pwc-archive/papers-with-abstracts huggingface dataset:
Few-Shot Meta-Learning on Point Cloud for Semantic Segmentation
Authors: Xudong Li, Li Feng, Lei LI, Chen Wang
Date: 2021-04-07 00:00:00
Tasks: Autonomous Driving, Meta-Learning, Semantic Segmentation

The promotion of construction robots can solve the problem of human resource shortage and improve the quality of decoration. To help the construction robots obtain environmental information, we need to use 3D point cloud, which is widely used in robotics, autonomous driving, and so on. With a good understanding of environmental information, construction robots can work better. However, the dynamic changes of 3D point cloud data may bring difficulties for construction robots to understand environmental information, such as when construction robots renovate houses. The paper proposes a semantic segmentation method for point cloud based on meta-learning. The method includes a basic learning module and a meta-learning module. The basic learning module is responsible for learning data features and evaluating the model, while the meta-learning module is responsible for updating the parameters of the model and improving the model generalization ability. In our work, we pioneered the method of producing datasets for meta-learning in 3D scenes, as well as demonstrated that the Model-Agnostic Meta-Learning (MAML) algorithm can be applied to process 3D point cloud data. At the same time, experiments show that our method can allow the model to be quickly applied to new environments with a few samples. Our method has important applications.
Active learning using weakly supervised signals for quality inspection
Authors: Antoine Cordier, Deepan Das, Pierre Gutierrez
Date: 2021-04-07 00:00:00
Tasks: Active Learning

Because manufacturing processes evolve fast, and since production visual aspect can vary significantly on a daily basis, the ability to rapidly update machine vision based inspection systems is paramount. Unfortunately, supervised learning of convolutional neural networks requires a significant amount of annotated images for being able to learn effectively from new data. Acknowledging the abundance of continuously generated images coming from the production line and the cost of their annotation, we demonstrate it is possible to prioritize and accelerate the annotation process. In this work, we develop a methodology for learning actively, from rapidly mined, weakly (i.e. partially) annotated data, enabling a fast, direct feedback from the operators on the production line and tackling a big machine vision weakness: false positives. We also consider the problem of covariate shift, which arises inevitably due to changing conditions during data acquisition. In that regard, we show domain-adversarial training to be an efficient way to address this issue.
MPN: Multimodal Parallel Network for Audio-Visual Event Localization
Authors: Jiashuo Yu, Ying Cheng, Rui Feng
Date: 2021-04-07 00:00:00
Tasks: audio-visual event localization, General Classification

Audio-visual event localization aims to localize an event that is both audible and visible in the wild, which is a widespread audio-visual scene analysis task for unconstrained videos. To address this task, we propose a Multimodal Parallel Network (MPN), which can perceive global semantics and unmixed local information parallelly. Specifically, our MPN framework consists of a classification subnetwork to predict event categories and a localization subnetwork to predict event boundaries. The classification subnetwork is constructed by the Multimodal Co-attention Module (MCM) and obtains global contexts. The localization subnetwork consists of Multimodal Bottleneck Attention Module (MBAM), which is designed to extract fine-grained segment-level contents. Extensive experiments demonstrate that our framework achieves the state-of-the-art performance both in fully supervised and weakly supervised settings on the Audio-Visual Event (AVE) dataset.
The Emergence of Abstract and Episodic Neurons in Episodic Meta-RL
Authors: Badr AlKhamissi, Muhammad ElNokrashy, Michael Spranger
Date: 2021-04-07 00:00:00
Proceeding: ICLR Workshop Learning_to_Learn 2021 5

In this work, we analyze the reinstatement mechanism introduced by Ritter et al. (2018) to reveal two classes of neurons that emerge in the agent's working memory (an epLSTM cell) when trained using episodic meta-RL on an episodic variant of the Harlow visual fixation task. Specifically, Abstract neurons encode knowledge shared across tasks, while Episodic neurons carry information relevant for a specific episode's task.
Deep Interpretable Models of Theory of Mind
Authors: Ini Oguntola, Dana Hughes, Katia Sycara
Date: 2021-04-07 00:00:00
Tasks: Minecraft

When developing AI systems that interact with humans, it is essential to design both a system that can understand humans, and a system that humans can understand. Most deep network based agent-modeling approaches are 1) not interpretable and 2) only model external behavior, ignoring internal mental states, which potentially limits their capability for assistance, interventions, discovering false beliefs, etc. To this end, we develop an interpretable modular neural framework for modeling the intentions of other observed entities. We demonstrate the efficacy of our approach with experiments on data from human participants on a search and rescue task in Minecraft, and show that incorporating interpretability can significantly increase predictive performance under the right conditions.
A Question-answering Based Framework for Relation Extraction Validation
Authors: Jiayang Cheng, Haiyun Jiang, Deqing Yang, Yanghua Xiao
Date: 2021-04-07 00:00:00
Tasks: Question Answering, Relation, Relation Extraction

