在本文中，我们将针对基于文本的描述的任意类别执行全新的计算机视觉任务，开放式全磁全面分割，该任务旨在执行全景分段（背景语义标签 +前景实例分段）。我们首先构建了一种基线方法，而无需填充或蒸馏以利用现有夹模型中的知识。然后，我们开发了一种新方法MaskClip，该方法是一种基于变压器的方法，该方法使用带有基于VIT的夹子主链的掩码查询来执行语义分割和对象实例分割。在这里，我们设计了一个相对的掩码注意力（RMA）模块，以将分割作为VIT夹模型的其他令牌。 MaskClip通过避免使用外部剪贴图像模型的暂停操作来裁剪图像贴片和计算功能，从而有效地有效地利用预训练的密集/局部剪辑功能。我们为开放式综合综合分割和最先进的结果获得了令人鼓舞的结果。我们显示具有自定义类别的MaskClip的定性插图。

我们研究视觉变压器（VIT）的半监督学习（SSL），尽管VIT架构广泛采用了不同的任务，但视觉变形金刚（VIT）还是一个不足的主题。为了解决这个问题，我们提出了一条新的SSL管道，该管道由第一个联合国/自制的预训练组成，然后是监督的微调，最后是半监督的微调。在半监督的微调阶段，我们采用指数的移动平均线（EMA） - 教师框架，而不是流行的FixMatch，因为前者更稳定，并且为半手不见的视觉变压器提供了更高的准确性。此外，我们提出了一种概率的伪混合机制来插入未标记的样品及其伪标签以改善正则化，这对于训练电感偏差较弱的训练VIT很重要。我们所提出的方法被称为半vit，比半监督分类设置中的CNN对应物获得可比性或更好的性能。半vit还享受VIT的可伸缩性优势，可以很容易地扩展到具有越来越高的精度的大型模型。例如，半效率总数仅使用1％标签在Imagenet上获得令人印象深刻的80％TOP-1精度，使用100％ImageNet标签与Inception-V4相当。

我们呈现了对比邻域对准（CNA），一种歧管学习方法来维持学习特征的拓扑，由此映射到源（教师）模型的附近表示的数据点也被目标（学生）模型映射到邻居。目标模型旨在模拟使用对比损耗来模拟源代表空间的局部结构。CNA是一种无人监督的学习算法，不需要对各个样本的地面真理标签。CNA在三种情况下示出：歧管学习，其中模型在尺寸减小空间中保持原始数据的本地拓扑;模型蒸馏，其中小学生模型培训以模仿更大的老师;和遗留模型更新，其中旧模型被更强大的更强大的型号。实验表明，CNA能够在高维空间中捕获歧管，并与其域中的竞争方法相比提高性能。

执行单个图像整体理解和3D重建是计算机视觉中的核心任务。本文介绍了从单个RGB图像的室内和室外场景执行整体图像分段，对象检测，实例分段，深度估计和对象实例3D重建。我们命名我们的系统Panoptic 3D解析，其中Panoptic Segsation（“填写”分割和“检测/分割”的“检测/分割”。我们设计了一个舞台明智的系统，其中不存在一整套注释。此外，我们介绍了一个端到端的管道，在合成数据集上培训，具有全套注释。我们在室内（3D-Flact）和户外（可可和城市）的场景上显示结果。我们提出的Panoptic 3D解析框架指向计算机愿景中有希望的方向。它可以应用于各种应用，包括自主驾驶，映射，机器人，设计，计算机图形学，机器人，人机互动和增强现实。

Despite the steady progress in video analysis led by the adoption of convolutional neural networks (CNNs), the relative improvement has been less drastic as that in 2D static image classification. Three main challenges exist including spatial (image) feature representation, temporal information representation, and model/computation complexity. It was recently shown by Carreira and Zisserman that 3D CNNs, inflated from 2D networks and pretrained on Ima-geNet, could be a promising way for spatial and temporal representation learning. However, as for model/computation complexity, 3D CNNs are much more expensive than 2D CNNs and prone to overfit. We seek a balance between speed and accuracy by building an effective and efficient video classification system through systematic exploration of critical network design choices. In particular, we show that it is possible to replace many of the 3D convolutions by low-cost 2D convolutions. Rather surprisingly, best result (in both speed and accuracy) is achieved when replacing the 3D convolutions at the bottom of the network, suggesting that temporal representation learning on high-level "semantic" features is more useful. Our conclusion generalizes to datasets with very different properties. When combined with several other cost-effective designs including separable spatial/temporal convolution and feature gating, our system results in an effective video classification system that that produces very competitive results on several action classification benchmarks (Kinetics, Something-something, UCF101 and HMDB), as well as two action detection (localization) benchmarks (JHMDB and UCF101-24).

We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call "cardinality" (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online 1 .

Recent progress on salient object detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs). Semantic segmentation and salient object detection algorithms developed lately have been mostly based on Fully Convolutional Neural Networks (FCNs). There is still a large room for improvement over the generic FCN models that do not explicitly deal with the scale-space problem. Holistically-Nested Edge Detector (HED) provides a skip-layer structure with deep supervision for edge and boundary detection, but the performance gain of HED on saliency detection is not obvious. In this paper, we propose a new salient object detection method by introducing short connections to the skip-layer structures within the HED architecture. Our framework takes full advantage of multi-level and multi-scale features extracted from FCNs, providing more advanced representations at each layer, a property that is critically needed to perform segment detection. Our method produces state-of-theart results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency (0.08 seconds per image), effectiveness, and simplicity over the existing algorithms. Beyond that, we conduct an exhaustive analysis on the role of training data on performance. Our experimental results provide a more reasonable and powerful training set for future research and fair comparisons.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

Recent studies have shown that using an external Language Model (LM) benefits the end-to-end Automatic Speech Recognition (ASR). However, predicting tokens that appear less frequently in the training set is still quite challenging. The long-tail prediction problems have been widely studied in many applications, but only been addressed by a few studies for ASR and LMs. In this paper, we propose a new memory augmented lookup dictionary based Transformer architecture for LM. The newly introduced lookup dictionary incorporates rich contextual information in training set, which is vital to correctly predict long-tail tokens. With intensive experiments on Chinese and English data sets, our proposed method is proved to outperform the baseline Transformer LM by a great margin on both word/character error rate and tail tokens error rate. This is achieved without impact on the decoding efficiency. Overall, we demonstrate the effectiveness of our proposed method in boosting the ASR decoding performance, especially for long-tail tokens.

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.