Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.

Human modeling and relighting are two fundamental problems in computer vision and graphics, where high-quality datasets can largely facilitate related research. However, most existing human datasets only provide multi-view human images captured under the same illumination. Although valuable for modeling tasks, they are not readily used in relighting problems. To promote research in both fields, in this paper, we present UltraStage, a new 3D human dataset that contains more than 2K high-quality human assets captured under both multi-view and multi-illumination settings. Specifically, for each example, we provide 32 surrounding views illuminated with one white light and two gradient illuminations. In addition to regular multi-view images, gradient illuminations help recover detailed surface normal and spatially-varying material maps, enabling various relighting applications. Inspired by recent advances in neural representation, we further interpret each example into a neural human asset which allows novel view synthesis under arbitrary lighting conditions. We show our neural human assets can achieve extremely high capture performance and are capable of representing fine details such as facial wrinkles and cloth folds. We also validate UltraStage in single image relighting tasks, training neural networks with virtual relighted data from neural assets and demonstrating realistic rendering improvements over prior arts. UltraStage will be publicly available to the community to stimulate significant future developments in various human modeling and rendering tasks.

Recent years have witnessed the tremendous progress of 3D GANs for generating view-consistent radiance fields with photo-realism. Yet, high-quality generation of human radiance fields remains challenging, partially due to the limited human-related priors adopted in existing methods. We present HumanGen, a novel 3D human generation scheme with detailed geometry and $\text{360}^{\circ}$ realistic free-view rendering. It explicitly marries the 3D human generation with various priors from the 2D generator and 3D reconstructor of humans through the design of "anchor image". We introduce a hybrid feature representation using the anchor image to bridge the latent space of HumanGen with the existing 2D generator. We then adopt a pronged design to disentangle the generation of geometry and appearance. With the aid of the anchor image, we adapt a 3D reconstructor for fine-grained details synthesis and propose a two-stage blending scheme to boost appearance generation. Extensive experiments demonstrate our effectiveness for state-of-the-art 3D human generation regarding geometry details, texture quality, and free-view performance. Notably, HumanGen can also incorporate various off-the-shelf 2D latent editing methods, seamlessly lifting them into 3D.

在这项工作中，我们提出了叙述，这是一种新颖的管道，可以以逼真的方式同时编辑肖像照明和观点。作为一种混合神经形态的面部模型，叙述了几何学感知生成方法和正常辅助物理面部模型的互补益处。简而言之，叙述首先将输入肖像转变为粗糙的几何形状，并采用神经渲染来产生类似于输入的图像，并产生令人信服的姿势变化。但是，反演步骤引入了不匹配，带来了较少面部细节的低质量图像。因此，我们进一步估计了师范的肖像，以增强粗糙的几何形状，从而创建高保真的物理面部模型。特别是，我们融合了神经和身体渲染，以补偿不完善的反转，从而产生了现实和视图一致的新颖透视图像。在重新阶段，以前的作品着重于单一视图肖像重新审议，但也忽略了不同观点之间的一致性，引导不稳定和不一致的照明效果以进行视图变化。我们通过将其多视图输入正常地图与物理面部模型统一，以解决此问题。叙事通过一致的正常地图进行重新进行重新，施加了跨视图的约束并表现出稳定且连贯的照明效果。我们在实验上证明，叙述在先前的工作中取得了更现实的，可靠的结果。我们进一步使用动画和样式转移工具进行介绍，从而分别或组合姿势变化，灯光变化，面部动画和样式转移，所有这些都以摄影质量为单位。我们展示了生动的自由视图面部动画以及3D感知可靠的风格化，可帮助促进各种AR/VR应用程序，例如虚拟摄影，3D视频会议和后期制作。

众所周知，深度学习模型容易受到对抗性例子的影响。现有对对抗训练的研究已在这一挑战中取得了长足的进步。作为一个典型的特征，他们经常认为班级分布总体平衡。但是，在广泛的应用中，长尾数据集无处不在，其中头等级实例的数量大于尾巴类。在这种情况下，AUC比准确度更合理，因为它对课堂分布不敏感。在此激励的情况下，我们提出了一项早期试验，以探索对抗性训练方法以优化AUC。主要的挑战在于，积极和负面的例子与目标函数紧密结合。作为直接结果，如果没有数据集进行全面扫描，就无法生成对抗示例。为了解决此问题，基于凹入的正则化方案，我们将AUC优化问题重新制定为鞍点问题，该问题将成为实例函数。这导致端到端培训方案。此外，我们提供了提出的算法的收敛保证。我们的分析与现有研究不同，因为该算法被要求通过计算Min-Max问题的梯度来产生对抗性示例。最后，广泛的实验结果表明，在三个长尾数据集中，我们的算法的性能和鲁棒性。

Advanced visual localization techniques encompass image retrieval challenges and 6 Degree-of-Freedom (DoF) camera pose estimation, such as hierarchical localization. Thus, they must extract global and local features from input images. Previous methods have achieved this through resource-intensive or accuracy-reducing means, such as combinatorial pipelines or multi-task distillation. In this study, we present a novel method called SuperGF, which effectively unifies local and global features for visual localization, leading to a higher trade-off between localization accuracy and computational efficiency. Specifically, SuperGF is a transformer-based aggregation model that operates directly on image-matching-specific local features and generates global features for retrieval. We conduct experimental evaluations of our method in terms of both accuracy and efficiency, demonstrating its advantages over other methods. We also provide implementations of SuperGF using various types of local features, including dense and sparse learning-based or hand-crafted descriptors.

