Multi-view projection techniques have shown themselves to be highly effective in achieving top-performing results in the recognition of 3D shapes. These methods involve learning how to combine information from multiple view-points. However, the camera view-points from which these views are obtained are often fixed for all shapes. To overcome the static nature of current multi-view techniques, we propose learning these view-points. Specifically, we introduce the Multi-View Transformation Network (MVTN), which uses differentiable rendering to determine optimal view-points for 3D shape recognition. As a result, MVTN can be trained end-to-end with any multi-view network for 3D shape classification. We integrate MVTN into a novel adaptive multi-view pipeline that is capable of rendering both 3D meshes and point clouds. Our approach demonstrates state-of-the-art performance in 3D classification and shape retrieval on several benchmarks (ModelNet40, ScanObjectNN, ShapeNet Core55). Further analysis indicates that our approach exhibits improved robustness to occlusion compared to other methods. We also investigate additional aspects of MVTN, such as 2D pretraining and its use for segmentation. To support further research in this area, we have released MVTorch, a PyTorch library for 3D understanding and generation using multi-view projections.

With the recent advances in video and 3D understanding, novel 4D spatio-temporal challenges fusing both concepts have emerged. Towards this direction, the Ego4D Episodic Memory Benchmark proposed a task for Visual Queries with 3D Localization (VQ3D). Given an egocentric video clip and an image crop depicting a query object, the goal is to localize the 3D position of the center of that query object with respect to the camera pose of a query frame. Current methods tackle the problem of VQ3D by lifting the 2D localization results of the sister task Visual Queries with 2D Localization (VQ2D) into a 3D reconstruction. Yet, we point out that the low number of Queries with Poses (QwP) from previous VQ3D methods severally hinders their overall success rate and highlights the need for further effort in 3D modeling to tackle the VQ3D task. In this work, we formalize a pipeline that better entangles 3D multiview geometry with 2D object retrieval from egocentric videos. We estimate more robust camera poses, leading to more successful object queries and substantially improved VQ3D performance. In practice, our method reaches a top-1 overall success rate of 86.36% on the Ego4D Episodic Memory Benchmark VQ3D, a 10x improvement over the previous state-of-the-art. In addition, we provide a complete empirical study highlighting the remaining challenges in VQ3D.

近期和越来越越来越多的视频 - 语言研究的兴趣已经推动了大规模数据集的开发，可实现数据密集型机器学习技术。相比之下，在评估这些数据集的适应性时，已经进行了有限的努力进行视频 - 语言接地任务。最近的作品已经开始发现这些数据集中的重大限制，这表明最先进的技术通常会过度地覆盖到隐藏的数据集偏差。在这项工作中，我们呈现MAD（电影音频描述），这是一种新颖的基准，从扩充现有视频数据集的范式，其中包含文本注释，并专注于爬行和对齐主流电影的可用音频描述。 MAD包含超过384,000个自然语言句子，该句子接地为超过1,200小时的视频，并且在视频 - 语言接地数据集中展示目前诊断的偏差显着减少。疯狂的收集策略使新颖且更具挑战性的视频 - 语言接地版本，其中短时间时刻（通常秒长）必须在多样化的长型视频中准确地接地，可以持续长达三个小时。

多视图投影方法在3D理解任务等方面表现出有希望的性能，如3D分类和分割。然而，它仍然不明确如何将这种多视图方法与广泛可用的3D点云组合。以前的方法使用未受忘掉的启发式方法在点级别结合功能。为此，我们介绍了多视图点云（vinoint云）的概念，表示每个3D点作为从多个视图点提取的一组功能。这种新颖的3D Vintor云表示将3D点云表示的紧凑性与多视图表示的自然观。当然，我们可以用卷积和汇集操作配备这一新的表示。我们以理论上建立的功能形式部署了Voint神经网络（vointnet），以学习vinite空间中的表示。我们的小说代表在ScanObjectnn，ModelNet40和ShapEnet​​ Core55上实现了3D分类和检索的最先进的性能。此外，我们在ShapeNet零件上实现了3D语义细分的竞争性能。进一步的分析表明，与其他方法相比，求力提高了旋转和闭塞的鲁棒性。

Despite the numerous developments in object tracking, further development of current tracking algorithms is limited by small and mostly saturated datasets. As a matter of fact, data-hungry trackers based on deep-learning currently rely on object detection datasets due to the scarcity of dedicated large-scale tracking datasets. In this work, we present TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild. We provide more than 30K videos with more than 14 million dense bounding box annotations. Our dataset covers a wide selection of object classes in broad and diverse context. By releasing such a large-scale dataset, we expect deep trackers to further improve and generalize. In addition, we introduce a new benchmark composed of 500 novel videos, modeled with a distribution similar to our training dataset. By sequestering the annotation of the test set and providing an online evaluation server, we provide a fair benchmark for future development of object trackers. Deep trackers fine-tuned on a fraction of our dataset improve their performance by up to 1.6% on OTB100 and up to 1.7% on TrackingNet Test. We provide an extensive benchmark on TrackingNet by evaluating more than 20 trackers. Our results suggest that object tracking in the wild is far from being solved.

