图像缝线旨在缝合从不同的观点拍摄的图像到与更广泛的视野的图象。现有方法使用估计的扭曲函数将目标图像翘曲到参考图像，并且同情是最常用的翘曲功能之一。然而，当由于相机的非平面场景和平移运动导致图像具有大的视差时，同性特性不能完全描述两个图像之间的映射。基于全局或​​本地同类估计的现有方法不存在来自此问题的不含问题，并且由于视差而受到不期望的伪影。在本文中，而不是依赖于基于同位的扭曲，我们提出了一种新颖的深度图像拼接框架，利用像素 - 明智的横田来处理大视差问题。所提出的深度图像拼接框架由两个模块组成：像素 - 明智的翘曲模块（PWM）和缝合图像生成模块（SIGMO）。 PWM采用光学流量估计模型来获得整个图像的像素方面的翘曲，并通过所获得的跨场重新恢复目标图像的像素。 SIGMO将翘曲的目标图像和参考图像混合，同时消除了诸如损害缝合结果的合理性的未对准，接缝和孔的不需要的伪影。为了培训和评估所提出的框架，我们构建了一个大规模数据集，包括具有相应像素的图像对的图像对，该图像对进行映像对实际翘曲和样本缝合结果图像。我们表明，所提出的框架的结果与传统方法的结果优于常规方法，特别是当图像具有大视差时。代码和建议的数据集即将公开发布。

弱监督语义分段（WSSS）的现有研究已经利用了类激活映射（CAM）来本地化类对象。然而，由于分类损失不足以提供精确的物区域，因此凸轮倾向于偏向辨别模式（即，稀疏），并且不提供精确的对象边界信息（即，不确定）。为了解决这些限制，我们提出了一种新颖的框架（由MainNet和SupportNet组成），从给定的图像级监督导出像素级自我监督。在我们的框架中，借助拟议的区域对比模块（RCM）和多尺寸细分模块（MAM），MainNet由来自SupportNet的自我监督训练。 RCM从SupportNet中提取两种形式的自我监督：（1）从凸轮和（2）根据类区域掩码的特征获得的（2）类的类别区域掩模。然后，主目的的每个像素明智的特征被原型训练以对比的方式，锐化所产生的凸轮。 MAM利用从SupportNet的多个尺度推断的凸轮作为自我监控来指导MailNet。基于Mainnet和SupportNet的多尺度凸轮之间的不相似性，来自主目的的凸轮训练以扩展到较少辨别的区域。该方法在Pascal VOC 2012数据集上显示了在列车和验证集上的最先进的WSSS性能。为了再现性，代码将很快公开提供。

Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE). However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.

The tracking-by-detection paradigm today has become the dominant method for multi-object tracking and works by detecting objects in each frame and then performing data association across frames. However, its sequential frame-wise matching property fundamentally suffers from the intermediate interruptions in a video, such as object occlusions, fast camera movements, and abrupt light changes. Moreover, it typically overlooks temporal information beyond the two frames for matching. In this paper, we investigate an alternative by treating object association as clip-wise matching. Our new perspective views a single long video sequence as multiple short clips, and then the tracking is performed both within and between the clips. The benefits of this new approach are two folds. First, our method is robust to tracking error accumulation or propagation, as the video chunking allows bypassing the interrupted frames, and the short clip tracking avoids the conventional error-prone long-term track memory management. Second, the multiple frame information is aggregated during the clip-wise matching, resulting in a more accurate long-range track association than the current frame-wise matching. Given the state-of-the-art tracking-by-detection tracker, QDTrack, we showcase how the tracking performance improves with our new tracking formulation. We evaluate our proposals on two tracking benchmarks, TAO and MOT17 that have complementary characteristics and challenges each other.

Scaling object taxonomies is one of the important steps toward a robust real-world deployment of recognition systems. We have faced remarkable progress in images since the introduction of the LVIS benchmark. To continue this success in videos, a new video benchmark, TAO, was recently presented. Given the recent encouraging results from both detection and tracking communities, we are interested in marrying those two advances and building a strong large vocabulary video tracker. However, supervisions in LVIS and TAO are inherently sparse or even missing, posing two new challenges for training the large vocabulary trackers. First, no tracking supervisions are in LVIS, which leads to inconsistent learning of detection (with LVIS and TAO) and tracking (only with TAO). Second, the detection supervisions in TAO are partial, which results in catastrophic forgetting of absent LVIS categories during video fine-tuning. To resolve these challenges, we present a simple but effective learning framework that takes full advantage of all available training data to learn detection and tracking while not losing any LVIS categories to recognize. With this new learning scheme, we show that consistent improvements of various large vocabulary trackers are capable, setting strong baseline results on the challenging TAO benchmarks.

