Zero-Shot Learning has been a highlighted research topic in both vision and language areas. Recently, most existing methods adopt structured knowledge information to model explicit correlations among categories and use deep graph convolutional network to propagate information between different categories. However, it is difficult to add new categories to existing structured knowledge graph, and deep graph convolutional network suffers from over-smoothing problem. In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation. Our semantic enhanced knowledge graph can further enhance the correlations among categories and make it easy to absorb new categories. To propagate information on the knowledge graph, we propose a novel Residual Graph Convolutional Network (ResGCN), which can effectively alleviate the problem of over-smoothing. Experiments conducted on the widely used large-scale ImageNet-21K dataset and AWA2 dataset show the effectiveness of our method, and establish a new state-of-the-art on zero-shot learning. Moreover, our results on the large-scale ImageNet-21K with various feature extraction networks show that our method has better generalization and robustness.

Neural Radiance Fields (NeRF) methods have proved effective as compact, high-quality and versatile representations for 3D scenes, and enable downstream tasks such as editing, retrieval, navigation, etc. Various neural architectures are vying for the core structure of NeRF, including the plain Multi-Layer Perceptron (MLP), sparse tensors, low-rank tensors, hashtables and their compositions. Each of these representations has its particular set of trade-offs. For example, the hashtable-based representations admit faster training and rendering but their lack of clear geometric meaning hampers downstream tasks like spatial-relation-aware editing. In this paper, we propose Progressive Volume Distillation (PVD), a systematic distillation method that allows any-to-any conversions between different architectures, including MLP, sparse or low-rank tensors, hashtables and their compositions. PVD consequently empowers downstream applications to optimally adapt the neural representations for the task at hand in a post hoc fashion. The conversions are fast, as distillation is progressively performed on different levels of volume representations, from shallower to deeper. We also employ special treatment of density to deal with its specific numerical instability problem. Empirical evidence is presented to validate our method on the NeRF-Synthetic, LLFF and TanksAndTemples datasets. For example, with PVD, an MLP-based NeRF model can be distilled from a hashtable-based Instant-NGP model at a 10X~20X faster speed than being trained the original NeRF from scratch, while achieving a superior level of synthesis quality. Code is available at https://github.com/megvii-research/AAAI2023-PVD.

Several works have proven that finetuning is an applicable approach for debiasing contextualized word embeddings. Similarly, discrete prompts with semantic meanings have shown to be effective in debiasing tasks. With unfixed mathematical representation at the token level, continuous prompts usually surpass discrete ones at providing a pre-trained language model (PLM) with additional task-specific information. Despite this, relatively few efforts have been made to debias PLMs by prompt tuning with continuous prompts compared to its discrete counterpart. Furthermore, for most debiasing methods that alter a PLM's original parameters, a major problem is the need to not only decrease the bias in the PLM but also to ensure that the PLM does not lose its representation ability. Finetuning methods typically have a hard time maintaining this balance, as they tend to violently remove meanings of attribute words. In this paper, we propose ADEPT, a method to debias PLMs using prompt tuning while maintaining the delicate balance between removing biases and ensuring representation ability. To achieve this, we propose a new training criterion inspired by manifold learning and equip it with an explicit debiasing term to optimize prompt tuning. In addition, we conduct several experiments with regard to the reliability, quality, and quantity of a previously proposed attribute training corpus in order to obtain a clearer prototype of a certain attribute, which indicates the attribute's position and relative distances to other words on the manifold. We evaluate ADEPT on several widely acknowledged debiasing benchmarks and downstream tasks, and find that it achieves competitive results while maintaining (and in some cases even improving) the PLM's representation ability. We further visualize words' correlation before and after debiasing a PLM, and give some possible explanations for the visible effects.

Video event extraction aims to detect salient events from a video and identify the arguments for each event as well as their semantic roles. Existing methods focus on capturing the overall visual scene of each frame, ignoring fine-grained argument-level information. Inspired by the definition of events as changes of states, we propose a novel framework to detect video events by tracking the changes in the visual states of all involved arguments, which are expected to provide the most informative evidence for the extraction of video events. In order to capture the visual state changes of arguments, we decompose them into changes in pixels within objects, displacements of objects, and interactions among multiple arguments. We further propose Object State Embedding, Object Motion-aware Embedding and Argument Interaction Embedding to encode and track these changes respectively. Experiments on various video event extraction tasks demonstrate significant improvements compared to state-of-the-art models. In particular, on verb classification, we achieve 3.49% absolute gains (19.53% relative gains) in F1@5 on Video Situation Recognition.