The understanding capabilities of current state-of-the-art 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. In its 2D counterpart, recent advances have shown that similar problems can be significantly alleviated by employing knowledge from other modalities, such as language. Inspired by this, leveraging multimodal information for 3D modality could be promising to improve 3D understanding under the restricted data regime, but this line of research is not well studied. Therefore, we introduce ULIP to learn a unified representation of image, text, and 3D point cloud by pre-training with object triplets from the three modalities. To overcome the shortage of training triplets, ULIP leverages a pre-trained vision-language model that has already learned a common visual and textual space by training with massive image-text pairs. Then, ULIP learns a 3D representation space aligned with the common image-text space, using a small number of automatically synthesized triplets. ULIP is agnostic to 3D backbone networks and can easily be integrated into any 3D architecture. Experiments show that ULIP effectively improves the performance of multiple recent 3D backbones by simply pre-training them on ShapeNet55 using our framework, achieving state-of-the-art performance in both standard 3D classification and zero-shot 3D classification on ModelNet40 and ScanObjectNN. ULIP also improves the performance of PointMLP by around 3% in 3D classification on ScanObjectNN, and outperforms PointCLIP by 28.8% on top-1 accuracy for zero-shot 3D classification on ModelNet40. Our code and pre-trained models will be released.

A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal pathways. To date, tractograms are the most accurate white matter representation and thus are used for tasks like presurgical planning and investigations of neuroplasticity, brain disorders, or brain networks. However, it is a well-known issue that a large portion of tractogram fibers is not anatomically plausible and can be considered artifacts of the tracking procedure. With Verifyber, we tackle the problem of filtering out such non-plausible fibers using a novel fully-supervised learning approach. Differently from other approaches based on signal reconstruction and/or brain topology regularization, we guide our method with the existing anatomical knowledge of the white matter. Using tractograms annotated according to anatomical principles, we train our model, Verifyber, to classify fibers as either anatomically plausible or non-plausible. The proposed Verifyber model is an original Geometric Deep Learning method that can deal with variable size fibers, while being invariant to fiber orientation. Our model considers each fiber as a graph of points, and by learning features of the edges between consecutive points via the proposed sequence Edge Convolution, it can capture the underlying anatomical properties. The output filtering results highly accurate and robust across an extensive set of experiments, and fast; with a 12GB GPU, filtering a tractogram of 1M fibers requires less than a minute. Verifyber implementation and trained models are available at https://github.com/FBK-NILab/verifyber.

We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research.

生物学和人造药物需要处理现实世界中的不断变化。我们在四个经典的连续控制环境中研究了这个问题，并通过形态扰动增强。当不同身体部位的长度和厚度变化时，学习势头是挑战性的，因为需要控制政策才能适应形态以成功平衡和推进代理。我们表明，基于本体感受状态的控制策略的表现差，可以通过高度可变的身体配置，而（甲骨文）代理可以访问学习扰动的编码的（甲骨文）的性能要好得多。我们介绍了DMAP，这是一种以生物学启发的，基于注意力的策略网络体系结构。 DMAP将独立的本体感受处理，分布式策略与每个关节的单个控制器以及注意力机制结合在一起，从不同身体部位到不同控制器的动态门感觉信息。尽管无法访问（隐藏的）形态信息，但在所有考虑的环境中，DMAP都可以端对端训练，整体匹配或超越了Oracle代理的性能。因此，DMAP是从生物运动控制中实施原理的，为学习挑战的感觉运动任务提供了强烈的诱导偏见。总体而言，我们的工作证实了这些原则在挑战运动任务中的力量。

事实证明，图形神经网络（GNN）在图形结构数据的几个预测建模任务中已被证明。在这些任务中，链接预测是许多现实世界应用（例如推荐系统）的基本问题之一。但是，GNN不能免疫对抗攻击，即精心制作的恶意例子，旨在欺骗预测模型。在这项工作中，我们专注于对基于GNN的链接预测模型进行特定的白盒攻击，其中恶意节点的目的是出现在给定目标受害者的推荐节点列表中。为了实现这一目标，攻击者节点还可以指望它直接控制的其他现有同伴的合作，即在网络中注入许多``vicious''节点的能力。具体而言，所有这些恶意节点都可以添加新的边缘或删除现有的节点，从而扰乱原始图。因此，我们提出了野蛮人，一种新颖的框架和一种安装这种链接预测攻击的方法。野蛮人将对手的目标制定为一项优化任务，从而达到了攻击的有效性与所需的恶意资源的稀疏之间的平衡。在现实世界和合成数据集上进行的广泛实验表明，通过野蛮人实施的对抗性攻击确实达到了很高的攻击成功率，但使用少量恶性节点。最后，尽管这些攻击需要完全了解目标模型，但我们表明它们可以成功地转移到其他黑框方法以进行链接预测。