Relation extraction is an important task in knowledge acquisition and text understanding. Existing works mainly focus on improving relation extraction by extracting effective features or designing reasonable model structures. However, few works have focused on how to validate and correct the results generated by the existing relation extraction models. We argue that validation is an important and promising direction to further improve the performance of relation extraction. In this paper, we explore the possibility of using question answering as validation. Specifically, we propose a novel question-answering based framework to validate the results from relation extraction models. Our proposed framework can be easily applied to existing relation classifiers without any additional information. We conduct extensive experiments on the popular NYT dataset to evaluate the proposed framework, and observe consistent improvements over five strong baselines.
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data
Authors: Tingyi Wanyan, Jing Zhang, Ying Ding, Ariful Azad, Zhangyang Wang, Benjamin S Glicksberg
Date: 2021-04-07 00:00:00
Tasks: Attribute, Contrastive Learning, Data Augmentation

Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events. These tasks, however, often come with many challenges when using classical machine learning models due to a myriad of factors including class imbalance and data heterogeneity (i.e., the complex intra-class variances). To address some of these research gaps, this paper leverages the exciting contrastive learning framework and proposes a novel contrastive regularized clinical classification model. The contrastive loss is found to substantially augment EHR-based prediction: it effectively characterizes the similar/dissimilar patterns (by its "push-and-pull" form), meanwhile mitigating the highly skewed class distribution by learning more balanced feature spaces (as also echoed by recent findings). In particular, when naively exporting the contrastive learning to the EHR data, one hurdle is in generating positive samples, since EHR data is not as amendable to data augmentation as image data. To this end, we have introduced two unique positive sampling strategies specifically tailored for EHR data: a feature-based positive sampling that exploits the feature space neighborhood structure to reinforce the feature learning; and an attribute-based positive sampling that incorporates pre-generated patient similarity metrics to define the sample proximity. Both sampling approaches are designed with an awareness of unique high intra-class variance in EHR data. Our overall framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data with a total of 5,712 patients admitted to a large, urban health system. Specifically, our method reaches a high AUROC prediction score of 0.959, which outperforms other baselines and alternatives: cross-entropy(0.873) and focal loss(0.931).
Pretrained equivariant features improve unsupervised landmark discovery
Authors: Rahul Rahaman, Atin Ghosh, Alexandre H. Thiery
Date: 2021-04-07 00:00:00

Locating semantically meaningful landmark points is a crucial component of a large number of computer vision pipelines. Because of the small number of available datasets with ground truth landmark annotations, it is important to design robust unsupervised and semi-supervised methods for landmark detection. Many of the recent unsupervised learning methods rely on the equivariance properties of landmarks to synthetic image deformations. Our work focuses on such widely used methods and sheds light on its core problem, its inability to produce equivariant intermediate convolutional features. This finding leads us to formulate a two-step unsupervised approach that overcomes this challenge by first learning powerful pixel-based features and then use the pre-trained features to learn a landmark detector by the traditional equivariance method. Our method produces state-of-the-art results in several challenging landmark detection datasets such as the BBC Pose dataset and the Cat-Head dataset. It performs comparably on a range of other benchmarks.
Facial Attribute Transformers for Precise and Robust Makeup Transfer
Authors: Zhaoyi Wan, Haoran Chen, Jielei Zhang, Wentao Jiang, Cong Yao, Jiebo Luo
Date: 2021-04-07 00:00:00
Tasks: Attribute, Face Generation

In this paper, we address the problem of makeup transfer, which aims at transplanting the makeup from the reference face to the source face while preserving the identity of the source. Existing makeup transfer methods have made notable progress in generating realistic makeup faces, but do not perform well in terms of color fidelity and spatial transformation. To tackle these issues, we propose a novel Facial Attribute Transformer (FAT) and its variant Spatial FAT for high-quality makeup transfer. Drawing inspirations from the Transformer in NLP, FAT is able to model the semantic correspondences and interactions between the source face and reference face, and then precisely estimate and transfer the facial attributes. To further facilitate shape deformation and transformation of facial parts, we also integrate thin plate splines (TPS) into FAT, thus creating Spatial FAT, which is the first method that can transfer geometric attributes in addition to color and texture. Extensive qualitative and quantitative experiments demonstrate the effectiveness and superiority of our proposed FATs in the following aspects: (1) ensuring high-fidelity color transfer; (2) allowing for geometric transformation of facial parts; (3) handling facial variations (such as poses and shadows) and (4) supporting high-resolution face generation.
LI-Net: Large-Pose Identity-Preserving Face Reenactment Network
Authors: Jin Liu, Peng Chen, Tao Liang, Zhaoxing Li, Cai Yu, Shuqiao Zou, Jiao Dai, Jizhong Han
Date: 2021-04-07 00:00:00
Tasks: Face Reenactment

