In this paper, we propose a novel 3D graph convolution based pipeline for category-level 6D pose and size estimation from monocular RGB-D images. The proposed method leverages an efficient 3D data augmentation and a novel vector-based decoupled rotation representation. Specifically, we first design an orientation-aware autoencoder with 3D graph convolution for latent feature learning. The learned latent feature is insensitive to point shift and size thanks to the shift and scale-invariance properties of the 3D graph convolution. Then, to efficiently decode the rotation information from the latent feature, we design a novel flexible vector-based decomposable rotation representation that employs two decoders to complementarily access the rotation information. The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task. Finally, we propose a 3D deformation mechanism to increase the generalization ability of the pipeline. Extensive experiments show that the proposed pipeline achieves state-of-the-art performance on category-level tasks. Further, the experiments demonstrate that the proposed rotation representation is more suitable for the pose estimation tasks than other rotation representations.

We propose GazeNeRF, a 3D-aware method for the task of gaze redirection. Existing gaze redirection methods operate on 2D images and struggle to generate 3D consistent results. Instead, we build on the intuition that the face region and eyeballs are separate 3D structures that move in a coordinated yet independent fashion. Our method leverages recent advancements in conditional image-based neural radiance fields and proposes a two-stream architecture that predicts volumetric features for the face and eye regions separately. Rigidly transforming the eye features via a 3D rotation matrix provides fine-grained control over the desired gaze angle. The final, redirected image is then attained via differentiable volume compositing. Our experiments show that this architecture outperforms naively conditioned NeRF baselines as well as previous state-of-the-art 2D gaze redirection methods in terms of redirection accuracy and identity preservation.

尽管最近在手动和对象数据集中进行了准确的3D注释做出了努力，但3D手和对象重建仍然存在差距。现有作品利用接触地图来完善不准确的手动姿势构成估计，并在给定的对象模型中生成grasps。但是，它们需要明确的3D监督，因此很少可用，因此仅限于受限的设置，例如，热摄像机观察到操纵物体上剩下的残留热量。在本文中，我们提出了一个新颖的半监督框架，使我们能够从单眼图像中学习接触。具体而言，我们利用大规模数据集中的视觉和几何一致性约束来在半监督学习中生成伪标记，并提出一个有效的基于图形的网络来推断联系。我们的半监督学习框架对接受“有限”注释的数据培训的现有监督学习方法取得了良好的改进。值得注意的是，与常用的基于点网的方法相比，我们所提出的模型能够以不到网络参数和内存访问成本的一半以下的一半获得卓越的结果。我们显示出使用触点图的好处，该触点图规则手动相互作用以产生更准确的重建。我们进一步证明，使用伪标签的培训可以将联系地图估计扩展到域外对象，并在多个数据集中更好地概括。

我们提出了一个新的变压器模型，用于无监督学习骨架运动序列的任务。用于基于无监督骨骼的动作学习的现有变压器模型被了解到每个关节从相邻帧的瞬时速度没有全球运动信息。因此，该模型在学习全身运动和暂时遥远的关节方面的关注方面存在困难。此外，模型中尚未考虑人与人之间的互动。为了解决全身运动，远程时间动态和人与人之间的互动的学习，我们设计了一种全球和本地的注意机制，在其中，全球身体动作和本地关节运动相互关注。此外，我们提出了一种新颖的预处理策略，即多间隔姿势位移预测，以在不同的时间范围内学习全球和本地关注。提出的模型成功地学习了关节的局部动力学，并从运动序列中捕获了全局上下文。我们的模型优于代表性基准中明显边缘的最先进模型。代码可在https://github.com/boeun-kim/gl-transformer上找到。

我们提出了一种新颖的优化框架，其基于用户描述和美学作证给定图像。与现有的图像裁剪方法不同，其中通常会列举深网络以回归裁剪参数或裁剪动作，我们建议通过重新修复在图像标题和美学任务上的预先训练的网络，而无需任何微调，我们建议直接优化裁剪参数。从而避免训练单独的网络。具体而言，我们搜索最大限度地减少这些网络初始目标的组合损失的最佳作物参数。为了使优化表提出三种策略：（i）多级双线性采样，（ii）退火的作物区域的规模，因此有效地减少了多种优化结果的参数空间，（iii）聚合。通过各种定量和定性评估，我们表明我们的框架可以产生与预期用户描述和美学令人愉悦的作物。

利用源区和目标域之间的张建空间是最近无监督的域适应方法之一。然而，标签的平衡崩溃问题，源标签在邻居实例的预测中占据了目标标签的主导地位，从未得到解决。在本文中，我们提出了一个实例 - 方面的最小策略，最小化了张开的空间中的高不确定性实例的熵，以解决它。我们通过最低限度问题的解决方案将大亨空间分为两个子空间：对比空间和共识空间。在对比的空间中，通过约束实例来减轻域间差异，以具有对比度视图和标签，并且共识空间减少了域内类别之间的混淆。我们的方法的有效性在公共基准上证明，包括办公室-31，办公室和visda-c，这实现了最先进的表演。我们进一步表明，我们的方法在PACS上表明了当前最先进的方法，这表示我们的实例 - 方面的方法适用于多源域适应。

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.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Recently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable performance to the continuous counterparts in representation learning, while being more interpretable in their predictions. In this paper, we develop a topic-informed discrete latent variable model for semantic textual similarity, which learns a shared latent space for sentence-pair representation via vector quantization. Compared with previous models limited to local semantic contexts, our model can explore richer semantic information via topic modeling. We further boost the performance of semantic similarity by injecting the quantized representation into a transformer-based language model with a well-designed semantic-driven attention mechanism. We demonstrate, through extensive experiments across various English language datasets, that our model is able to surpass several strong neural baselines in semantic textual similarity tasks.

将大型矩阵分配到小矩阵中是模型压缩的流行策略。奇异值分解（SVD）在这种压缩策略中起着至关重要的作用，近似具有较少参数的学习矩阵。但是，SVD最大程度地减少了平方误差以重建原始矩阵而不衡量参数的重要性，从而为那些影响任务准确性的人提供了更大的重建误差。换句话说，SVD的优化目标与受过训练的模型的任务准确性不符。我们通过引入Fisher信息来权衡影响模型预测的参数的重要性来分析此先前未开发的问题，进行观察并解决该问题。这个想法导致了我们的方法：Fisher加权SVD（FWSVD）。尽管我们方法的分解矩阵并没有导致较小的重建错误，但我们发现我们所得的任务准确性更接近原始模型的性能。我们使用基于变压器的语言模型进行分析，显示我们的加权SVD很大程度上减轻了不匹配的优化目标，并可以以更高的压缩率维持模型性能。我们的方法可以直接压缩特定于任务的模型，同时比需要昂贵的模型预训练的其他紧凑型模型策略更好。此外，对压缩模型的评估表明，我们的方法可以进一步降低9％至30％的参数，对任务准确性产生不大的影响。