Test-time adaptation (TTA) has attracted significant attention due to its practical properties which enable the adaptation of a pre-trained model to a new domain with only target dataset during the inference stage. Prior works on TTA assume that the target dataset comes from the same distribution and thus constitutes a single homogeneous domain. In practice, however, the target domain can contain multiple homogeneous domains which are sufficiently distinctive from each other and those multiple domains might occur cyclically. Our preliminary investigation shows that domain-specific TTA outperforms vanilla TTA treating compound domain (CD) as a single one. However, domain labels are not available for CD, which makes domain-specific TTA not practicable. To this end, we propose an online clustering algorithm for finding pseudo-domain labels to obtain similar benefits as domain-specific configuration and accumulating knowledge of cyclic domains effectively. Moreover, we observe that there is a significant discrepancy in terms of prediction quality among samples, especially in the CD context. This further motivates us to boost its performance with gradient denoising by considering the image-wise similarity with the source distribution. Overall, the key contribution of our work lies in proposing a highly significant new task compound domain test-time adaptation (CD-TTA) on semantic segmentation as well as providing a strong baseline to facilitate future works to benchmark.

Universal Domain Adaptation aims to transfer the knowledge between the datasets by handling two shifts: domain-shift and category-shift. The main challenge is correctly distinguishing the unknown target samples while adapting the distribution of known class knowledge from source to target. Most existing methods approach this problem by first training the target adapted known classifier and then relying on the single threshold to distinguish unknown target samples. However, this simple threshold-based approach prevents the model from considering the underlying complexities existing between the known and unknown samples in the high-dimensional feature space. In this paper, we propose a new approach in which we use two sets of feature points, namely dual Classifiers for Prototypes and Reciprocals (CPR). Our key idea is to associate each prototype with corresponding known class features while pushing the reciprocals apart from these prototypes to locate them in the potential unknown feature space. The target samples are then classified as unknown if they fall near any reciprocals at test time. To successfully train our framework, we collect the partial, confident target samples that are classified as known or unknown through on our proposed multi-criteria selection. We then additionally apply the entropy loss regularization to them. For further adaptation, we also apply standard consistency regularization that matches the predictions of two different views of the input to make more compact target feature space. We evaluate our proposal, CPR, on three standard benchmarks and achieve comparable or new state-of-the-art results. We also provide extensive ablation experiments to verify our main design choices in our framework.

Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim to develop a personalized sleep induction system based on mental states using electroencephalogram and auditory stimulation. Our system analyzes users' mental states using an electroencephalogram and results of the Pittsburgh sleep quality index and Brunel mood scale. According to mental states, the system plays sleep induction sound among five auditory stimulation: white noise, repetitive beep sounds, rainy sound, binaural beat, and sham sound. Finally, the sleep-inducing system classified the sleep stage of participants with 94.7 percent and stopped auditory stimulation if participants showed non-rapid eye movement sleep. Our system makes 18 participants fall asleep among 20 participants.

We present a unified and compact representation for object rendering, 3D reconstruction, and grasp pose prediction that can be inferred from a single image within a few seconds. We achieve this by leveraging recent advances in the Neural Radiance Field (NeRF) literature that learn category-level priors and fine-tune on novel objects with minimal data and time. Our insight is that we can learn a compact shape representation and extract meaningful additional information from it, such as grasping poses. We believe this to be the first work to retrieve grasping poses directly from a NeRF-based representation using a single viewpoint (RGB-only), rather than going through a secondary network and/or representation. When compared to prior art, our method is two to three orders of magnitude smaller while achieving comparable performance at view reconstruction and grasping. Accompanying our method, we also propose a new dataset of rendered shoes for training a sim-2-real NeRF method with grasping poses for different widths of grippers.

来自磁共振成像（MRI）的体积图像在直肠癌的术前分期提供了宝贵的信息。最重要的是，T2和T3阶段之间的准确术前歧视可以说是直肠癌治疗的最具挑战性和临床意义的任务，因为通常建议对T3（或更大）阶段癌症患者进行化学疗法。在这项研究中，我们提出了一个体积卷积神经网络，可准确区分T2与直肠MR体积的T3阶段直肠癌。具体而言，我们提出1）基于自定义的基于重新连接的卷编码器，该编码器与晚期融合的固定间关系建模（即最后一层的3D卷积），2）双线性计算，该计算汇总了编码器所得的功能以创建一个创建一个的功能体积特征和3）三重损失和焦点损失的关节最小化。通过病理确认的T2/T3直肠癌的MR量，我们进行了广泛的实验，以比较残留学习框架内的各种设计。结果，我们的网络达到了0.831的AUC，高于专业放射科医生组的准确性。我们认为该方法可以扩展到其他卷分析任务