Are extralinguistic signals such as image pixels crucial for inducing constituency grammars? While past work has shown substantial gains from multimodal cues, we investigate whether such gains persist in the presence of rich information from large language models (LLMs). We find that our approach, LLM-based C-PCFG (LC-PCFG), outperforms previous multi-modal methods on the task of unsupervised constituency parsing, achieving state-of-the-art performance on a variety of datasets. Moreover, LC-PCFG results in an over 50% reduction in parameter count, and speedups in training time of 1.7x for image-aided models and more than 5x for video-aided models, respectively. These results challenge the notion that extralinguistic signals such as image pixels are needed for unsupervised grammar induction, and point to the need for better text-only baselines in evaluating the need of multi-modality for the task.
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该技术报告描述了无回报(PNR)时间定位挑战的EGO4D点的SVIT方法。我们提出了一个学习框架的结构(简称SVIT),该结构证明了仅在训练过程中仅可用的少量图像的结构才能改善视频模型。SVIT依靠两个关键见解。首先,由于图像和视频都包含结构化信息,因此我们用一组\ emph {对象令牌}丰富了一个可以在图像和视频中使用的\ emph {对象令牌}的模型。其次,视频中各个帧的场景表示应与静止图像的场景表示“对齐”。这是通过“框架夹一致性”损失实现的,该损失可确保图像和视频之间结构化信息的流动。SVIT在挑战测试集上获得了强劲的性能,并具有0.656绝对时间定位误差。
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最近的动作识别模型通过整合对象,其位置和互动来取得令人印象深刻的结果。但是,为每个框架获得密集的结构化注释是乏味且耗时的,使这些方法的训练昂贵且可扩展性较低。同时,如果可以在感兴趣的域内或之外使用一小部分带注释的图像,我们如何将它们用于下游任务的视频?我们提出了一个学习框架的结构(简称SVIT),该结构证明了仅在训练过程中仅可用的少量图像的结构才能改善视频模型。 SVIT依靠两个关键见解。首先,由于图像和视频都包含结构化信息,因此我们用一组\ emph {对象令牌}丰富了一个可以在图像和视频中使用的\ emph {对象令牌}的模型。其次,视频中各个帧的场景表示应与静止图像的场景表示“对齐”。这是通过\ emph {frame-clip一致性}损失来实现的,该损失可确保图像和视频之间结构化信息的流动。我们探索场景结构的特定实例化,即\ emph {手对象图},由手和对象组成,其位置为节点,以及触点/no-contact的物理关系作为边缘。 SVIT在多个视频理解任务和数据集上显示出强烈的性能改进;它在EGO4D CVPR'22对象状态本地化挑战中赢得了第一名。对于代码和预算模型,请访问\ url {https://eladb3.github.io/svit/}的项目页面
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While today's video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video architectures can only process <5 seconds of a video without hitting the computation or memory bottlenecks. In this paper, we propose a new strategy to overcome this challenge. Instead of trying to process more frames at once like most existing methods, we propose to process videos in an online fashion and cache "memory" at each iteration. Through the memory, the model can reference prior context for long-term modeling, with only a marginal cost. Based on this idea, we build MeMViT, a Memory-augmented Multiscale Vision Transformer, that has a temporal support 30x longer than existing models with only 4.5% more compute; traditional methods need >3,000% more compute to do the same. On a wide range of settings, the increased temporal support enabled by MeMViT brings large gains in recognition accuracy consistently. MeMViT obtains state-of-the-art results on the AVA, EPIC-Kitchens-100 action classification, and action anticipation datasets. Code and models are available at https://github.com/facebookresearch/memvit.
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生成的对抗网络(GANS)是用于各种应用的一类生成模型,但是已知它们遭受模式崩溃问题,其中由发电机忽略目标分布的一些模式。使用新数据生成程序的调查研究表明,发电机的模式崩溃是由鉴别者在先前看到的样本上维持分类准确性的模式,这是一种被持续学习的灾难性遗忘的现象。这种观察的动机,我们介绍了一种新颖的培训程序,可以自适应地产生额外的鉴别者来记住以前的一代模式。在几个数据集上,我们表明我们的培训方案可以插入现有的GaN框架,以减轻模式崩溃并改进GaN评估的标准度量。
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在本文中,我们将多尺度视觉变压器(MVIT)作为图像和视频分类的统一架构,以及对象检测。我们提出了一种改进的MVIT版本,它包含分解的相对位置嵌入和残余汇集连接。我们以五种尺寸实例化此架构,并评估Imagenet分类,COCO检测和动力学视频识别,在此优先效果。我们进一步比较了MVITS的汇集注意力来窗口注意力机制,其中它在准确性/计算中优于后者。如果没有钟声,MVIT在3个域中具有最先进的性能:ImageNet分类的准确性为88.8%,Coco对象检测的56.1盒AP和动力学-400视频分类的86.1%。代码和模型将公开可用。
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最近,视频变压器在视频理解方面取得了巨大成功,超过了CNN性能;然而,现有的视频变换器模型不会明确地模拟对象,尽管对象对于识别操作至关重要。在这项工作中,我们呈现对象区域视频变换器(Orvit),一个\ emph {对象为中心}方法,它与直接包含对象表示的块扩展视频变压器图层。关键的想法是从早期层开始融合以对象形式的表示,并将它们传播到变压器层中,从而影响整个网络的时空表示。我们的orvit块由两个对象级流组成:外观和动态。在外观流中,“对象区域关注”模块在修补程序上应用自我关注和\ emph {对象区域}。以这种方式,Visual对象区域与统一修补程序令牌交互,并通过上下文化对象信息来丰富它们。我们通过单独的“对象 - 动态模块”进一步模型对象动态,捕获轨迹交互,并显示如何集成两个流。我们在四个任务和五个数据集中评估我们的模型:在某事物中的某些问题和几次射击动作识别,以及在AVA上的某些时空动作检测,以及在某种东西上的标准动作识别 - 某种东西 - 东西,潜水48和EPIC-Kitchen100。我们在考虑的所有任务和数据集中展示了强大的性能改进,展示了将对象表示的模型的值集成到变压器体系结构中。对于代码和预用模型,请访问项目页面\ url {https://roeiherz.github.io/orvit/}
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We present Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models. Multiscale Transformers have several channel-resolution scale stages. Starting from the input resolution and a small channel dimension, the stages hierarchically expand the channel capacity while reducing the spatial resolution. This creates a multiscale pyramid of features with early layers operating at high spatial resolution to model simple low-level visual information, and deeper layers at spatially coarse, but complex, high-dimensional features. We evaluate this fundamental architectural prior for modeling the dense nature of visual signals for a variety of video recognition tasks where it outperforms concurrent vision transformers that rely on large scale external pre-training and are 5-10× more costly in computation and parameters. We further remove the temporal dimension and apply our model for image classification where it outperforms prior work on vision transformers. Code is available at: https: //github.com/facebookresearch/SlowFast.
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