arXiv subjects

Yujia Zhang

Publications and source records attributed to Yujia Zhang.

67 records · Page 4Linked to original sources

arXiv2021

Template-based Chatbot for Agriculture Related FAQs

Agriculture is the fundamental industry of the society, which is the basis of food supply and an important source of employment and GDP increase. However, the insufficient expert can not fulfill the demand of farmers. To address this problem, we design a chatbot to answer frequently asked questions in the Agriculture field. Template-based questions will be answered by AIML while LSA is used for other service-based questions. This chatbot will assist farmers by dealing with industry problems conveniently and efficiently.

cs.AI↗

arXiv2020

Group Testing Enables Asymptomatic Screening for COVID-19 Mitigation: Feasibility and Optimal Pool Size Selection with Dilution Effects

Repeated asymptomatic screening for SARS-CoV-2 promises to control spread of the virus but would require too many resources to implement at scale. Group testing is promising for screening more people with fewer test resources: multiple samples tested together in one pool can be excluded with one negative test result. Existing approaches to group testing design for SARS-CoV-2 asymptomatic screening, however, do not consider dilution effects: that false negatives become more common with larger pools. As a consequence, they may recommend pool sizes that are too large or misestimate the benefits of screening. Modeling dilution effects, we derive closed-form expressions for the expected number of tests and false negative/positives per person screened under two popular group testing methods: the linear and square array methods. We find that test error correlation induced by a common viral load across an individual's samples results in many fewer false negatives than would be expected from less realistic but more widely assumed independent errors. This insight also suggests that false positives can be controlled through repeated tests without significantly increasing false negatives. Using these closed-form expressions to trace a Pareto frontier over error rates and tests, we design testing protocols for repeated asymptomatic screening of a large population. We minimize disease prevalence by optimizing a time-varying pool sizes and screening frequency constrained by daily test capacity and a false positive limit. This provides a testing protocol practitioners can use for mitigating COVID-19. In a case study, we demonstrate the effectiveness of this methodology in controlling spread.

q-bio.PE↗

arXiv2020

TB-Net: A Three-Stream Boundary-Aware Network for Fine-Grained Pavement Disease Segmentation

Regular pavement inspection plays a significant role in road maintenance for safety assurance. Existing methods mainly address the tasks of crack detection and segmentation that are only tailored for long-thin crack disease. However, there are many other types of diseases with a wider variety of sizes and patterns that are also essential to segment in practice, bringing more challenges towards fine-grained pavement inspection. In this paper, our goal is not only to automatically segment cracks, but also to segment other complex pavement diseases as well as typical landmarks (markings, runway lights, etc.) and commonly seen water/oil stains in a single model. To this end, we propose a three-stream boundary-aware network (TB-Net). It consists of three streams fusing the low-level spatial and the high-level contextual representations as well as the detailed boundary information. Specifically, the spatial stream captures rich spatial features. The context stream, where an attention mechanism is utilized, models the contextual relationships over local features. The boundary stream learns detailed boundaries using a global-gated convolution to further refine the segmentation outputs. The network is trained using a dual-task loss in an end-to-end manner, and experiments on a newly collected fine-grained pavement disease dataset show the effectiveness of our TB-Net.

cs.CV↗

arXiv2020

A Generalized Zero-Shot Framework for Emotion Recognition from Body Gestures

Although automatic emotion recognition from facial expressions and speech has made remarkable progress, emotion recognition from body gestures has not been thoroughly explored. People often use a variety of body language to express emotions, and it is difficult to enumerate all emotional body gestures and collect enough samples for each category. Therefore, recognizing new emotional body gestures is critical for better understanding human emotions. However, the existing methods fail to accurately determine which emotional state a new body gesture belongs to. In order to solve this problem, we introduce a Generalized Zero-Shot Learning (GZSL) framework, which consists of three branches to infer the emotional state of the new body gestures with only their semantic descriptions. The first branch is a Prototype-Based Detector (PBD) which is used to determine whether an sample belongs to a seen body gesture category and obtain the prediction results of the samples from the seen categories. The second branch is a Stacked AutoEncoder (StAE) with manifold regularization, which utilizes semantic representations to predict samples from unseen categories. Note that both of the above branches are for body gesture recognition. We further add an emotion classifier with a softmax layer as the third branch in order to better learn the feature representations for this emotion classification task. The input features for these three branches are learned by a shared feature extraction network, i.e., a Bidirectional Long Short-Term Memory Networks (BLSTM) with a self-attention module. We treat these three branches as subtasks and use multi-task learning strategies for joint training. The performance of our framework on an emotion recognition dataset is significantly superior to the traditional method of emotion classification and state-of-the-art zero-shot learning methods.

