Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not. This paradigm, although effective, is inefficient because the detectors have to go through all patches, severely hindering the inference speed. This paper presents an Objectness Activation Network (OAN) to help detectors focus on fewer patches but achieve more efficient inference and more accurate results, enabling a simple and effective solution to object detection in large images. In brief, OAN is a light fully-convolutional network for judging whether each patch contains objects or not, which can be easily integrated into many object detectors and jointly trained with them end-to-end. We extensively evaluate our OAN with five advanced detectors. Using OAN, all five detectors acquire more than 30.0% speed-up on three large-scale aerial image datasets, meanwhile with consistent accuracy improvements. On extremely large Gaofen-2 images (29200$\times$27620 pixels), our OAN improves the detection speed by 70.5%. Moreover, we extend our OAN to driving-scene object detection and 4K video object detection, boosting the detection speed by 112.1% and 75.0%, respectively, without sacrificing the accuracy. Code is available at https://github.com/Ranchosky/OAN.

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.

Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations.

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.

Interoperability issue is a significant problem in Building Information Modeling (BIM). Object type, as a kind of critical semantic information needed in multiple BIM applications like scan-to-BIM and code compliance checking, also suffers when exchanging BIM data or creating models using software of other domains. It can be supplemented using deep learning. Current deep learning methods mainly learn from the shape information of BIM objects for classification, leaving relational information inherent in the BIM context unused. To address this issue, we introduce a two-branch geometric-relational deep learning framework. It boosts previous geometric classification methods with relational information. We also present a BIM object dataset IFCNet++, which contains both geometric and relational information about the objects. Experiments show that our framework can be flexibly adapted to different geometric methods. And relational features do act as a bonus to general geometric learning methods, obviously improving their classification performance, thus reducing the manual labor of checking models and improving the practical value of enriched BIM models.

Diffusion models, which learn to reverse a signal destruction process to generate new data, typically require the signal at each step to have the same dimension. We argue that, considering the spatial redundancy in image signals, there is no need to maintain a high dimensionality in the evolution process, especially in the early generation phase. To this end, we make a theoretical generalization of the forward diffusion process via signal decomposition. Concretely, we manage to decompose an image into multiple orthogonal components and control the attenuation of each component when perturbing the image. That way, along with the noise strength increasing, we are able to diminish those inconsequential components and thus use a lower-dimensional signal to represent the source, barely losing information. Such a reformulation allows to vary dimensions in both training and inference of diffusion models. Extensive experiments on a range of datasets suggest that our approach substantially reduces the computational cost and achieves on-par or even better synthesis performance compared to baseline methods. We also show that our strategy facilitates high-resolution image synthesis and improves FID of diffusion model trained on FFHQ at $1024\times1024$ resolution from 52.40 to 10.46. Code and models will be made publicly available.

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.

大规模发光点云的快速有效语义分割是自主驾驶中的一个基本问题。为了实现这一目标，现有的基于点的方法主要选择采用随机抽样策略来处理大规模点云。但是，我们的数量和定性研究发现，随机抽样可能不适合自主驾驶场景，因为LiDAR点遵循整个空间的不均匀甚至长尾巴分布，这阻止了模型从从中捕获足够的信息，从而从中捕获了足够的信息不同的距离范围并降低了模型的学习能力。为了减轻这个问题，我们提出了一种新的极性缸平衡的随机抽样方法，该方法使下采样的点云能够保持更平衡的分布并改善不同空间分布下的分割性能。此外，引入了采样一致性损失，以进一步提高分割性能并降低模型在不同采样方法下的方差。广泛的实验证实，我们的方法在Semantickitti和Semanticposs基准测试中都产生了出色的性能，分别提高了2.8％和4.0％。

尽管变形金刚及其变体构象体在语音识别方面表现出了有希望的表现，但参数化的属性在训练和推理过程中导致了很大的记忆成本。一些作品使用跨层重量分享来减少模型的参数。但是，不可避免的能力损失会损害模型性能。为了解决这个问题，本文提出了通过共享稀疏门控专家的参数效率构象异构体。具体而言，我们使用稀疏门控的专家（MOE）来扩展构型块的容量而不增加计算。然后，共享分组构象块的参数，以减少参数的数量。接下来，为了确保具有不同级别适应表示的灵活性的共享块，我们会单独设计MOE路由器和标准化。此外，我们使用知识蒸馏来进一步提高性能。实验结果表明，与全参数模型相比，所提出的模型用编码器的1/3来实现竞争性能。

我们通过重新访问最近的质心，这是最经典，最简单的分类器之一，这是一个概念上优雅而有效的网络，这是一个概念上优雅而有效的网络，这是一个概念上优雅而有效的网络，这是一个概念上优雅而有效的网络，这是一个概念上优雅而有效的网络，这是一个概念上优雅但令人惊讶的有效网络，这是一个概念上优雅而有效的网络，这是最经典，最简单的分类器之一。当前的深层模型以完全参数的方式学习分类器，忽略了潜在的数据结构，缺乏简单性和解释性。 DNC相反进行非参数，基于案例的推理；它利用训练样本的亚中心来描述类别分布，并清楚地将分类解释为特征空间中测试数据和类亚电视的近距离。由于基于距离的性质，网络输出维度是灵活的，所有可学习的参数仅用于数据嵌入。这意味着在“预训练和微调”范式下，可以将所有用于像素识别学习的知识完全转移到像素识别学习中。除了其嵌套的简单性和直观的决策机制外，DNC甚至可以选择次级抗毒剂作为人类可以查看和检查的实际训练图像时具有临时解释性。与参数对应物相比，DNC在图像分类（CIFAR-10，IMAGENET）和靴子像素识别（ADE20K，CityScapes）方面的性能更好，具有提高的透明度和更少的可学习参数，使用各种网络体系结构（Resnet，SWIN，SWIN）和分割模型（Resnet，Swin）和分裂模型（ FCN，DeepLabv3，Swin）。我们认为这项工作带来了对相关领域的基本见解。