Face reenactment is a challenging task, as it is difficult to maintain accurate expression, pose and identity simultaneously. Most existing methods directly apply driving facial landmarks to reenact source faces and ignore the intrinsic gap between two identities, resulting in the identity mismatch issue. Besides, they neglect the entanglement of expression and pose features when encoding driving faces, leading to inaccurate expressions and visual artifacts on large-pose reenacted faces. To address these problems, we propose a Large-pose Identity-preserving face reenactment network, LI-Net. Specifically, the Landmark Transformer is adopted to adjust driving landmark images, which aims to narrow the identity gap between driving and source landmark images. Then the Face Rotation Module and the Expression Enhancing Generator decouple the transformed landmark image into pose and expression features, and reenact those attributes separately to generate identity-preserving faces with accurate expressions and poses. Both qualitative and quantitative experimental results demonstrate the superiority of our method.
Deep Implicit Statistical Shape Models for 3D Medical Image Delineation
Authors: Ashwin Raju, Shun Miao, Dakai Jin, Le Lu, Junzhou Huang, Adam P. Harrison
Date: 2021-04-07 00:00:00
Tasks: Descriptive, Liver Segmentation, Pose Estimation

3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. Today fully-convolutional networks (FCNs), while dominant, do not offer these capabilities. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of convolutional neural networks (CNNs) with the robustness of SSMs. DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance. To reliably fit anatomically plausible shapes to an image, we introduce a novel rigid and non-rigid pose estimation pipeline that is modelled as a Markov decision process(MDP). We outline a training regime that includes inverted episodic training and a deep realization of marginal space learning (MSL). Intra-dataset experiments on the task of pathological liver segmentation demonstrate that DISSMs can perform more robustly than three leading FCN models, including nnU-Net: reducing the mean Hausdorff distance (HD) by 7.7-14.3mm and improving the worst case Dice-Sorensen coefficient (DSC) by 1.2-2.3%. More critically, cross-dataset experiments on a dataset directly reflecting clinical deployment scenarios demonstrate that DISSMs improve the mean DSC and HD by 3.5-5.9% and 12.3-24.5mm, respectively, and the worst-case DSC by 5.4-7.3%. These improvements are over and above any benefits from representing delineations with high-quality surface.
GEM: Group Enhanced Model for Learning Dynamical Control Systems
Authors: Philippe Hansen-Estruch, Wenling Shang, Lerrel Pinto, Pieter Abbeel, Stas Tiomkin
Date: 2021-04-07 00:00:00
Tasks: continuous-control, Continuous Control, Model-based Reinforcement Learning

Learning the dynamics of a physical system wherein an autonomous agent operates is an important task. Often these systems present apparent geometric structures. For instance, the trajectories of a robotic manipulator can be broken down into a collection of its transitional and rotational motions, fully characterized by the corresponding Lie groups and Lie algebras. In this work, we take advantage of these structures to build effective dynamical models that are amenable to sample-based learning. We hypothesize that learning the dynamics on a Lie algebra vector space is more effective than learning a direct state transition model. To verify this hypothesis, we introduce the Group Enhanced Model (GEM). GEMs significantly outperform conventional transition models on tasks of long-term prediction, planning, and model-based reinforcement learning across a diverse suite of standard continuous-control environments, including Walker, Hopper, Reacher, Half-Cheetah, Inverted Pendulums, Ant, and Humanoid. Furthermore, plugging GEM into existing state of the art systems enhances their performance, which we demonstrate on the PETS system. This work sheds light on a connection between learning of dynamics and Lie group properties, which opens doors for new research directions and practical applications along this direction. Our code is publicly available at: https://tinyurl.com/GEMMBRL.
Learning Triadic Belief Dynamics in Nonverbal Communication from Videos
Authors: Lifeng Fan, Shuwen Qiu, Zilong Zheng, Tao Gao, Song-Chun Zhu, Yixin Zhu
Date: 2021-04-07 00:00:00
Proceeding: CVPR 2021 1
Tasks: Scene Understanding

