pwc-archive/papers-with-abstracts huggingface dataset:
Stay Positive: Knowledge Graph Embedding Without Negative Sampling
Authors: Ainaz Hajimoradlou, Mehran Kazemi
Date: 2022-01-07 00:00:00
Tasks: Binary Classification, Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs

Knowledge graphs (KGs) are typically incomplete and we often wish to infer new facts given the existing ones. This can be thought of as a binary classification problem; we aim to predict if new facts are true or false. Unfortunately, we generally only have positive examples (the known facts) but we also need negative ones to train a classifier. To resolve this, it is usual to generate negative examples using a negative sampling strategy. However, this can produce false negatives which may reduce performance, is computationally expensive, and does not produce calibrated classification probabilities. In this paper, we propose a training procedure that obviates the need for negative sampling by adding a novel regularization term to the loss function. Our results for two relational embedding models (DistMult and SimplE) show the merit of our proposal both in terms of performance and speed.
United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI
Authors: Jianfeng Zhao, Dengwang Li, Shuo Li
Date: 2022-01-07 00:00:00
Tasks: feature selection, Segmentation, Tumor Segmentation

Simultaneous segmentation and detection of liver tumors (hemangioma and hepatocellular carcinoma (HCC)) by using multi-modality non-contrast magnetic resonance imaging (NCMRI) are crucial for the clinical diagnosis. However, it is still a challenging task due to: (1) the HCC information on NCMRI is invisible or insufficient makes extraction of liver tumors feature difficult; (2) diverse imaging characteristics in multi-modality NCMRI causes feature fusion and selection difficult; (3) no specific information between hemangioma and HCC on NCMRI cause liver tumors detection difficult. In this study, we propose a united adversarial learning framework (UAL) for simultaneous liver tumors segmentation and detection using multi-modality NCMRI. The UAL first utilizes a multi-view aware encoder to extract multi-modality NCMRI information for liver tumor segmentation and detection. In this encoder, a novel edge dissimilarity feature pyramid module is designed to facilitate the complementary multi-modality feature extraction. Second, the newly designed fusion and selection channel is used to fuse the multi-modality feature and make the decision of the feature selection. Then, the proposed mechanism of coordinate sharing with padding integrates the multi-task of segmentation and detection so that it enables multi-task to perform united adversarial learning in one discriminator. Lastly, an innovative multi-phase radiomics guided discriminator exploits the clear and specific tumor information to improve the multi-task performance via the adversarial learning strategy. The UAL is validated in corresponding multi-modality NCMRI (i.e. T1FS pre-contrast MRI, T2FS MRI, and DWI) and three phases contrast-enhanced MRI of 255 clinical subjects. The experiments show that UAL has great potential in the clinical diagnosis of liver tumors.
Attention Option-Critic
Authors: Raviteja Chunduru, Doina Precup
Date: 2022-01-07 00:00:00
Proceeding: ICML Workshop LifelongML 2020 7
Tasks: Atari Games, Transfer Learning

Temporal abstraction in reinforcement learning is the ability of an agent to learn and use high-level behaviors, called options. The option-critic architecture provides a gradient-based end-to-end learning method to construct options. We propose an attention-based extension to this framework, which enables the agent to learn to focus different options on different aspects of the observation space. We show that this leads to behaviorally diverse options which are also capable of state abstraction, and prevents the degeneracy problems of option domination and frequent option switching that occur in option-critic, while achieving a similar sample complexity. We also demonstrate the more efficient, interpretable, and reusable nature of the learned options in comparison with option-critic, through different transfer learning tasks. Experimental results in a relatively simple four-rooms environment and the more complex ALE (Arcade Learning Environment) showcase the efficacy of our approach.
Probabilistic spatial clustering based on the Self Discipline Learning (SDL) model of autonomous learning
Authors: Zecang Gu, Xiaoqi Sun, Yuan Sun, Fuquan Zhang
Date: 2022-01-07 00:00:00
Tasks: Clustering

Unsupervised clustering algorithm can effectively reduce the dimension of high-dimensional unlabeled data, thus reducing the time and space complexity of data processing. However, the traditional clustering algorithm needs to set the upper bound of the number of categories in advance, and the deep learning clustering algorithm will fall into the problem of local optimum. In order to solve these problems, a probabilistic spatial clustering algorithm based on the Self Discipline Learning(SDL) model is proposed. The algorithm is based on the Gaussian probability distribution of the probability space distance between vectors, and uses the probability scale and maximum probability value of the probability space distance as the distance measurement judgment, and then determines the category of each sample according to the distribution characteristics of the data set itself. The algorithm is tested in Laboratory for Intelligent and Safe Automobiles(LISA) traffic light data set, the accuracy rate is 99.03%, the recall rate is 91%, and the effect is achieved.
Spatio-Temporal Graph Representation Learning for Fraudster Group Detection
Authors: Saeedreza Shehnepoor, Roberto Togneri, Wei Liu, Mohammed Bennamoun
Date: 2022-01-07 00:00:00
Tasks: Graph Representation Learning, Representation Learning