cs.CV↗

arXiv2020

A Prototype-Based Generalized Zero-Shot Learning Framework for Hand Gesture Recognition

Hand gesture recognition plays a significant role in human-computer interaction for understanding various human gestures and their intent. However, most prior works can only recognize gestures of limited labeled classes and fail to adapt to new categories. The task of Generalized Zero-Shot Learning (GZSL) for hand gesture recognition aims to address the above issue by leveraging semantic representations and detecting both seen and unseen class samples. In this paper, we propose an end-to-end prototype-based GZSL framework for hand gesture recognition which consists of two branches. The first branch is a prototype-based detector that learns gesture representations and determines whether an input sample belongs to a seen or unseen category. The second branch is a zero-shot label predictor which takes the features of unseen classes as input and outputs predictions through a learned mapping mechanism between the feature and the semantic space. We further establish a hand gesture dataset that specifically targets this GZSL task, and comprehensive experiments on this dataset demonstrate the effectiveness of our proposed approach on recognizing both seen and unseen gestures.

cs.CV↗

arXiv2019

The Mobile AR Sensor Logger for Android and iOS Devices

In recent years, commodity mobile devices equipped with cameras and inertial measurement units (IMUs) have attracted much research and design effort for augmented reality (AR) and robotics applications. Based on such sensors, many commercial AR toolkits and public benchmark datasets have been made available to accelerate hatching and validating new ideas. To lower the difficulty and enhance the flexibility in accessing the rich raw data of typical AR sensors on mobile devices, this paper present the mobile AR sensor (MARS) logger for two of the most popular mobile operating systems, Android and iOS. The logger highlights the best possible synchronization between the camera and the IMU allowed by a mobile device, and efficient saving of images at about 30Hz, and recording the metadata relevant to AR applications. This logger has been tested on a relatively large spectrum of mobile devices, and the collected data has been used for analyzing the sensor characteristics. We see that this application will facilitate research and development related to AR and robotics, so it has been open sourced at https://github.com/OSUPCVLab/mobile-ar-sensor-logger.

cs.HC↗

arXiv2019

Dilated Temporal Relational Adversarial Network for Generic Video Summarization

The large amount of videos popping up every day, make it more and more critical that key information within videos can be extracted and understood in a very short time. Video summarization, the task of finding the smallest subset of frames, which still conveys the whole story of a given video, is thus of great significance to improve efficiency of video understanding. We propose a novel Dilated Temporal Relational Generative Adversarial Network (DTR-GAN) to achieve frame-level video summarization. Given a video, it selects the set of key frames, which contain the most meaningful and compact information. Specifically, DTR-GAN learns a dilated temporal relational generator and a discriminator with three-player loss in an adversarial manner. A new dilated temporal relation (DTR) unit is introduced to enhance temporal representation capturing. The generator uses this unit to effectively exploit global multi-scale temporal context to select key frames and to complement the commonly used Bi-LSTM. To ensure that summaries capture enough key video representation from a global perspective rather than a trivial randomly shorten sequence, we present a discriminator that learns to enforce both the information completeness and compactness of summaries via a three-player loss. The loss includes the generated summary loss, the random summary loss, and the real summary (ground-truth) loss, which play important roles for better regularizing the learned model to obtain useful summaries. Comprehensive experiments on three public datasets show the effectiveness of the proposed approach.

cs.CV↗

arXiv2019

"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations

Methods for interpreting machine learning black-box models increase the outcomes' transparency and in turn generates insight into the reliability and fairness of the algorithms. However, the interpretations themselves could contain significant uncertainty that undermines the trust in the outcomes and raises concern about the model's reliability. Focusing on the method "Local Interpretable Model-agnostic Explanations" (LIME), we demonstrate the presence of two sources of uncertainty, namely the randomness in its sampling procedure and the variation of interpretation quality across different input data points. Such uncertainty is present even in models with high training and test accuracy. We apply LIME to synthetic data and two public data sets, text classification in 20 Newsgroup and recidivism risk-scoring in COMPAS, to support our argument.

