在本文中，我们介绍了人际内和人际关系网络（I^2R-NET），以进行多人姿势估计。它涉及两个基本模块。首先，人类内部关系模块在一个人身上运行，旨在捕获人类内部依赖性。其次，人际关系模块考虑了多个实例之间的关系，并着重于捕获人间的相互作用。人际关系间的关系模块可以通过减少特征图的分辨率来设计非常轻巧，但学习有用的关系信息以显着提高人类内部关系模块的性能。即使没有铃铛和哨子，我们的方法也可以竞争或胜过当前的比赛获胜者。我们对可可，人群和ochuman数据集进行了广泛的实验。结果表明，所提出的模型超过了所有最新方法。具体而言，所提出的方法在众群数据集上达到了77.4％的AP和Ochuman数据集上的67.8％AP，从而超过了现有方法的大幅度优于较大的利润率。此外，消融研究和可视化分析还证明了我们的模型的有效性。

The click-through rate (CTR) prediction task is to predict whether a user will click on the recommended item. As mind-boggling amounts of data are produced online daily, accelerating CTR prediction model training is critical to ensuring an up-to-date model and reducing the training cost. One approach to increase the training speed is to apply large batch training. However, as shown in computer vision and natural language processing tasks, training with a large batch easily suffers from the loss of accuracy. Our experiments show that previous scaling rules fail in the training of CTR prediction neural networks. To tackle this problem, we first theoretically show that different frequencies of ids make it challenging to scale hyperparameters when scaling the batch size. To stabilize the training process in a large batch size setting, we develop the adaptive Column-wise Clipping (CowClip). It enables an easy and effective scaling rule for the embeddings, which keeps the learning rate unchanged and scales the L2 loss. We conduct extensive experiments with four CTR prediction networks on two real-world datasets and successfully scaled 128 times the original batch size without accuracy loss. In particular, for CTR prediction model DeepFM training on the Criteo dataset, our optimization framework enlarges the batch size from 1K to 128K with over 0.1% AUC improvement and reduces training time from 12 hours to 10 minutes on a single V100 GPU. Our code locates at https://github.com/bytedance/LargeBatchCTR.

与准确性和计算成本具有密切关系的图像分辨率在网络培训中发挥了关键作用。在本文中，我们观察到缩小图像保留相对完整的形状语义，但是失去了广泛的纹理信息。通过形状语义的一致性和纹理信息的脆弱的启发，我们提出了一个名为时间性解决方案递减的新颖培训策略。其中，我们在时域中随机将训练图像降低到较小的分辨率。在使用缩小图像和原始图像的替代训练期间，图像中的不稳定纹理信息导致纹理相关模式与正确标签之间的相关性较弱，自然强制执行模型，以更多地依赖于稳健的形状属性。符合人类决策规则。令人惊讶的是，我们的方法大大提高了卷积神经网络的计算效率。在Imagenet分类上，使用33％的计算量（随机将培训图像随机降低到112 $ \倍112美元）仍然可以将resnet-50从76.32％提高到77.71％，并使用63％的计算量（随机减少在50％时期的训练图像到112 x 112）可以改善resnet-50至78.18％。

Visual question answering (VQA) is challenging not only because the model has to handle multi-modal information, but also because it is just so hard to collect sufficient training examples -- there are too many questions one can ask about an image. As a result, a VQA model trained solely on human-annotated examples could easily over-fit specific question styles or image contents that are being asked, leaving the model largely ignorant about the sheer diversity of questions. Existing methods address this issue primarily by introducing an auxiliary task such as visual grounding, cycle consistency, or debiasing. In this paper, we take a drastically different approach. We found that many of the "unknowns" to the learned VQA model are indeed "known" in the dataset implicitly. For instance, questions asking about the same object in different images are likely paraphrases; the number of detected or annotated objects in an image already provides the answer to the "how many" question, even if the question has not been annotated for that image. Building upon these insights, we present a simple data augmentation pipeline SimpleAug to turn this "known" knowledge into training examples for VQA. We show that these augmented examples can notably improve the learned VQA models' performance, not only on the VQA-CP dataset with language prior shifts but also on the VQA v2 dataset without such shifts. Our method further opens up the door to leverage weakly-labeled or unlabeled images in a principled way to enhance VQA models. Our code and data are publicly available at https://github.com/heendung/simpleAUG.

