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"abstract": "Despite the success of deep neural networks (DNN) in many applications, their ability to model uncertainty is still significantly limited. For example, in safety-critical applications such as autonomous driving, it is crucial to obtain a prediction that reflects different types of uncertainty to address life-threatening situations appropriately. In such cases, it is essential to be aware of the risk (i.e., aleatoric uncertainty) and the reliability (i.e., epistemic uncertainty) that comes with a prediction. We present AE-DNN, a model allowing the separation of aleatoric and epistemic uncertainty while maintaining a proper generalization capability. AE-DNN is based on deterministic DNN, which can determine the respective uncertainty measures in a single forward pass. In analyses with synthetic and image data, we show that our method improves the modeling of epistemic uncertainty while providing an intuitively understandable separation of risk and reliability.",
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"abstract": "Object detection in autonomous cars is commonly based on camera images and Lidar inputs, which are often used to train prediction models such as deep artificial neural networks for decision making for object recognition, adjusting speed, etc. A mistake in such decision making can be damaging; thus, it is vital to measure the reliability of decisions made by such prediction models via uncertainty measurement. Uncertainty, in deep learning models, is often measured for classification problems. However, deep learning models in autonomous driving are often multi-output regression models. Hence, we propose a novel method called PURE (Prediction sURface uncErtainty) for measuring prediction uncertainty of such regression models. We formulate the object recognition problem as a regression model with more than one outputs for finding object locations in a 2-dimensional camera view. For evaluation, we modified three widely-applied object recognition models (i.e., YoLo, SSD300 and SSD512) and used the KITTI, Stanford Cars, Berkeley DeepDrive, and NEXET datasets. Results showed the statistically significant negative correlation between prediction surface uncertainty and prediction accuracy suggesting that uncertainty significantly impacts the decisions made by autonomous driving.",
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"abstract": "In the remote diagnosis and treatment scenarios of telemedicine, there are various authentication elements that are not credible, and the traditional doctor authentication methods cannot realize the problem of credible authentication. The article combines conventional authentication technology and user portrait technology, and proposes a continuous authentication and abnormal behavior monitoring technology based on doctor user portraits. The article obtains the property labels by analyzing the basic attributes of doctors and the behavior labels by mining the frequent access sequences of doctors using the Prefixspan algorithm, and constructs a digital portrait of doctor users by combining the property labels and behavior labels. The experimental results show that the implementation of continuous identity authentication and abnormal behavior monitoring technology based on the digital portrait of user authentication effectively improves the authentication problem under the condition of untrustworthy authentication elements and enhances the security of the telemedicine service platform.",
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"fullName": "Jing Sun",
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"abstract": "In this paper, we propose a method for semi-automatically creating an anime-like 3D face model from a single illustration. In the proposed method, principal component analysis (PCA) is applied to existing anime-like 3D models to obtain base models for generating natural 3D models. To align the dimensions of the data and make geometric correspondence, a template model is deformed using a modified Nonrigid Iterative Closest Point (NICP) method. Then, the coefficients of the linear combination of the base models are estimated by minimizing the difference between the rendered image of the 3D model with the coefficients and the input illustration using edge-based matching. We confirmed that our method was able to generate a natural anime-like 3D face models which has similar eye and face shapes to those of the input illustration.",
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"normalizedAbstract": "Museums and exhibitions often present physical artefacts which may contain rich histories or deep meaning associated with them. These additional contents are often installed physically as informational panels shown on the wall. However, these may sometimes be challenging to deploy due to space constraints. In order to address this challenge, we introduce the use of mixed reality. Mixed reality offers an immersive and interactive experience through the use of head mounted displays and in-air gestures. Visitors can discover additional content virtually, without changing the physical space. For a small-scale exhibition at a cafe, we developed a Microsoft HoloLens application to create an interactive experience on top of a collection of historic physical items. Through public experiences at the café, we received positive feedback of our system. In this paper, we discuss the design and implications of our system, survey results, as well as challenges that were encountered in deploying our mixed reality experience in a public setting.",