Motivated by potential financial gain, companies may hire fraudster groups to write fake reviews to either demote competitors or promote their own businesses. Such groups are considerably more successful in misleading customers, as people are more likely to be influenced by the opinion of a large group. To detect such groups, a common model is to represent fraudster groups' static networks, consequently overlooking the longitudinal behavior of a reviewer thus the dynamics of co-review relations among reviewers in a group. Hence, these approaches are incapable of excluding outlier reviewers, which are fraudsters intentionally camouflaging themselves in a group and genuine reviewers happen to co-review in fraudster groups. To address this issue, in this work, we propose to first capitalize on the effectiveness of the HIN-RNN in both reviewers' representation learning while capturing the collaboration between reviewers, we first utilize the HIN-RNN to model the co-review relations of reviewers in a group in a fixed time window of 28 days. We refer to this as spatial relation learning representation to signify the generalisability of this work to other networked scenarios. Then we use an RNN on the spatial relations to predict the spatio-temporal relations of reviewers in the group. In the third step, a Graph Convolution Network (GCN) refines the reviewers' vector representations using these predicted relations. These refined representations are then used to remove outlier reviewers. The average of the remaining reviewers' representation is then fed to a simple fully connected layer to predict if the group is a fraudster group or not. Exhaustive experiments of the proposed approach showed a 5% (4%), 12% (5%), 12% (5%) improvement over three of the most recent approaches on precision, recall, and F1-value over the Yelp (Amazon) dataset, respectively.
Posture Prediction for Healthy Sitting using a Smart Chair
Authors: Tariku Adane Gelaw, Misgina Tsighe Hagos
Date: 2022-01-05 00:00:00
Tasks: Prediction

Poor sitting habits have been identified as a risk factor to musculoskeletal disorders and lower back pain especially on the elderly, disabled people, and office workers. In the current computerized world, even while involved in leisure or work activity, people tend to spend most of their days sitting at computer desks. This can result in spinal pain and related problems. Therefore, a means to remind people about their sitting habits and provide recommendations to counterbalance, such as physical exercise, is important. Posture recognition for seated postures have not received enough attention as most works focus on standing postures. Wearable sensors, pressure or force sensors, videos and images were used for posture recognition in the literature. The aim of this study is to build Machine Learning models for classifying sitting posture of a person by analyzing data collected from a chair platted with two 32 by 32 pressure sensors at its seat and backrest. Models were built using five algorithms: Random Forest (RF), Gaussian Na\"ive Bayes, Logistic Regression, Support Vector Machine and Deep Neural Network (DNN). All the models are evaluated using KFold cross-validation technique. This paper presents experiments conducted using the two separate datasets, controlled and realistic, and discusses results achieved at classifying six sitting postures. Average classification accuracies of 98% and 97% were achieved on the controlled and realistic datasets, respectively.
C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System
Authors: Yuanhang Zhou, Kun Zhou, Wayne Xin Zhao, Cheng Wang, Peng Jiang, He Hu
Date: 2022-01-04 00:00:00
Tasks: Contrastive Learning, Recommendation Systems, Text Generation, Vocal Bursts Type Prediction

Conversational recommender systems (CRS) aim to recommend suitable items to users through natural language conversations. For developing effective CRSs, a major technical issue is how to accurately infer user preference from very limited conversation context. To address issue, a promising solution is to incorporate external data for enriching the context information. However, prior studies mainly focus on designing fusion models tailored for some specific type of external data, which is not general to model and utilize multi-type external data. To effectively leverage multi-type external data, we propose a novel coarse-to-fine contrastive learning framework to improve data semantic fusion for CRS. In our approach, we first extract and represent multi-grained semantic units from different data signals, and then align the associated multi-type semantic units in a coarse-to-fine way. To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarse-grained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion. Such an approach can be extended to incorporate more kinds of external data. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach in both recommendation and conversation tasks.
Building Human-like Communicative Intelligence: A Grounded Perspective
Authors: Marina Dubova
Date: 2022-01-02 00:00:00
Tasks: Common Sense Reasoning, image-classification, Image Classification

