自Bert（Devlin等，2018）以来，学习上下文化的单词嵌入一直是NLP中的事实上的标准。然而，学习上下文化短语嵌入的进展受到缺乏人类通知的语句基准基准的阻碍。为了填补这一空白，我们提出了PIC- 〜28K名词短语的数据集伴随着它们的上下文Wikipedia页面，以及一套三个任务，这些任务增加了评估短语嵌入质量的难度。我们发现，在我们的数据集中进行的培训提高了排名模型的准确性，并明显地将问题答案（QA）模型推向了近人类的准确性，而在语义搜索上，鉴于询问短语和段落，在语义搜索上是95％的精确匹配（EM）。有趣的是，我们发现这种令人印象深刻的性能的证据是因为质量检查模型学会了更好地捕获短语的共同含义，而不管其实际背景如何。也就是说，在我们的短语中歧义歧义（PSD）任务上，SOTA模型的精度大大下降（60％EM），在两个不同情况下未能区分相同短语的两种不同感觉。在我们的3任任务基准测试中的进一步结果表明，学习上下文化的短语嵌入仍然是一个有趣的开放挑战。

命名实体识别（ner）是从文本中提取特定类型的命名实体的任务。当前的NER模型往往依赖于人类注释的数据集，要求在目标领域和实体上广泛参与专业知识。这项工作介绍了一个询问生成的方法，它通过询问反映实体类型的需求的简单自然语言问题来自动生成NER数据集（例如，哪种疾病？）到开放式域问题应答系统。不使用任何域中资源（即，培训句子，标签或域名词典），我们的模型在我们生成的数据集上仅培训了，这在很大程度上超过了四个不同域的六个基准测试的弱势监督模型。令人惊讶的是，在NCBI疾病中，我们的模型达到75.5 F1得分，甚至优于以前的最佳弱监督模型4.1 F1得分，它利用域专家提供的丰富的域名词典。制定具有自然语言的NER的需求，也允许我们为诸如奖项等细粒度实体类型构建NER模型，其中我们的模型甚至优于完全监督模型。在三个少量的NER基准测试中，我们的模型实现了新的最先进的性能。

Understanding the informative structures of scenes is essential for low-level vision tasks. Unfortunately, it is difficult to obtain a concrete visual definition of the informative structures because influences of visual features are task-specific. In this paper, we propose a single general neural network architecture for extracting task-specific structure guidance for scenes. To do this, we first analyze traditional spectral clustering methods, which computes a set of eigenvectors to model a segmented graph forming small compact structures on image domains. We then unfold the traditional graph-partitioning problem into a learnable network, named \textit{Scene Structure Guidance Network (SSGNet)}, to represent the task-specific informative structures. The SSGNet yields a set of coefficients of eigenvectors that produces explicit feature representations of image structures. In addition, our SSGNet is light-weight ($\sim$ 55K parameters), and can be used as a plug-and-play module for off-the-shelf architectures. We optimize the SSGNet without any supervision by proposing two novel training losses that enforce task-specific scene structure generation during training. Our main contribution is to show that such a simple network can achieve state-of-the-art results for several low-level vision applications including joint upsampling and image denoising. We also demonstrate that our SSGNet generalizes well on unseen datasets, compared to existing methods which use structural embedding frameworks. Our source codes are available at https://github.com/jsshin98/SSGNet.

In this paper, we learn a diffusion model to generate 3D data on a scene-scale. Specifically, our model crafts a 3D scene consisting of multiple objects, while recent diffusion research has focused on a single object. To realize our goal, we represent a scene with discrete class labels, i.e., categorical distribution, to assign multiple objects into semantic categories. Thus, we extend discrete diffusion models to learn scene-scale categorical distributions. In addition, we validate that a latent diffusion model can reduce computation costs for training and deploying. To the best of our knowledge, our work is the first to apply discrete and latent diffusion for 3D categorical data on a scene-scale. We further propose to perform semantic scene completion (SSC) by learning a conditional distribution using our diffusion model, where the condition is a partial observation in a sparse point cloud. In experiments, we empirically show that our diffusion models not only generate reasonable scenes, but also perform the scene completion task better than a discriminative model. Our code and models are available at https://github.com/zoomin-lee/scene-scale-diffusion

The deep neural network (DNN) models for object detection using camera images are widely adopted in autonomous vehicles. However, DNN models are shown to be susceptible to adversarial image perturbations. In the existing methods of generating the adversarial image perturbations, optimizations take each incoming image frame as the decision variable to generate an image perturbation. Therefore, given a new image, the typically computationally-expensive optimization needs to start over as there is no learning between the independent optimizations. Very few approaches have been developed for attacking online image streams while considering the underlying physical dynamics of autonomous vehicles, their mission, and the environment. We propose a multi-level stochastic optimization framework that monitors an attacker's capability of generating the adversarial perturbations. Based on this capability level, a binary decision attack/not attack is introduced to enhance the effectiveness of the attacker. We evaluate our proposed multi-level image attack framework using simulations for vision-guided autonomous vehicles and actual tests with a small indoor drone in an office environment. The results show our method's capability to generate the image attack in real-time while monitoring when the attacker is proficient given state estimates.