The advance of computer-aided detection systems using deep learning opened a new scope in endoscopic image analysis. However, the learning-based models developed on closed datasets are susceptible to unknown anomalies in complex clinical environments. In particular, the high false positive rate of polyp detection remains a major challenge in clinical practice. In this work, we release the FPPD-13 dataset, which provides a taxonomy and real-world cases of typical false positives during computer-aided polyp detection in real-world colonoscopy. We further propose a post-hoc module EndoBoost, which can be plugged into generic polyp detection models to filter out false positive predictions. This is realized by generative learning of the polyp manifold with normalizing flows and rejecting false positives through density estimation. Compared to supervised classification, this anomaly detection paradigm achieves better data efficiency and robustness in open-world settings. Extensive experiments demonstrate a promising false positive suppression in both retrospective and prospective validation. In addition, the released dataset can be used to perform 'stress' tests on established detection systems and encourages further research toward robust and reliable computer-aided endoscopic image analysis. The dataset and code will be publicly available at http://endoboost.miccai.cloud.

Monocular depth estimation has been actively studied in fields such as robot vision, autonomous driving, and 3D scene understanding. Given a sequence of color images, unsupervised learning methods based on the framework of Structure-From-Motion (SfM) simultaneously predict depth and camera relative pose. However, dynamically moving objects in the scene violate the static world assumption, resulting in inaccurate depths of dynamic objects. In this work, we propose a new method to address such dynamic object movements through monocular 3D object detection. Specifically, we first detect 3D objects in the images and build the per-pixel correspondence of the dynamic pixels with the detected object pose while leaving the static pixels corresponding to the rigid background to be modeled with camera motion. In this way, the depth of every pixel can be learned via a meaningful geometry model. Besides, objects are detected as cuboids with absolute scale, which is used to eliminate the scale ambiguity problem inherent in monocular vision. Experiments on the KITTI depth dataset show that our method achieves State-of-The-Art performance for depth estimation. Furthermore, joint training of depth, camera motion and object pose also improves monocular 3D object detection performance. To the best of our knowledge, this is the first work that allows a monocular 3D object detection network to be fine-tuned in a self-supervised manner.

随着几个行业正在朝着建模大规模的3D虚拟世界迈进，因此需要根据3D内容的数量，质量和多样性来扩展的内容创建工具的需求变得显而易见。在我们的工作中，我们旨在训练Parterant 3D生成模型，以合成纹理网格，可以通过3D渲染引擎直接消耗，因此立即在下游应用中使用。 3D生成建模的先前工作要么缺少几何细节，因此在它们可以生成的网格拓扑中受到限制，通常不支持纹理，或者在合成过程中使用神经渲染器，这使得它们在常见的3D软件中使用。在这项工作中，我们介绍了GET3D，这是一种生成模型，该模型直接生成具有复杂拓扑，丰富几何细节和高保真纹理的显式纹理3D网格。我们在可区分的表面建模，可区分渲染以及2D生成对抗网络中桥接了最新成功，以从2D图像集合中训练我们的模型。 GET3D能够生成高质量的3D纹理网格，从汽车，椅子，动物，摩托车和人类角色到建筑物，对以前的方法进行了重大改进。

我们考虑了户外照明估算的挑战性问题，即影像逼真的虚拟对象将其插入照片中的目标。现有在室外照明估计的作品通常将场景照明简化为环境图，该图无法捕获室外场景中的空间变化的照明效果。在这项工作中，我们提出了一种神经方法，该方法可以从单个图像中估算5D HDR光场，以及一个可区分的对象插入公式，该公式可以通过基于图像的损失来端对端训练，从而鼓励现实主义。具体而言，我们设计了针对室外场景量身定制的混合照明表示，其中包含一个HDR Sky Dome，可处理太阳的极端强度，并具有体积的照明表示，该代表模拟了周围场景的空间变化外观。通过估计的照明，我们的阴影感知对象插入是完全可区分的，这使得对复合图像的对抗训练可以为照明预测提供其他监督信号。我们在实验上证明，混合照明表示比现有的室外照明估计方法更具性能。我们进一步显示了AR对象插入在自主驾驶应用程序中的好处，在对我们的增强数据进行培训时，我们可以在其中获得3D对象检测器的性能提高。