Modern Artificial Intelligence (AI) systems excel at diverse tasks, from image classification to strategy games, even outperforming humans in many of these domains. After making astounding progress in language learning in the recent decade, AI systems, however, seem to approach the ceiling that does not reflect important aspects of human communicative capacities. Unlike human learners, communicative AI systems often fail to systematically generalize to new data, suffer from sample inefficiency, fail to capture common-sense semantic knowledge, and do not translate to real-world communicative situations. Cognitive Science offers several insights on how AI could move forward from this point. This paper aims to: (1) suggest that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI, and (2) articulate an alternative, "grounded", perspective on AI advancement, inspired by Embodied, Embedded, Extended, and Enactive Cognition (4E) research. I review results on 4E research lines in Cognitive Science to distinguish the main aspects of naturalistic learning conditions that play causal roles for human language development. I then use this analysis to propose a list of concrete, implementable components for building "grounded" linguistic intelligence. These components include embodying machines in a perception-action cycle, equipping agents with active exploration mechanisms so they can build their own curriculum, allowing agents to gradually develop motor abilities to promote piecemeal language development, and endowing the agents with adaptive feedback from their physical and social environment. I hope that these ideas can direct AI research towards building machines that develop human-like language abilities through their experiences with the world.
Language-driven Semantic Segmentation
Authors: Boyi Li, Kilian Q. Weinberger, Serge Belongie, Vladlen Koltun, René Ranftl
Date: 2022-01-10 00:00:00
Proceeding: ICLR 2022 4
Tasks: Descriptive, Few-Shot Semantic Segmentation, Image Segmentation, Segmentation, Semantic Segmentation, Zero Shot Segmentation

We present LSeg, a novel model for language-driven semantic image segmentation. LSeg uses a text encoder to compute embeddings of descriptive input labels (e.g., "grass" or "building") together with a transformer-based image encoder that computes dense per-pixel embeddings of the input image. The image encoder is trained with a contrastive objective to align pixel embeddings to the text embedding of the corresponding semantic class. The text embeddings provide a flexible label representation in which semantically similar labels map to similar regions in the embedding space (e.g., "cat" and "furry"). This allows LSeg to generalize to previously unseen categories at test time, without retraining or even requiring a single additional training sample. We demonstrate that our approach achieves highly competitive zero-shot performance compared to existing zero- and few-shot semantic segmentation methods, and even matches the accuracy of traditional segmentation algorithms when a fixed label set is provided. Code and demo are available at https://github.com/isl-org/lang-seg.
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning
Authors: Utku Evci, Vincent Dumoulin, Hugo Larochelle, Michael C. Mozer
Date: 2022-01-10 00:00:00
Tasks: Transfer Learning

Transfer-learning methods aim to improve performance in a data-scarce target domain using a model pretrained on a data-rich source domain. A cost-efficient strategy, linear probing, involves freezing the source model and training a new classification head for the target domain. This strategy is outperformed by a more costly but state-of-the-art method -- fine-tuning all parameters of the source model to the target domain -- possibly because fine-tuning allows the model to leverage useful information from intermediate layers which is otherwise discarded by the later pretrained layers. We explore the hypothesis that these intermediate layers might be directly exploited. We propose a method, Head-to-Toe probing (Head2Toe), that selects features from all layers of the source model to train a classification head for the target-domain. In evaluations on the VTAB-1k, Head2Toe matches performance obtained with fine-tuning on average while reducing training and storage cost hundred folds or more, but critically, for out-of-distribution transfer, Head2Toe outperforms fine-tuning.
Learning Population-level Shape Statistics and Anatomy Segmentation From Images: A Joint Deep Learning Model
Authors: Wenzheng Tao, Riddhish Bhalodia, Shireen Elhabian
Date: 2022-01-10 00:00:00
Tasks: Anatomy, Segmentation

Statistical shape modeling is an essential tool for the quantitative analysis of anatomical populations. Point distribution models (PDMs) represent the anatomical surface via a dense set of correspondences, an intuitive and easy-to-use shape representation for subsequent applications. These correspondences are exhibited in two coordinate spaces: the local coordinates describing the geometrical features of each individual anatomical surface and the world coordinates representing the population-level statistical shape information after removing global alignment differences across samples in the given cohort. We propose a deep-learning-based framework that simultaneously learns these two coordinate spaces directly from the volumetric images. The proposed joint model serves a dual purpose; the world correspondences can directly be used for shape analysis applications, circumventing the heavy pre-processing and segmentation involved in traditional PDM models. Additionally, the local correspondences can be used for anatomy segmentation. We demonstrate the efficacy of this joint model for both shape modeling applications on two datasets and its utility in inferring the anatomical surface.
3D Face Morphing Attacks: Generation, Vulnerability and Detection
Authors: Jag Mohan Singh, Raghavendra Ramachandra
Date: 2022-01-10 00:00:00
Tasks: Face Morphing Attack Detection, Face Recognition, MORPH