Vanilla用于物体检测和实例分割的模型遭受重偏向朝着长尾设置中的频繁对象进行偏向。现有方法主要在培训期间解决此问题，例如，通过重新采样或重新加权。在本文中，我们调查了一个很大程度上被忽视的方法 - 置信分数的后处理校准。我们提出NORCAL，用于长尾对象检测和实例分割的归一化校准校准，简单而简单的配方，通过其训练样本大小重新恢复每个阶级的预测得分。我们展示了单独处理背景类并使每个提案的课程分数标准化是实现卓越性能的键。在LVIS DataSet上，Norcal不仅可以在罕见的课程上有效地改善所有基线模型，也可以在普通和频繁的阶级上改进。最后，我们进行了广泛的分析和消融研究，以了解我们方法的各种建模选择和机制的见解。我们的代码在https://github.com/tydpan/norcal/上公开提供。

一个多世纪以前，伊万·P·帕夫洛夫（Ivan P. Pavlov）在经典实验中展示了狗如何学会将铃铛与食物联系起来，从而导致戒指导致唾液。如今，很少发现使用Pavlovian类型的关联学习用于人工智能（AI）应用程序，即使其他学习概念，尤其是对人工神经网络（ANN）的反向传播也蓬勃发展。但是，使用反向传播方法的训练在“常规” ANN上，尤其是现代深神经网络（DNNS）的形式，是计算和能量密集型的。在这里，我们在实验上展示了使用单个（或单一）关联硬件元素的无反向传播学习形式。我们使用相位变换材料与芯片级联方向耦合器相结合的集成光子平台上意识到这一点。然后，我们使用我们的Monadic Pavlovian光子硬件开发扩展的电路网络，该硬件可以基于单元素关联提供独特的机器学习框架，并且重要的是，重要的是，使用无反向传播的架构来解决一般学习任务。我们的方法通过在传统的神经网络方法中学习来减轻施加的计算负担，从而提高了速度，同时还提供了我们光子实现固有的更高带宽。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part segmentation into one task. Previous works utilize separated approaches to handle thing, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework named Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we make the following contributions: Firstly, we design a meta-architecture that decouples part feature and things/stuff feature, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Secondly, we propose a new metric Part-Whole Quality (PWQ) to better measure such task from both pixel-region and part-whole perspectives. It can also decouple the error for part segmentation and panoptic segmentation. Thirdly, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross attention scheme to further boost part segmentation qualities. We design a new part-whole interaction method using masked cross attention. Finally, the extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results with a significant cost drop of 70% on GFlops and 50% on parameters. Our models can serve as a strong baseline and aid future research in PPS. Code will be available.

Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news. To protect rankings, existing works mainly develop privacy protection on a single ranking within a set of ranking or pairwise comparisons of a ranking under the $\epsilon$-differential privacy. This paper proposes a novel notion called $\epsilon$-ranking differential privacy for protecting ranks. We establish the connection between the Mallows model (Mallows, 1957) and the proposed $\epsilon$-ranking differential privacy. This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed $\epsilon$-ranking differential privacy. Theoretical results regarding the utility of synthetic rankings in the downstream tasks, including the inference attack and the personalized ranking tasks, are established. For the inference attack, we quantify how $\epsilon$ affects the estimation of the true ranking based on synthetic rankings. For the personalized ranking task, we consider varying privacy preferences among users and quantify how their privacy preferences affect the consistency in estimating the optimal ranking function. Extensive numerical experiments are carried out to verify the theoretical results and demonstrate the effectiveness of the proposed synthetic ranking algorithm.

In this work, we focus on instance-level open vocabulary segmentation, intending to expand a segmenter for instance-wise novel categories without mask annotations. We investigate a simple yet effective framework with the help of image captions, focusing on exploiting thousands of object nouns in captions to discover instances of novel classes. Rather than adopting pretrained caption models or using massive caption datasets with complex pipelines, we propose an end-to-end solution from two aspects: caption grounding and caption generation. In particular, we devise a joint Caption Grounding and Generation (CGG) framework based on a Mask Transformer baseline. The framework has a novel grounding loss that performs explicit and implicit multi-modal feature alignments. We further design a lightweight caption generation head to allow for additional caption supervision. We find that grounding and generation complement each other, significantly enhancing the segmentation performance for novel categories. We conduct extensive experiments on the COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS) and Open Set Panoptic Segmentation (OSPS). The results demonstrate the superiority of our CGG framework over previous OVIS methods, achieving a large improvement of 6.8% mAP on novel classes without extra caption data. Our method also achieves over 15% PQ improvements for novel classes on the OSPS benchmark under various settings.