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"affiliation": "The Chinese University of Hong Kong",
"fullName": "Jiageng Mao",
"givenName": "Jiageng",
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"affiliation": "National University of Singapore",
"fullName": "Yujing Xue",
"givenName": "Yujing",
"surname": "Xue",
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"fullName": "Minzhe Niu",
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"fullName": "Haoyue Bai",
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"affiliation": "National University of Singapore",
"fullName": "Jiashi Feng",
"givenName": "Jiashi",
"surname": "Feng",
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"affiliation": "Sun Yat-Sen University",
"fullName": "Xiaodan Liang",
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"surname": "Liang",
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"fullName": "Hang Xu",
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"abstract": "In the domains of Natural Language Processing (NLP) and Computer Vision (CV) Visual Question Answering (VQA) is a multidisciplinary task, in which an image and a question are given to a VQA system, which is responsible for giving the answer. The VQA system is used for a variety of real-world applications, such as providing situational information based on visual material, making judgments using a vast quantity of surveillance data, interacting with robots, and helping individuals who are blind or visually impaired. Although it is required yet challenging to complete comprehensive VQA, Fact-based VQA (FVQA) approaches in which external knowledge is required to process with image and question. Existing FVQA methods combine all types of data without fine-grained selection, thereby generating unexpected noise while reasoning about the final result. The problem solution should be able to collect complementary-information evidence based on question-attention. We represent an image with different layers of information by a multimodal knowledge graph relating to the features of visual, factual, and semantic. We propose a multimodal knowledge graph-convolutional-network (GCN) to collect relevant-information evidence from different information layers based on the given question. In particular, intra-modal knowledge graph attention takes evidence from each modality, while inter-modal knowledge graph attention gets evidence across the different information layers. To get an optimal answer, we stack this process multiple times to perform a reasoning mechanism. Over the FVQA dataset, we achieved state-of-the-art results by improving 10.86% test accuracy, which demonstrates the usefulness and interpretability of our approach.",
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"content": "In the domains of Natural Language Processing (NLP) and Computer Vision (CV) Visual Question Answering (VQA) is a multidisciplinary task, in which an image and a question are given to a VQA system, which is responsible for giving the answer. The VQA system is used for a variety of real-world applications, such as providing situational information based on visual material, making judgments using a vast quantity of surveillance data, interacting with robots, and helping individuals who are blind or visually impaired. Although it is required yet challenging to complete comprehensive VQA, Fact-based VQA (FVQA) approaches in which external knowledge is required to process with image and question. Existing FVQA methods combine all types of data without fine-grained selection, thereby generating unexpected noise while reasoning about the final result. The problem solution should be able to collect complementary-information evidence based on question-attention. We represent an image with different layers of information by a multimodal knowledge graph relating to the features of visual, factual, and semantic. We propose a multimodal knowledge graph-convolutional-network (GCN) to collect relevant-information evidence from different information layers based on the given question. In particular, intra-modal knowledge graph attention takes evidence from each modality, while inter-modal knowledge graph attention gets evidence across the different information layers. To get an optimal answer, we stack this process multiple times to perform a reasoning mechanism. Over the FVQA dataset, we achieved state-of-the-art results by improving 10.86% test accuracy, which demonstrates the usefulness and interpretability of our approach.",
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"abstract": "We propose a tactic of advancing Visual Question Answering (VQA) task by incorporating 3D information via multi-view images. Conventional VQA approaches, which reply an answer in words against a linguistic question about a given RGB image, have less ability to recognize geometrical information so that they tend to fail to count things or guess positional relationship. Moreover, they have no ability to determine blinded space, so it is not feasible to invent VQA function to robots which will work in highly-occluded real-world environments. To achieve the situation, we introduce a new multi-view VQA dataset along with an approach that incorporating 3D scene information directly captured from multi-view images into VQA without using depth images or employing SLAM. Our proposed approach achieves strong performance with an overall accuracy of 95.4% on the challenging multi-view VQA dataset setup, which contains relatively severe occlusion. This work also demonstrates the promising aspects of bridging the gap between 3D vision and language.",