Face Recognition systems (FRS) have been found to be vulnerable to morphing attacks, where the morphed face image is generated by blending the face images from contributory data subjects. This work presents a novel direction for generating face-morphing attacks in 3D. To this extent, we introduced a novel approach based on blending 3D face point clouds corresponding to contributory data subjects. The proposed method generates 3D face morphing by projecting the input 3D face point clouds onto depth maps and 2D color images, followed by image blending and wrapping operations performed independently on the color images and depth maps. We then back-projected the 2D morphing color map and the depth map to the point cloud using the canonical (fixed) view. Given that the generated 3D face morphing models will result in holes owing to a single canonical view, we have proposed a new algorithm for hole filling that will result in a high-quality 3D face morphing model. Extensive experiments were conducted on the newly generated 3D face dataset comprising 675 3D scans corresponding to 41 unique data subjects and a publicly available database (Facescape) with 100 data subjects. Experiments were performed to benchmark the vulnerability of the {proposed 3D morph-generation scheme against} automatic 2D, 3D FRS, and human observer analysis. We also presented a quantitative assessment of the quality of the generated 3D face-morphing models using eight different quality metrics. Finally, we propose three different 3D face Morphing Attack Detection (3D-MAD) algorithms to benchmark the performance of 3D face morphing attack detection techniques.
High-resolution Ecosystem Mapping in Repetitive Environments Using Dual Camera SLAM
Authors: Brian M. Hopkinson, Suchendra M. Bhandarkar
Date: 2022-01-10 00:00:00
Tasks: global-optimization, Simultaneous Localization and Mapping, Vocal Bursts Intensity Prediction

Structure from Motion (SfM) techniques are being increasingly used to create 3D maps from images in many domains including environmental monitoring. However, SfM techniques are often confounded in visually repetitive environments as they rely primarily on globally distinct image features. Simultaneous Localization and Mapping (SLAM) techniques offer a potential solution in visually repetitive environments since they use local feature matching, but SLAM approaches work best with wide-angle cameras that are often unsuitable for documenting the environmental system of interest. We resolve this issue by proposing a dual-camera SLAM approach that uses a forward facing wide-angle camera for localization and a downward facing narrower angle, high-resolution camera for documentation. Video frames acquired by the forward facing camera video are processed using a standard SLAM approach providing a trajectory of the imaging system through the environment which is then used to guide the registration of the documentation camera images. Fragmentary maps, initially produced from the documentation camera images via monocular SLAM, are subsequently scaled and aligned with the localization camera trajectory and finally subjected to a global optimization procedure to produce a unified, refined map. An experimental comparison with several state-of-the-art SfM approaches shows the dual-camera SLAM approach to perform better in repetitive environmental systems based on select samples of ground control point markers.
GMFIM: A Generative Mask-guided Facial Image Manipulation Model for Privacy Preservation
Authors: Mohammad Hossein Khojaste, Nastaran Moradzadeh Farid, Ahmad Nickabadi
Date: 2022-01-10 00:00:00
Tasks: Face Recognition, Image Manipulation

The use of social media websites and applications has become very popular and people share their photos on these networks. Automatic recognition and tagging of people's photos on these networks has raised privacy preservation issues and users seek methods for hiding their identities from these algorithms. Generative adversarial networks (GANs) are shown to be very powerful in generating face images in high diversity and also in editing face images. In this paper, we propose a Generative Mask-guided Face Image Manipulation (GMFIM) model based on GANs to apply imperceptible editing to the input face image to preserve the privacy of the person in the image. Our model consists of three main components: a) the face mask module to cut the face area out of the input image and omit the background, b) the GAN-based optimization module for manipulating the face image and hiding the identity and, c) the merge module for combining the background of the input image and the manipulated de-identified face image. Different criteria are considered in the loss function of the optimization step to produce high-quality images that are as similar as possible to the input image while they cannot be recognized by AFR systems. The results of the experiments on different datasets show that our model can achieve better performance against automated face recognition systems in comparison to the state-of-the-art methods and it catches a higher attack success rate in most experiments from a total of 18. Moreover, the generated images of our proposed model have the highest quality and are more pleasing to human eyes.
COIN: Counterfactual Image Generation for VQA Interpretation
Authors: Zeyd Boukhers, Timo Hartmann, Jan Jürjens
Date: 2022-01-10 00:00:00
Tasks: counterfactual, Image Generation, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent and advanced. However, they are still error-prone when dealing with relatively complex questions. Therefore, it is important to understand the behaviour of the VQA models before adopting their results. In this paper, we introduce an interpretability approach for VQA models by generating counterfactual images. Specifically, the generated image is supposed to have the minimal possible change to the original image and leads the VQA model to give a different answer. In addition, our approach ensures that the generated image is realistic. Since quantitative metrics cannot be employed to evaluate the interpretability of the model, we carried out a user study to assess different aspects of our approach. In addition to interpreting the result of VQA models on single images, the obtained results and the discussion provides an extensive explanation of VQA models' behaviour.