在过去的十年中，AI AID毒品发现（AIDD）的计算方法和数据集策划的繁荣发展。但是，现实世界中的药物数据集经常表现出高度不平衡的分布，这在很大程度上被当前的文献忽略了，但可能会严重损害机器学习应用程序的公平性和概括。在这一观察结果的激励下，我们介绍了Imdrug，这是一个全面的基准标准，其开源python库由4个不平衡设置，11个AI-Ready数据集，54个学习任务和16种为不平衡学习量身定制的基线算法。它为涵盖广泛的药物发现管道（例如分子建模，药物靶标相互作用和逆合合成）的问题和解决方案提供了可访问且可定制的测试床。我们通过新的评估指标进行广泛的实证研究，以证明现有算法在数据不平衡情况下无法解决药物和药物挑战。我们认为，Imdrug为未来的研究和发展开辟了途径，在AIDD和深度不平衡学习的交集中对现实世界中的挑战开辟了道路。

初始化时（OPAI）的一次性网络修剪是降低网络修剪成本的有效方法。最近，人们越来越相信数据在OPAI中是不必要的。但是，我们通过两种代表性的OPAI方法，即剪切和掌握的消融实验获得了相反的结论。具体而言，我们发现信息数据对于增强修剪性能至关重要。在本文中，我们提出了两种新颖的方法，即判别性的单发网络修剪（DOP）和超级缝制，以通过高级视觉判别图像贴片来修剪网络。我们的贡献如下。（1）广泛的实验表明OPAI是数据依赖性的。（2）超级缝线的性能明显优于基准图像网上的原始OPAI方法，尤其是在高度压缩的模型中。

由于其不断增加的资源需求，在低资源边缘设备上部署深层神经网络是具有挑战性的。最近的研究提出了无倍数的神经网络，以减少计算和记忆消耗。 Shift神经网络是这些减少的最有效工具之一。但是，现有的低位换档网络不如其完整的精度对应物准确，并且由于其固有的设计缺陷，无法有效地转移到广泛的任务中。我们提出了利用以下新颖设计的光泽网络。首先，我们证明低位移位网络中的零重量值既不有用，也不简化模型推断。因此，我们建议使用零移动机制来简化推理，同时增加模型容量。其次，我们设计了一个新的指标，以测量训练低位移位网络中的重量冻结问题，并提出一个符号尺度分解以提高训练效率。第三，我们提出了低变化的随机初始化策略，以提高模型在转移学习方案中的性能。我们对各种计算机视觉和语音任务进行了广泛的实验。实验结果表明，光泽网络明显胜过现有的低位乘法网络，并可以实现全精度对应物的竞争性能。它还表现出强大的转移学习表现，没有准确性下降。

近年来，基于方面的情感分析（ABSA）已成为一项普遍的任务。但是，在当前的ABSA研究中缺乏统一的框架使得公平地比较不同模型的性能是具有挑战性的。因此，我们创建了一个开源的ABSA框架，即Pyabsa。此外，以前的努力通常忽略了前体方面术语提取（ASC）子任务，并专注于方面情感分类（ATE）子任务。与以前的作品相比，PYABSA包括方面术语提取，方面情感分类和文本分类的特征，而多个ABSA子任务可以适应Pyabsa，因为其模块化体系结构。为了促进ABSA应用程序，Pyabsaseamless集成了多语言建模，自动化数据集注释等，这有助于部署ABSA服务。在ASC和Ate中，Pyabsa分别提供多达33个和7个内置模型，而所有模型都提供快速培训和即时推断。此外，Pyabsa还包含21个增强ABSA数据集的180K+ ABSA实例，用于应用和研究。 Pyabsa可从https://github.com/yangheng95/pyabsa获得

手术场景细分对于促使机器人手术的认知援助至关重要。但是，以逐帧方式以像素为单位的注释视频是昂贵且耗时的。为了大大减轻标签负担，在这项工作中，我们从机器人手术视频中研究了半监督的场景细分，这实际上是必不可少的，但以前很少探索。我们考虑在等距采样下的临床上适当的注释情况。然后，我们提出了PGV-CL，这是一种新型的伪标签引导的跨视频对比学习方法，以增强场景分割。它有效地利用了未标记的数据来实现可信赖和全球模型的正则化，从而产生更具歧视性的特征表示。具体来说，对于可信赖的表示学习，我们建议合并伪标签以指导对选择，从而获得更可靠的代表对像素对比度。此外，我们将代表学习空间从以前的图像级扩展到交叉视频，该图像可以捕获全球语义以使学习过程受益。我们广泛评估了公共机器人手术数据集Edovis18和公共白内障数据集Cadis的方法。实验结果证明了我们方法的有效性，在不同的标签比下始终超过了最先进的半监督方法，甚至超过了10.1％标签的destovis18上的全面监督培训。

深度神经网络（DNN）在多个领域取得了令人印象深刻的成功。多年来，随着更深层次，更复杂的体系结构的扩散，这些模型的准确性已经提高。因此，最新的解决方案通常在计算上很昂贵，这使得它们不适合在边缘计算平台上部署。为了减轻推断卷积神经网络（CNN）的高计算，内存和功率要求，我们提出了两次量化量化的使用，该量化量量化量的功率量化将连续参数量化为低点的两个值值。这通过删除昂贵的乘法操作和使用低位权重来降低计算复杂性。 Resnet被用作解决方案的基础，并根据口语理解（SLU）任务评估了建议的模型。实验结果表明，在测试集中，我们的低位量化实现了换档神经网络体系结构的性能，其低位量化达到了98.76 \％，这与其完整精确的对应物和最先进的解决方案相当。