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"abstract": "Limited by the incomprehensive training samples, existing scene graph generation (SGG) methods perform poorly on predicting zero-shot (i.e., unseen) subject-predicate-object triples. To address this problem, we propose a general SGG framework to improve their zero-shot performance. The main idea of our method is to generate the information of zero-shot triples before the training of the predicate classifier and thus make the original zero-shot triples non-zero-shot. Specifically, the missing information of zero-shot triples is generated by our proposed knowledge graph completion strategy and then integrated with visual features of images. Therefore, the predicate classification of zero-shot triples is no longer just regarded as a single visual classification task but also transformed into a prediction task of missing links in a knowledge graph. The experiments on the dataset Visual Genome demonstrate that our proposed method outperforms the state-of-the-art methods in popular zero-shot metrics (i.e., zR@N, ng-zR@N) for all popular SGG tasks.",
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"normalizedAbstract": "Scene graph generation (SGG) aims to capture a wide variety of interactions between pairs of objects, which is essential for full scene understanding. Existing SGG methods trained on the entire set of relations fail to acquire complex reasoning about visual and textual correlations due to various biases in training data. Learning on trivial relations that indicate generic spatial configuration like ‘on’ instead of informative relations such as ‘parked on’ does not enforce this complex reasoning, harming generalization. To address this problem, we propose a novel framework for SGG training that exploits relation labels based on their informativeness. Our model-agnostic training procedure imputes missing informative relations for less informative samples in the training data and trains a SGG model on the imputed labels along with existing annotations. We show that this approach can successfully be used in conjunction with state-of-the-art SGG methods and improves their performance significantly in multiple metrics on the standard Visual Genome benchmark. Furthermore, we obtain considerable improvements for unseen triplets in a more challenging zero-shot setting.",
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"content": "Scene graph generation (SGG) aims to detect objects and predict their pairwise relationships within an image. Current SGG methods typically utilize graph neural net-works (GNNs) to acquire context information between ob-jects/relationships. Despite their effectiveness, however, current SGG methods only assume scene graph homophily while ignoring heterophily. Accordingly, in this paper, we propose a novel Heterophily Learning Network (HL-Net) to comprehensively explore the homophily and heterophily be-tween objects/relationships in scene graphs. More specif-ically, HL-Net comprises the following 1) an adaptive reweighting transformer module, which adaptively inte-grates the information from different layers to exploit both the heterophily and homophily in objects; 2) a relation-ship feature propagation module that efficiently explores the connections between relationships by considering het-erophily in order to refine the relationship representation; 3) a heterophily-aware message-passing scheme to fur-ther distinguish the heterophily and homophily between ob-jects/relationships, thereby facilitating improved message passing in graphs. We conducted extensive experiments on two public datasets: Visual Genome (VG) and Open Images (OI). The experimental results demonstrate the superiority of our proposed HL-Net over existing state-of-the-art approaches. In more detail, HL-Net outperforms the second-best competitors by 2.1% on the VG datasetfor scene graph classification and 1.2% on the IO dataset for the final score. Code is available at https://github.com/simI3/HL-Net.",
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"abstract": "Existing scene graph generation (SGG) methods are far from practical, primarily due to their poor performance on predicting zero-shot (i.e., unseen) subject-predicate-object triples. We observe that these SGG methods treat images along with the triples in them independently and thus fail to consider the complex and hidden information that is inherently implicit in the triples of other images. To this effect, our paper proposes a novel encoder-decoder SGG framework to leverage the semantic correlations between the triples of different images into the prediction of a zero-shot triple. Specifically, the encoder aggregates the triples in each image of training set into a large knowledge graph and learns the entity embeddings that capture the features of their neighborhoods with a relational graph neural network. The neighborhood-aware embeddings are then fed into the vision-based decoder to predict the predicates in images. Extensive experiments on the popular benchmark Visual Genome demonstrate that our proposed method outperforms the state-of-the-art methods in popular zero-shot metrics (i.e., zR@N, ngzR@N) for all SGG tasks.",
"abstracts": [
