In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.
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人搜索是一项具有挑战性的任务,旨在实现共同的行人检测和人重新识别(REID)。以前的作品在完全和弱监督的设置下取得了重大进步。但是,现有方法忽略了人搜索模型的概括能力。在本文中,我们采取了进一步的步骤和现在的域自适应人员搜索(DAPS),该搜索旨在将模型从标记的源域概括为未标记的目标域。在这种新环境下出现了两个主要挑战:一个是如何同时解决检测和重新ID任务的域未对准问题,另一个是如何在目标域上训练REID子任务而不可靠的检测结果。为了应对这些挑战,我们提出了一个强大的基线框架,并使用两个专用设计。 1)我们设计一个域对齐模块,包括图像级和任务敏感的实例级别对齐,以最大程度地减少域差异。 2)我们通过动态聚类策略充分利用未标记的数据,并使用伪边界框来支持目标域上的REID和检测训练。通过上述设计,我们的框架在MAP中获得了34.7%的地图,而PRW数据集的TOP-1则达到80.6%,超过了直接转移基线的大幅度。令人惊讶的是,我们无监督的DAPS模型的性能甚至超过了一些完全和弱监督的方法。该代码可在https://github.com/caposerenity/daps上找到。
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在本文中,我们提出了PETRV2,这是来自多视图图像的3D感知统一框架。基于PETR,PETRV2探讨了时间建模的有效性,该时间建模利用先前帧的时间信息来增强3D对象检测。更具体地说,我们扩展了PETR中的3D位置嵌入(3D PE)进行时间建模。 3D PE可以在不同帧的对象位置上实现时间对齐。进一步引入了特征引导的位置编码器,以提高3D PE的数据适应性。为了支持高质量的BEV分割,PETRV2通过添加一组分割查询提供了简单而有效的解决方案。每个分割查询负责分割BEV映射的一个特定补丁。 PETRV2在3D对象检测和BEV细分方面实现了最先进的性能。在PETR框架上还进行了详细的鲁棒性分析。我们希望PETRV2可以作为3D感知的强大基准。代码可在\ url {https://github.com/megvii-research/petr}中获得。
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域概括人员重新识别旨在将培训的模型应用于未经看明域。先前作品将所有培训域中的数据组合以捕获域不变的功能,或者采用专家的混合来调查特定域的信息。在这项工作中,我们争辩说,域特定和域不变的功能对于提高重新ID模型的泛化能力至关重要。为此,我们设计了一种新颖的框架,我们命名为两流自适应学习(tal),同时模拟这两种信息。具体地,提出了一种特定于域的流以捕获具有批量归一化(BN)参数的训练域统计,而自适应匹配层被设计为动态聚合域级信息。同时,我们在域不变流中设计一个自适应BN层,以近似各种看不见域的统计信息。这两个流自适应地和协作地工作,以学习更广泛的重新ID功能。我们的框架可以应用于单源和多源域泛化任务,实验结果表明我们的框架显着优于最先进的方法。
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基础模型不是模型生产管道的最后一章。以少数数据以少数数据传输到数千个下游任务正在成为基础模型的应用的趋势。在本文中,我们提出了一个通用转移框架:一个传输所有(OTA),将任何视觉基础模型(VFM)转移到具有少数下游数据的下游任务。我们首先通过图像重新表示微调(IRF)将VFM传输到特定于任务特定模型,然后将知识从特定于任务的模型蒸馏到部署的模型,其中包含由下游图像引导的生成(DIGG)产生的数据。OTA在传输时没有对上游数据,VFM和下游任务的依赖性。它还为VFM研究人员提供了一种方法,以释放其上游信息,以便更好地转移,但由于隐私要求而没有泄漏数据。大规模实验在少数数据设置中验证我们方法的有效性和优越性。我们的代码将被释放。
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过去几年的技术创新的巨大浪潮,标志着AI技术的进展,是深刻的重塑行业和社会。然而,在路上,一个关键的挑战等待着我们,即我们满足快速增长的情景的能力的能力受到收购培训数据的成本的严重限制。由于主流学习范式的局限性,这一困难的局面是基于主流学习范式的局限性:我们需要根据大量注释的数据以及通常从头来训练每个新场景的新模型。在解决这一基本问题时,我们超越并开发一个名为实习生的新学习范式。通过在多个阶段的来自多个来源的监控信号学习,培训的模型将产生强大的相互性。我们在26个众所周知的数据集中评估我们的模型,该数据集涵盖计算机视觉中的四类任务。在大多数情况下,我们的模型仅适用于目标域中的培训数据的10%,始终以完整的数据培训的对应物,通常由显着的边距。这是一个重要前景的重要一步,其中具有一般视觉能力的这种模型可以大大降低对数据的依赖,从而加速通过AI技术的采用。此外,围绕我们的新范式旋转,我们还介绍了一个新的数据系统,新的架构和新的基准,以及一起形成一般愿景生态系统,以开放和包容性的方式支持其未来的发展。
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模型量化已成为加速深度学习推理的不可或缺的技术。虽然研究人员继续推动量化算法的前沿,但是现有量化工作通常是不可否认的和不可推销的。这是因为研究人员不选择一致的训练管道并忽略硬件部署的要求。在这项工作中,我们提出了模型量化基准(MQBench),首次尝试评估,分析和基准模型量化算法的再现性和部署性。我们为实际部署选择多个不同的平台,包括CPU,GPU,ASIC,DSP,并在统一培训管道下评估广泛的最新量化算法。 MQBENCK就像一个连接算法和硬件的桥梁。我们进行全面的分析,并找到相当大的直观或反向直观的见解。通过对齐训练设置,我们发现现有的算法在传统的学术轨道上具有大致相同的性能。虽然用于硬件可部署量化,但有一个巨大的精度差距,仍然不稳定。令人惊讶的是,没有现有的算法在MQBench中赢得每一项挑战,我们希望这项工作能够激发未来的研究方向。