Iris segmentation is the initial step to identify biometric of animals to establish a traceability system of livestock. In this study, we propose a novel deep learning framework for pixel-wise segmentation with minimum use of annotation labels using BovineAAEyes80 public dataset. In the experiment, U-Net with VGG16 backbone was selected as the best combination of encoder and decoder model, demonstrating a 99.50% accuracy and a 98.35% Dice coefficient score. Remarkably, the selected model accurately segmented corrupted images even without proper annotation data. This study contributes to the advancement of the iris segmentation and the development of a reliable DNNs training framework.

We study grammar induction with mildly context-sensitive grammars for unsupervised discontinuous parsing. Using the probabilistic linear context-free rewriting system (LCFRS) formalism, our approach fixes the rule structure in advance and focuses on parameter learning with maximum likelihood. To reduce the computational complexity of both parsing and parameter estimation, we restrict the grammar formalism to LCFRS-2 (i.e., binary LCFRS with fan-out two) and further discard rules that require O(n^6) time to parse, reducing inference to O(n^5). We find that using a large number of nonterminals is beneficial and thus make use of tensor decomposition-based rank-space dynamic programming with an embedding-based parameterization of rule probabilities to scale up the number of nonterminals. Experiments on German and Dutch show that our approach is able to induce linguistically meaningful trees with continuous and discontinuous structures

This work proposes a framework developed to generalize Critical Heat Flux (CHF) detection classification models using an Unsupervised Image-to-Image (UI2I) translation model. The framework enables a typical classification model that was trained and tested on boiling images from domain A to predict boiling images coming from domain B that was never seen by the classification model. This is done by using the UI2I model to transform the domain B images to look like domain A images that the classification model is familiar with. Although CNN was used as the classification model and Fixed-Point GAN (FP-GAN) was used as the UI2I model, the framework is model agnostic. Meaning, that the framework can generalize any image classification model type, making it applicable to a variety of similar applications and not limited to the boiling crisis detection problem. It also means that the more the UI2I models advance, the better the performance of the framework.

Human organs constantly undergo anatomical changes due to a complex mix of short-term (e.g., heartbeat) and long-term (e.g., aging) factors. Evidently, prior knowledge of these factors will be beneficial when modeling their future state, i.e., via image generation. However, most of the medical image generation tasks only rely on the input from a single image, thus ignoring the sequential dependency even when longitudinal data is available. Sequence-aware deep generative models, where model input is a sequence of ordered and timestamped images, are still underexplored in the medical imaging domain that is featured by several unique challenges: 1) Sequences with various lengths; 2) Missing data or frame, and 3) High dimensionality. To this end, we propose a sequence-aware diffusion model (SADM) for the generation of longitudinal medical images. Recently, diffusion models have shown promising results on high-fidelity image generation. Our method extends this new technique by introducing a sequence-aware transformer as the conditional module in a diffusion model. The novel design enables learning longitudinal dependency even with missing data during training and allows autoregressive generation of a sequence of images during inference. Our extensive experiments on 3D longitudinal medical images demonstrate the effectiveness of SADM compared with baselines and alternative methods.

Word Sense Disambiguation (WSD) is an NLP task aimed at determining the correct sense of a word in a sentence from discrete sense choices. Although current systems have attained unprecedented performances for such tasks, the nonuniform distribution of word senses during training generally results in systems performing poorly on rare senses. To this end, we consider data augmentation to increase the frequency of these least frequent senses (LFS) to reduce the distributional bias of senses during training. We propose Sense-Maintained Sentence Mixup (SMSMix), a novel word-level mixup method that maintains the sense of a target word. SMSMix smoothly blends two sentences using mask prediction while preserving the relevant span determined by saliency scores to maintain a specific word's sense. To the best of our knowledge, this is the first attempt to apply mixup in NLP while preserving the meaning of a specific word. With extensive experiments, we validate that our augmentation method can effectively give more information about rare senses during training with maintained target sense label.