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"content": "Existing scene graph generation (SGG) methods are far from practical, primarily due to their poor performance on predicting zero-shot (i.e., unseen) subject-predicate-object triples. We observe that these SGG methods treat images along with the triples in them independently and thus fail to consider the complex and hidden information that is inherently implicit in the triples of other images. To this effect, our paper proposes a novel encoder-decoder SGG framework to leverage the semantic correlations between the triples of different images into the prediction of a zero-shot triple. Specifically, the encoder aggregates the triples in each image of training set into a large knowledge graph and learns the entity embeddings that capture the features of their neighborhoods with a relational graph neural network. The neighborhood-aware embeddings are then fed into the vision-based decoder to predict the predicates in images. Extensive experiments on the popular benchmark Visual Genome demonstrate that our proposed method outperforms the state-of-the-art methods in popular zero-shot metrics (i.e., zR@N, ngzR@N) for all SGG tasks.",
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"abstract": "Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e.g., collapsing diverse \"human walk on / sit on / lay on beach\" into \"human on beach\". Given such SGG, the down-stream tasks such as VQA can hardly infer better scene structures than merely a bag of objects. However, debiasing in SGG is not trivial because traditional debiasing methods cannot distinguish between the good and bad bias, e.g., good context prior (e.g., \"person read book\" rather than \"eat\") and bad long-tailed bias (e.g., \"near\" dominating \"behind / in front of\"). In this paper, we present a novel SGG framework based on causal inference but not the conventional likelihood. We first build a causal graph for SGG, and perform traditional biased training with the graph. Then, we propose to draw the counterfactual causality from the trained graph to infer the effect from the bad bias, which should be removed. In particular, we use Total Direct Effect (TDE) as the proposed final predicate score for unbiased SGG. Note that our framework is agnostic to any SGG model and thus can be widely applied in the community who seeks unbiased predictions. By using the proposed Scene Graph Diagnosis toolkit on the SGG benchmark Visual Genome and several prevailing models, we observed significant improvements over the previous state-of-the-art methods.",
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"content": "Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e.g., collapsing diverse \"human walk on / sit on / lay on beach\" into \"human on beach\". Given such SGG, the down-stream tasks such as VQA can hardly infer better scene structures than merely a bag of objects. However, debiasing in SGG is not trivial because traditional debiasing methods cannot distinguish between the good and bad bias, e.g., good context prior (e.g., \"person read book\" rather than \"eat\") and bad long-tailed bias (e.g., \"near\" dominating \"behind / in front of\"). In this paper, we present a novel SGG framework based on causal inference but not the conventional likelihood. We first build a causal graph for SGG, and perform traditional biased training with the graph. Then, we propose to draw the counterfactual causality from the trained graph to infer the effect from the bad bias, which should be removed. In particular, we use Total Direct Effect (TDE) as the proposed final predicate score for unbiased SGG. Note that our framework is agnostic to any SGG model and thus can be widely applied in the community who seeks unbiased predictions. By using the proposed Scene Graph Diagnosis toolkit on the SGG benchmark Visual Genome and several prevailing models, we observed significant improvements over the previous state-of-the-art methods.",
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"abstract": "Abstract: This paper presents a new approach to document categorization and retrieval using Growing Cell Structures. Semantic microfeatures together with other information are adopted for document representation. The main advantage over traditional information retrieval systems which adopt index terms to index and retrieve documents is that the new approach considers the semantic relationships among documents. This paper describes how to construct Document Microfeature Weight Vectors and use Growing Cell Structures clustering them. Some experimental results are also presented by this paper.",
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"abstract": "The legal system is one of the most important pillars of human society. While digitization is integrated into many areas of everyday life, the legal system is still very traditionally positioned. Recent advances in storing and processing large number of documents initiated the work of intelligent legal advisors. While first approaches in the English language suggest an enormous potential to generate knowledge from legal documents, there are no approaches in the German legal system. We present an intelligent legal advisor based on semantic document retrieval, to improve knowledge extraction from German legal documents. In addition, we set up a question-answering system. We implemented a BERT and a BM25-model for German document retrieval in legal documents. The approach is validated on a data set consisting of German question-answer pairs.",
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"content": "The legal system is one of the most important pillars of human society. While digitization is integrated into many areas of everyday life, the legal system is still very traditionally positioned. Recent advances in storing and processing large number of documents initiated the work of intelligent legal advisors. While first approaches in the English language suggest an enormous potential to generate knowledge from legal documents, there are no approaches in the German legal system. We present an intelligent legal advisor based on semantic document retrieval, to improve knowledge extraction from German legal documents. In addition, we set up a question-answering system. We implemented a BERT and a BM25-model for German document retrieval in legal documents. The approach is validated on a data set consisting of German question-answer pairs.",
