。从编码器中提取的功能被读取并更新在包含几个存储器项中以存储雨条目感知功能表示的几个存储器项。利用读/更新操作，存储器网络根据查询检索相关的存储器项，使得存储器项能够表示在时间流逝数据中包括的各种雨条纹。为了提高内存特征的辨别力，我们还通过擦除背景信息，提出了一种用于仅捕获存储网络中的雨条信息的新型背景选择性美白（BSW）损耗。标准基准测试的实验结果证明了我们方法的有效性和优越性。

我们解决了几次拍摄语义分割（FSS）的问题，该问题旨在通过一些带有一些注释的样本分段为目标图像中的新型类对象。尽管通过结合基于原型的公制学习来进行最近的进步，但由于其特征表示差，现有方法仍然显示出在极端内部对象变化和语义相似的类别对象下的有限性能。为了解决这个问题，我们提出了一种针对FSS任务定制的双重原型对比学习方法，以有效地捕获代表性的语义。主要思想是通过增加阶级距离来鼓励原型更差异，同时减少了原型特征空间中的课堂距离。为此，我们首先向类别特定的对比丢失丢失具有动态原型字典，该字典字典存储在训练期间的类感知原型，从而实现相同的类原型和不同的类原型是不同的。此外，我们通过压缩每集内语义类的特征分布来提高类别无话的对比损失，以提高未经看不见的类别的概念能力。我们表明，所提出的双重原型对比学习方法优于Pascal-5i和Coco-20i数据集的最先进的FSS方法。该代码可用于：https：//github.com/kwonjunn01/dpcl1。

我们提出了一种新的成本聚合网络，称为成本聚合变压器（CAT），在语义类似的图像之间找到密集的对应关系，其中具有大型类内外观和几何变化构成的额外挑战。成本聚合是匹配任务的一个非常重要的过程，匹配精度取决于其输出的质量。与寻址成本聚集的手工制作或基于CNN的方法相比，缺乏严重变形的鲁棒性或继承了由于接受领域有限而无法区分错误匹配的CNN的限制，猫探讨了初始相关图之间的全球共识一些建筑设计的帮助，使我们能够充分利用自我关注机制。具体地，我们包括外观亲和力建模，以帮助成本聚合过程，以消除嘈杂的初始相关映射并提出多级聚合，以有效地从分层特征表示中捕获不同的语义。然后，我们与交换自我关注技术和残留连接相结合，不仅要强制执行一致的匹配，而且还可以缓解学习过程，我们发现这些结果导致了表观性能提升。我们进行实验，以证明拟议模型在最新方法中的有效性，并提供广泛的消融研究。代码和培训的型号可以在https://github.com/sunghwanhong/cats提供。

From smoothly pursuing moving objects to rapidly shifting gazes during visual search, humans employ a wide variety of eye movement strategies in different contexts. While eye movements provide a rich window into mental processes, building generative models of eye movements is notoriously difficult, and to date the computational objectives guiding eye movements remain largely a mystery. In this work, we tackled these problems in the context of a canonical spatial planning task, maze-solving. We collected eye movement data from human subjects and built deep generative models of eye movements using a novel differentiable architecture for gaze fixations and gaze shifts. We found that human eye movements are best predicted by a model that is optimized not to perform the task as efficiently as possible but instead to run an internal simulation of an object traversing the maze. This not only provides a generative model of eye movements in this task but also suggests a computational theory for how humans solve the task, namely that humans use mental simulation.

Yes. In this paper, we investigate strong lottery tickets in generative models, the subnetworks that achieve good generative performance without any weight update. Neural network pruning is considered the main cornerstone of model compression for reducing the costs of computation and memory. Unfortunately, pruning a generative model has not been extensively explored, and all existing pruning algorithms suffer from excessive weight-training costs, performance degradation, limited generalizability, or complicated training. To address these problems, we propose to find a strong lottery ticket via moment-matching scores. Our experimental results show that the discovered subnetwork can perform similarly or better than the trained dense model even when only 10% of the weights remain. To the best of our knowledge, we are the first to show the existence of strong lottery tickets in generative models and provide an algorithm to find it stably. Our code and supplementary materials are publicly available.

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.

FSS(Few-shot segmentation)~aims to segment a target class with a small number of labeled images (support Set). To extract information relevant to target class, a dominant approach in best performing FSS baselines removes background features using support mask. We observe that this support mask presents an information bottleneck in several challenging FSS cases e.g., for small targets and/or inaccurate target boundaries. To this end, we present a novel method (MSI), which maximizes the support-set information by exploiting two complementary source of features in generating super correlation maps. We validate the effectiveness of our approach by instantiating it into three recent and strong FSS baselines. Experimental results on several publicly available FSS benchmarks show that our proposed method consistently improves the performance by visible margins and allows faster convergence. Our codes and models will be publicly released.

Semi-supervised anomaly detection is a common problem, as often the datasets containing anomalies are partially labeled. We propose a canonical framework: Semi-supervised Pseudo-labeler Anomaly Detection with Ensembling (SPADE) that isn't limited by the assumption that labeled and unlabeled data come from the same distribution. Indeed, the assumption is often violated in many applications - for example, the labeled data may contain only anomalies unlike unlabeled data, or unlabeled data may contain different types of anomalies, or labeled data may contain only 'easy-to-label' samples. SPADE utilizes an ensemble of one class classifiers as the pseudo-labeler to improve the robustness of pseudo-labeling with distribution mismatch. Partial matching is proposed to automatically select the critical hyper-parameters for pseudo-labeling without validation data, which is crucial with limited labeled data. SPADE shows state-of-the-art semi-supervised anomaly detection performance across a wide range of scenarios with distribution mismatch in both tabular and image domains. In some common real-world settings such as model facing new types of unlabeled anomalies, SPADE outperforms the state-of-the-art alternatives by 5% AUC in average.