Humans possess a unique social cognition capability; nonverbal communication can convey rich social information among agents. In contrast, such crucial social characteristics are mostly missing in the existing scene understanding literature. In this paper, we incorporate different nonverbal communication cues (e.g., gaze, human poses, and gestures) to represent, model, learn, and infer agents' mental states from pure visual inputs. Crucially, such a mental representation takes the agent's belief into account so that it represents what the true world state is and infers the beliefs in each agent's mental state, which may differ from the true world states. By aggregating different beliefs and true world states, our model essentially forms "five minds" during the interactions between two agents. This "five minds" model differs from prior works that infer beliefs in an infinite recursion; instead, agents' beliefs are converged into a "common mind". Based on this representation, we further devise a hierarchical energy-based model that jointly tracks and predicts all five minds. From this new perspective, a social event is interpreted by a series of nonverbal communication and belief dynamics, which transcends the classic keyframe video summary. In the experiments, we demonstrate that using such a social account provides a better video summary on videos with rich social interactions compared with state-of-the-art keyframe video summary methods.
Towards Measuring Fairness in AI: the Casual Conversations Dataset
Authors: Caner Hazirbas, Joanna Bitton, Brian Dolhansky, Jacqueline Pan, Albert Gordo, Cristian Canton Ferrer
Date: 2021-04-06 00:00:00
Tasks: Age And Gender Classification, DeepFake Detection, Face Swapping, Fairness, Gender Classification, Speech-to-Text Translation

This paper introduces a novel dataset to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of age, genders, apparent skin tones and ambient lighting conditions. Our dataset is composed of 3,011 subjects and contains over 45,000 videos, with an average of 15 videos per person. The videos were recorded in multiple U.S. states with a diverse set of adults in various age, gender and apparent skin tone groups. A key feature is that each subject agreed to participate for their likenesses to be used. Additionally, our age and gender annotations are provided by the subjects themselves. A group of trained annotators labeled the subjects' apparent skin tone using the Fitzpatrick skin type scale. Moreover, annotations for videos recorded in low ambient lighting are also provided. As an application to measure robustness of predictions across certain attributes, we provide a comprehensive study on the top five winners of the DeepFake Detection Challenge (DFDC). Experimental evaluation shows that the winning models are less performant on some specific groups of people, such as subjects with darker skin tones and thus may not generalize to all people. In addition, we also evaluate the state-of-the-art apparent age and gender classification methods. Our experiments provides a thorough analysis on these models in terms of fair treatment of people from various backgrounds.
Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic Systems
Authors: Karl-Philipp Kortmann, Moritz Fehsenfeld, Mark Wielitzka
Date: 2021-04-06 00:00:00
Tasks: Representation Learning, Time Series, Time Series Analysis

Sensor and control data of modern mechatronic systems are often available as heterogeneous time series with different sampling rates and value ranges. Suitable classification and regression methods from the field of supervised machine learning already exist for predictive tasks, for example in the context of condition monitoring, but their performance scales strongly with the number of labeled training data. Their provision is often associated with high effort in the form of person-hours or additional sensors. In this paper, we present a method for unsupervised feature extraction using autoencoder networks that specifically addresses the heterogeneous nature of the database and reduces the amount of labeled training data required compared to existing methods. Three public datasets of mechatronic systems from different application domains are used to validate the results.
Robust Semantic Interpretability: Revisiting Concept Activation Vectors
Authors: Jacob Pfau, Albert T. Young, Jerome Wei, Maria L. Wei, Michael J. Keiser
Date: 2021-04-06 00:00:00
Tasks: Benchmarking, counterfactual, image-classification, Image Classification

Interpretability methods for image classification assess model trustworthiness by attempting to expose whether the model is systematically biased or attending to the same cues as a human would. Saliency methods for feature attribution dominate the interpretability literature, but these methods do not address semantic concepts such as the textures, colors, or genders of objects within an image. Our proposed Robust Concept Activation Vectors (RCAV) quantifies the effects of semantic concepts on individual model predictions and on model behavior as a whole. RCAV calculates a concept gradient and takes a gradient ascent step to assess model sensitivity to the given concept. By generalizing previous work on concept activation vectors to account for model non-linearity, and by introducing stricter hypothesis testing, we show that RCAV yields interpretations which are both more accurate at the image level and robust at the dataset level. RCAV, like saliency methods, supports the interpretation of individual predictions. To evaluate the practical use of interpretability methods as debugging tools, and the scientific use of interpretability methods for identifying inductive biases (e.g. texture over shape), we construct two datasets and accompanying metrics for realistic benchmarking of semantic interpretability methods. Our benchmarks expose the importance of counterfactual augmentation and negative controls for quantifying the practical usability of interpretability methods.
Efficient transfer learning for NLP with ELECTRA
Authors: François Mercier
Date: 2021-04-06 00:00:00
Tasks: Transfer Learning

Clark et al. [2020] claims that the ELECTRA approach is highly efficient in NLP performances relative to computation budget. As such, this reproducibility study focus on this claim, summarized by the following question: Can we use ELECTRA to achieve close to SOTA performances for NLP in low-resource settings, in term of compute cost?