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"title": "2022 IEEE International Conference on Agents (ICA)",
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"title": "GORITE: A BDI Realisation of Behavior Trees",
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"abstract": "Behavior trees are becoming increasingly used in both the computer games and robotics industries as a mechanism for non-programmers to specify individual entity (non-playing character or robot) behaviors. The tree representation that is employed is amenable to graphical representation and the availability of graphical editors has contributed significantly to the uptake of the approach. Working with graphical representations of behavior makes development by non-programmers easier. However, existing behavior tree representations are limited in their ability to represent team behavior and complex entity reasoning. Agent frameworks exist that address these issues and while they have been successfully employed in military war gaming applications, the underlying behavior representation that they use are fundamentally different to behavior trees. In this paper, we introduce GORITE, a BDI agent framework in which agent behavior is specified using goal-based process models which are representationally similar to behavior trees. Unlike behavior trees, process models can be used to represent both individual and team behaviors. Furthermore, GORITE provides support to enable an agent (or team of agents) to reason about the goals that it intends to pursue as well as the goals that it is currently pursuing. A simple example is used to demonstrate how team behavior can be modelled using GORITE process models.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Behavior trees are becoming increasingly used in both the computer games and robotics industries as a mechanism for non-programmers to specify individual entity (non-playing character or robot) behaviors. The tree representation that is employed is amenable to graphical representation and the availability of graphical editors has contributed significantly to the uptake of the approach. Working with graphical representations of behavior makes development by non-programmers easier. However, existing behavior tree representations are limited in their ability to represent team behavior and complex entity reasoning. Agent frameworks exist that address these issues and while they have been successfully employed in military war gaming applications, the underlying behavior representation that they use are fundamentally different to behavior trees. In this paper, we introduce GORITE, a BDI agent framework in which agent behavior is specified using goal-based process models which are representationally similar to behavior trees. Unlike behavior trees, process models can be used to represent both individual and team behaviors. Furthermore, GORITE provides support to enable an agent (or team of agents) to reason about the goals that it intends to pursue as well as the goals that it is currently pursuing. A simple example is used to demonstrate how team behavior can be modelled using GORITE process models.",
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"fno": "693600a006",
"keywords": [
"Computer Games",
"Control Engineering Computing",
"Military Computing",
"Multi Agent Systems",
"Robots",
"Software Agents",
"Trees Mathematics",
"Agent Behavior",
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],
"authors": [
{
"affiliation": "University of South Australia,UniSA STEM,Adelaide,Australia",
"fullName": "Lui Cirocco",
"givenName": "Lui",
"surname": "Cirocco",
"__typename": "ArticleAuthorType"
},
{
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"title": "Exploring Adversarial Attacks on Neural Networks: An Explainable Approach",
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"abstract": "Deep Learning (DL) is being applied in various domains, especially in safety-critical applications such as autonomous driving. Consequently, it is of great significance to ensure the robustness of these methods and thus counteract uncertain behaviors caused by adversarial attacks. In this paper, we use gradient heatmaps to analyze the response characteristics of the VGG-16 model when the input images are mixed with adversarial noise and statistically similar Gaussian random noise. In particular, we compare the network response layer by layer to determine where errors occurred. Several interesting findings are derived. First, compared to Gaussian random noise, intentionally generated adversarial noise causes severe behavior deviation by distracting the area of concentration in the networks. Second, in many cases, adversarial examples only need to compromise a few intermediate blocks to mislead the final decision. Third, our experiments revealed that specific blocks are more vulnerable and easier to exploit by adversarial examples. Finally, we demonstrate that the layers Block4_conv1 and Block5_ cov1 of the VGG-16 model are more susceptible to adversarial attacks. Our work could potentially provide useful insights into developing more reliable Deep Neural Network (DNN) models.