cs.LG↗

arXiv2019

Rethinking Knowledge Graph Propagation for Zero-Shot Learning

Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning. These models are highly sample efficient as related concepts in the graph structure share statistical strength allowing generalization to new classes when faced with a lack of data. However, multi-layer architectures, which are required to propagate knowledge to distant nodes in the graph, dilute the knowledge by performing extensive Laplacian smoothing at each layer and thereby consequently decrease performance. In order to still enjoy the benefit brought by the graph structure while preventing dilution of knowledge from distant nodes, we propose a Dense Graph Propagation (DGP) module with carefully designed direct links among distant nodes. DGP allows us to exploit the hierarchical graph structure of the knowledge graph through additional connections. These connections are added based on a node's relationship to its ancestors and descendants. A weighting scheme is further used to weigh their contribution depending on the distance to the node to improve information propagation in the graph. Combined with finetuning of the representations in a two-stage training approach our method outperforms state-of-the-art zero-shot learning approaches.

cs.CV↗

arXiv2019

ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation

Salient segmentation aims to segment out attention-grabbing regions, a critical yet challenging task and the foundation of many high-level computer vision applications. It requires semantic-aware grouping of pixels into salient regions and benefits from the utilization of global multi-scale contexts to achieve good local reasoning. Previous works often address it as two-class segmentation problems utilizing complicated multi-step procedures including refinement networks and complex graphical models. We argue that semantic salient segmentation can instead be effectively resolved by reformulating it as a simple yet intuitive pixel-pair based connectivity prediction task. Following the intuition that salient objects can be naturally grouped via semantic-aware connectivity between neighboring pixels, we propose a pure Connectivity Net (ConnNet). ConnNet predicts connectivity probabilities of each pixel with its neighboring pixels by leveraging multi-level cascade contexts embedded in the image and long-range pixel relations. We investigate our approach on two tasks, namely salient object segmentation and salient instance-level segmentation, and illustrate that consistent improvements can be obtained by modeling these tasks as connectivity instead of binary segmentation tasks for a variety of network architectures. We achieve state-of-the-art performance, outperforming or being comparable to existing approaches while reducing inference time due to our less complex approach.

cs.CV↗

arXiv2018

Unsupervised Object-Level Video Summarization with Online Motion Auto-Encoder

Unsupervised video summarization plays an important role on digesting, browsing, and searching the ever-growing videos every day, and the underlying fine-grained semantic and motion information (i.e., objects of interest and their key motions) in online videos has been barely touched. In this paper, we investigate a pioneer research direction towards the fine-grained unsupervised object-level video summarization. It can be distinguished from existing pipelines in two aspects: extracting key motions of participated objects, and learning to summarize in an unsupervised and online manner. To achieve this goal, we propose a novel online motion Auto-Encoder (online motion-AE) framework that functions on the super-segmented object motion clips. Comprehensive experiments on a newly-collected surveillance dataset and public datasets have demonstrated the effectiveness of our proposed method.

cs.CV↗

arXiv2018

Query-Conditioned Three-Player Adversarial Network for Video Summarization

Video summarization plays an important role in video understanding by selecting key frames/shots. Traditionally, it aims to find the most representative and diverse contents in a video as short summaries. Recently, a more generalized task, query-conditioned video summarization, has been introduced, which takes user queries into consideration to learn more user-oriented summaries. In this paper, we propose a query-conditioned three-player generative adversarial network to tackle this challenge. The generator learns the joint representation of the user query and the video content, and the discriminator takes three pairs of query-conditioned summaries as the input to discriminate the real summary from a generated and a random one. A three-player loss is introduced for joint training of the generator and the discriminator, which forces the generator to learn better summary results, and avoids the generation of random trivial summaries. Experiments on a recently proposed query-conditioned video summarization benchmark dataset show the efficiency and efficacy of our proposed method.

cs.CV↗

arXiv2015

Pileup Subtraction and Jet Energy Prediction Using Machine Learning

In the Large Hardron Collider (LHC), multiple proton-proton collisions cause pileup in reconstructing energy information for a single primary collision (jet). This project aims to select the most important features and create a model to accurately estimate jet energy. Different machine learning methods were explored, including linear regression, support vector regression and decision tree. The best result is obtained by linear regression with predictive features and the performance is improved significantly from the baseline method.

hep-ph↗