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Object recognition techniques using convolutional neural networks (CNN) have achieved great success. However, state-of-the-art object detection methods still perform poorly on large vocabulary and long-tailed datasets, e.g. LVIS.In this work, we analyze this problem from a novel perspective: each positive sample of one category can be seen as a negative sample for other categories, making the tail categories receive more discouraging gradients. Based on it, we propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories. The equalization loss protects the learning of rare categories from being at a disadvantage during the network parameter updating. Thus the model is capable of learning better discriminative features for objects of rare classes. Without any bells and whistles, our method achieves AP gains of 4.1% and 4.8% for the rare and common categories on the challenging LVIS benchmark, compared to the Mask R-CNN baseline. With the utilization of the effective equalization loss, we finally won the 1st place in the LVIS Challenge 2019. Code has been made available at: https: //github.com/tztztztztz/eql.detectron2
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Spatiotemporal and motion features are two complementary and crucial information for video action recognition. Recent state-of-the-art methods adopt a 3D CNN stream to learn spatiotemporal features and another flow stream to learn motion features. In this work, we aim to efficiently encode these two features in a unified 2D framework. To this end, we first propose an STM block, which contains a Channel-wise SpatioTemporal Module (CSTM) to present the spatiotemporal features and a Channel-wise Motion Module (CMM) to efficiently encode motion features. We then replace original residual blocks in the ResNet architecture with STM blcoks to form a simple yet effective STM network by introducing very limited extra computation cost. Extensive experiments demonstrate that the proposed STM network outperforms the state-of-the-art methods on both temporal-related datasets (i.e., Something-Something v1 & v2 and Jester) and scene-related datasets (i.e., Kinetics-400, UCF-101, and HMDB-51) with the help of encoding spatiotemporal and motion features together. * The work was done during an internship at SenseTime.
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Siamese network based trackers formulate tracking as convolutional feature cross-correlation between a target template and a search region. However, Siamese trackers still have an accuracy gap compared with state-of-theart algorithms and they cannot take advantage of features from deep networks, such as ResNet-50 or deeper. In this work we prove the core reason comes from the lack of strict translation invariance. By comprehensive theoretical analysis and experimental validations, we break this restriction through a simple yet effective spatial aware sampling strategy and successfully train a ResNet-driven Siamese tracker with significant performance gain. Moreover, we propose a new model architecture to perform layer-wise and depthwise aggregations, which not only further improves the accuracy but also reduces the model size. We conduct extensive ablation studies to demonstrate the effectiveness of the proposed tracker, which obtains currently the best results on five large tracking benchmarks, including OTB2015, VOT2018, UAV123, LaSOT, and TrackingNet. Our model will be released to facilitate further researches.
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