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Deep Learning (DL) is being applied in various domains, especially in safety-critical applications such as autonomous driving. Consequently, it is of great significance to ensure the robustness of these methods and thus counteract uncertain behaviors caused by adversarial attacks. In this paper, we use gradient heatmaps to analyze the response characteristics of the VGG-16 model when the input images are mixed with adversarial noise and statistically similar Gaussian random noise. In particular, we compare the network response layer by layer to determine where errors occurred. Several interesting findings are derived. First, compared to Gaussian random noise, intentionally generated adversarial noise causes severe behavior deviation by distracting the area of concentration in the networks. Second, in many cases, adversarial examples only need to compromise a few intermediate blocks to mislead the final decision. Third, our experiments revealed that specific blocks are more vulnerable and easier to exploit by adversarial examples. Finally, we demonstrate that the layers Block4_conv1 and Block5_ cov1 of the VGG-16 model are more susceptible to adversarial attacks. Our work could potentially provide useful insights into developing more reliable Deep Neural Network (DNN) models.",
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"authors": [
{
"affiliation": "Embry-Riddle Aeronautical University,FL 32114,USA",
"fullName": "Justus Renkhoff",
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"fullName": "Wenkai Tan",
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"affiliation": "University of Colorado Boulder,CO 80309,USA",
"fullName": "Alvaro Velasquez",
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"affiliation": "Purdue University,IN 47907,USA",
"fullName": "William Yichen Wang",
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"affiliation": "Embry-Riddle Aeronautical University,FL 32114,USA",
"fullName": "Yongxin Liu",
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"affiliation": "University of Tennessee at Martin,TN 38237,USA",
"fullName": "Jian Wang",
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"affiliation": "Bowling Green State University,OH 43403,USA",
"fullName": "Shuteng Niu",
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"affiliation": "Trier University of Applied Sciences,Germany",
"fullName": "Lejla Begic Fazlic",
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"abstract": "The functional difference between a diffuse wall and a mirror is well understood: one scatters back into all directions, and the other one preserves the directionality of reflected light. The temporal structure of the light, however, is left intact by both: assuming simple surface reflection, photons that arrive first are reflected first. In this paper, we exploit this insight to recover objects outside the line of sight from second-order diffuse reflections, effectively turning walls into mirrors. We formulate the reconstruction task as a linear inverse problem on the transient response of a scene, which we acquire using an affordable setup consisting of a modulated light source and a time-of-flight image sensor. By exploiting sparsity in the reconstruction domain, we achieve resolutions in the order of a few centimeters for object shape (depth and laterally) and albedo. Our method is robust to ambient light and works for large room-sized scenes. It is drastically faster and less expensive than previous approaches using femtosecond lasers and streak cameras, and does not require any moving parts.",
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"normalizedAbstract": "Standard presentation systems consisting of a laptop connected to a projector suffer from two problems: (1) the projected image appears distorted (keystoned) unless the projector is precisely aligned to the projection screen; (2) the speaker is forced to interact with the computer rather than the audience. This paper shows how the addition of an uncalibrated camera, aimed at the screen, solves both problems. Although the locations, orientations and optical parameters of the camera and projector are unknown, the projector-camera system calibrates itself by exploiting the homography between the projected slide and the camera image. Significant improvements are possible over passively calibrating systems since the projector actively manipulates the environment by placing feature points into the scene. For instance, using a low-resolution (160/spl times/120) camera, we can achieve an accuracy of /spl plusmn/3 pixels in a 1024/spl times/768 presentation slide. The camera-projector system infers models for the projector-to-camera and projector-to-screen mappings in order to provide two major benefits.",
"fno": "00937525",
"keywords": [
"Technical Presentation",
"Business Graphics",
"Smarter Presentations",
"Homography",
"Camera Projector Systems",
"Orientations",
"Optical Parameters",
"Projector Camera System",
"Optical Distortion",
"Cameras",
"Portable Computers",
"Mice",
"Robots",
"Layout",
"Geometry",
"Hardware",
"Java",
"Application Software"
],
"authors": [
{
"affiliation": "Just Res., Pittsburgh, PA, USA",
"fullName": "R. Sukthankar",
"givenName": "R.",
"surname": "Sukthankar",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "R.G. Stockton",
"givenName": "R.G.",
"surname": "Stockton",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "M.D. Mullin",
"givenName": "M.D.",
"surname": "Mullin",
"__typename": "ArticleAuthorType"
}
],
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"pubDate": "2001-01-01T00:00:00",
"pubType": "proceedings",
"pages": "247,248,249,250,251,252,253",
"year": "2001",
"issn": null,
"isbn": null,
"notes": null,
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