Modern deep networks can be better generalized when trained with noisy samples and regularization techniques. Mixup and CutMix have been proven to be effective for data augmentation to help avoid overfitting. Previous Mixup-based methods linearly combine images and labels to generate additional training data. However, this is problematic if the object does not occupy the whole image as we demonstrate in Figure 1. Correctly assigning the label weights is hard even for human beings and there is no clear criterion to measure it. To tackle this problem, in this paper, we propose LUMix, which models such uncertainty by adding label perturbation during training. LUMix is simple as it can be implemented in just a few lines of code and can be universally applied to any deep networks \eg CNNs and Vision Transformers, with minimal computational cost. Extensive experiments show that our LUMix can consistently boost the performance for networks with a wide range of diversity and capacity on ImageNet, \eg $+0.7\%$ for a small model DeiT-S and $+0.6\%$ for a large variant XCiT-L. We also demonstrate that LUMix can lead to better robustness when evaluated on ImageNet-O and ImageNet-A. The source code can be found \href{https://github.com/kevin-ssy/LUMix}{here}

视频Panoptic semonation（VPS）旨在为每个像素分配类标签，唯一地分割和识别所有帧的所有对象实例。经典解决方案通常将VPS任务分解为多个子任务，并利用多个代理（例如框和掩码，中心和偏移）来表示对象。然而，这种鸿沟和征服策略需要在空间和时间域中进行复杂的后处理，并且易于来自代理任务的失败。在本文中，灵感来自以对象为中心的学习，它学习紧凑且强大的对象表示，我们呈现了Slot-VPS，这是此任务的第一个端到端框架。我们在视频中编码所有Panoptic实体，包括前景实例和后台语义，其中包含称为Panoptic插槽的统一表示。通过提出的视频Panoptic Retriever检索并将相干的时空对象的信息检索并编码到Panoptic插槽中，使其能够以统一的方式本地化，段，区分和关联对象。最后，输出Panoptic插槽可以直接转换为视频中Panoptic对象的类，掩码和对象ID。我们开展广泛的消融研究，并展示了我们对两个基准数据集，CityCAPE-VPS（\ Texit {Val}和测试集）和Viper（\ Texit {val}集）的有效性，实现了新的最先进的性能分别为63.7,63.3和56.2 VPQ。

已经发现基于混合的增强对于培训期间的概括模型有效，特别是对于视觉变压器（VITS），因为它们很容易过度装备。然而，先前的基于混合的方法具有潜在的先验知识，即目标的线性内插比应保持与输入插值中提出的比率相同。这可能导致一个奇怪的现象，有时由于增强中的随机过程，混合图像中没有有效对象，但标签空间仍然存在响应。为了弥合输入和标签空间之间的这种差距，我们提出了透明度，该差别将基于视觉变压器的注意图混合标签。如果受关注图的相应输入图像加权，则标签的置信度将会更大。传输令人尴尬地简单，可以在几行代码中实现，而不会在不引入任何额外的参数和拖鞋到基于Vit的模型。实验结果表明，我们的方法可以在想象集分类上一致地始终改善各种基于Vit的模型。在ImageNet上预先接受过扫描后，基于Vit的模型还展示了对语义分割，对象检测和实例分割的更好的可转换性。当在评估4个不同的基准时，传输展示展示更加强劲。代码将在https://github.com/beckschen/transmix上公开提供。

人类视力能够从整个场景中捕获部分整个分层信息。本文介绍了Visual解析器（VIP），它明确地构造了与变压器的等层次结构。 VIP将视觉表示分为两个级别，零件级别和整个级别。每个部分的信息代表整个内部的几个独立向量的组合。为了模拟两个级别的表示，我们首先通过注意机制将整体信息从整体编码为部分向量，然后将零件向量内的全局信息解码回到整个表示中。通过使用所提出的编码器 - 解码器交互迭代地解析两个级别，模型可以逐渐改进两个级别上的特征。实验结果表明，VIP可以在三个主要任务中实现非常竞争的性能。分类，检测和实例分割。特别是，它可以通过对象检测的大边缘超越先前的最先进的CNN主干。 VIP系列的小型型号为7.2美元，参数为$ 7.2 \ times $ 10.9 \ times $更少的拖鞋可以与最大的resnext-101-64 $ \ times $ 4d的resne（x）t家族相对表现。可视化结果还表明，学习部分对预测类具有高度信息，使VIP比以前的基本架构更可说明。代码可在https://github.com/kevin-ssy/vip上获得。

The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing selfdriving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the overall viability of the technology. In an effort to help align the research community's contributions with real-world selfdriving problems, we introduce a new large-scale, high quality, diverse dataset. Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies. It is 15x more diverse than the largest cam-era+LiDAR dataset available based on our proposed geographical coverage metric. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames. Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. We further study the effects of dataset size and generalization across geographies on 3D detection methods. Find data, code and more up-to-date information at http://www.waymo.com/open.

Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A simple combination of Cascade R-CNN and Mask R-CNN only brings limited gain. In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation. In this work, we propose a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background. Overall, this framework can learn more discriminative features progressively while integrating complementary features together in each stage. Without bells and whistles, a single HTC obtains 38.4% and 1.5% improvement over a strong Cascade Mask R-CNN baseline on MSCOCO dataset. Moreover, our overall system achieves 48.6 mask AP on the test-challenge split, ranking 1st in the COCO 2018 Challenge Object Detection Task. Code is available at: https://github.com/ open-mmlab/mmdetection.

The proliferation of automatic faithfulness metrics for summarization has produced a need for benchmarks to evaluate them. While existing benchmarks measure the correlation with human judgements of faithfulness on model-generated summaries, they are insufficient for diagnosing whether metrics are: 1) consistent, i.e., decrease as errors are introduced into a summary, 2) effective on human-written texts, and 3) sensitive to different error types (as summaries can contain multiple errors). To address these needs, we present a benchmark of unfaithful minimal pairs (BUMP), a dataset of 889 human-written, minimally different summary pairs, where a single error (from an ontology of 7 types) is introduced to a summary from the CNN/DailyMail dataset to produce an unfaithful summary. We find BUMP complements existing benchmarks in a number of ways: 1) the summaries in BUMP are harder to discriminate and less probable under SOTA summarization models, 2) BUMP enables measuring the consistency of metrics, and reveals that the most discriminative metrics tend not to be the most consistent, 3) BUMP enables the measurement of metrics' performance on individual error types and highlights areas of weakness for future work.

Recent studies have shown that CLIP has achieved remarkable success in performing zero-shot inference while its fine-tuning performance is not satisfactory. In this paper, we identify that fine-tuning performance is significantly impacted by hyper-parameter choices. We examine various key hyper-parameters and empirically evaluate their impact in fine-tuning CLIP for classification tasks through a comprehensive study. We find that the fine-tuning performance of CLIP is substantially underestimated. Equipped with hyper-parameter refinement, we demonstrate CLIP itself is better or at least competitive in fine-tuning compared with large-scale supervised pre-training approaches or latest works that use CLIP as prediction targets in Masked Image Modeling. Specifically, CLIP ViT-Base/16 and CLIP ViT-Large/14 can achieve 85.7%,88.0% finetuning Top-1 accuracy on the ImageNet-1K dataset . These observations challenge the conventional conclusion that CLIP is not suitable for fine-tuning, and motivate us to rethink recently proposed improvements based on CLIP. We will release our code publicly at \url{https://github.com/LightDXY/FT-CLIP}.

This paper presents a 3D generative model that uses diffusion models to automatically generate 3D digital avatars represented as neural radiance fields. A significant challenge in generating such avatars is that the memory and processing costs in 3D are prohibitive for producing the rich details required for high-quality avatars. To tackle this problem we propose the roll-out diffusion network (Rodin), which represents a neural radiance field as multiple 2D feature maps and rolls out these maps into a single 2D feature plane within which we perform 3D-aware diffusion. The Rodin model brings the much-needed computational efficiency while preserving the integrity of diffusion in 3D by using 3D-aware convolution that attends to projected features in the 2D feature plane according to their original relationship in 3D. We also use latent conditioning to orchestrate the feature generation for global coherence, leading to high-fidelity avatars and enabling their semantic editing based on text prompts. Finally, we use hierarchical synthesis to further enhance details. The 3D avatars generated by our model compare favorably with those produced by existing generative techniques. We can generate highly detailed avatars with realistic hairstyles and facial hair like beards. We also demonstrate 3D avatar generation from image or text as well as text-guided editability.

Estimating the structure of directed acyclic graphs (DAGs) of features (variables) plays a vital role in revealing the latent data generation process and providing causal insights in various applications. Although there have been many studies on structure learning with various types of data, the structure learning on the dynamic graph has not been explored yet, and thus we study the learning problem of node feature generation mechanism on such ubiquitous dynamic graph data. In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features. These two kinds of relationships form a DAG, which could effectively characterize the feature generation process in a concise way. To learn such a DAG, we cast the learning problem as a continuous score-based optimization problem, which consists of a differentiable score function to measure the validity of the learned DAGs and a smooth acyclicity constraint to ensure the acyclicity of the learned DAGs. These two components are translated into an unconstraint augmented Lagrangian objective which could be minimized by mature continuous optimization techniques. The resulting algorithm, named GraphNOTEARS, outperforms baselines on simulated data across a wide range of settings that may encounter in real-world applications. We also apply the proposed approach on two dynamic graphs constructed from the real-world Yelp dataset, demonstrating our method could learn the connections between node features, which conforms with the domain knowledge.