pwc-archive/papers-with-abstracts huggingface dataset:
Poseur: Direct Human Pose Regression with Transformers
Authors: Weian Mao, Yongtao Ge, Chunhua Shen, Zhi Tian, Xinlong Wang, Zhibin Wang, Anton Van Den Hengel
Date: 2022-01-19 00:00:00
Tasks: 2D Human Pose Estimation, Keypoint Detection, Pose Estimation, regression

We propose a direct, regression-based approach to 2D human pose estimation from single images. We formulate the problem as a sequence prediction task, which we solve using a Transformer network. This network directly learns a regression mapping from images to the keypoint coordinates, without resorting to intermediate representations such as heatmaps. This approach avoids much of the complexity associated with heatmap-based approaches. To overcome the feature misalignment issues of previous regression-based methods, we propose an attention mechanism that adaptively attends to the features that are most relevant to the target keypoints, considerably improving the accuracy. Importantly, our framework is end-to-end differentiable, and naturally learns to exploit the dependencies between keypoints. Experiments on MS-COCO and MPII, two predominant pose-estimation datasets, demonstrate that our method significantly improves upon the state-of-the-art in regression-based pose estimation. More notably, ours is the first regression-based approach to perform favorably compared to the best heatmap-based pose estimation methods.
KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition
Authors: Chingis Oinar, Binh M. Le, Simon S. Woo
Date: 2022-01-19 00:00:00
Tasks: Diversity, Face Recognition

Feature learning is a widely used method employed for large-scale face recognition. Recently, large-margin softmax loss methods have demonstrated significant enhancements on deep face recognition. These methods propose fixed positive margins in order to enforce intra-class compactness and inter-class diversity. However, the majority of the proposed methods do not consider the class imbalance issue, which is a major challenge in practice for developing deep face recognition models. We hypothesize that it significantly affects the generalization ability of the deep face models. Inspired by this observation, we introduce a novel adaptive strategy, called KappaFace, to modulate the relative importance based on class difficultness and imbalance. With the support of the von Mises-Fisher distribution, our proposed KappaFace loss can intensify the margin's magnitude for hard learning or low concentration classes while relaxing it for counter classes. Experiments conducted on popular facial benchmarks demonstrate that our proposed method achieves superior performance to the state-of-the-art.
Swin-Pose: Swin Transformer Based Human Pose Estimation
Authors: Zinan Xiong, Chenxi Wang, Ying Li, Yan Luo, Yu Cao
Date: 2022-01-19 00:00:00
Tasks: Pose Estimation

Convolutional neural networks (CNNs) have been widely utilized in many computer vision tasks. However, CNNs have a fixed reception field and lack the ability of long-range perception, which is crucial to human pose estimation. Due to its capability to capture long-range dependencies between pixels, transformer architecture has been adopted to computer vision applications recently and is proven to be a highly effective architecture. We are interested in exploring its capability in human pose estimation, and thus propose a novel model based on transformer architecture, enhanced with a feature pyramid fusion structure. More specifically, we use pre-trained Swin Transformer as our backbone and extract features from input images, we leverage a feature pyramid structure to extract feature maps from different stages. By fusing the features together, our model predicts the keypoint heatmap. The experiment results of our study have demonstrated that the proposed transformer-based model can achieve better performance compared to the state-of-the-art CNN-based models.
Online Deep Learning based on Auto-Encoder
Authors: Si-si Zhang, Jian-wei Liu, Xin Zuo, Run-kun Lu, Si-ming Lian
Date: 2022-01-19 00:00:00
Tasks: Deep Learning, Denoising

Online learning is an important technical means for sketching massive real-time and high-speed data. Although this direction has attracted intensive attention, most of the literature in this area ignore the following three issues: (1) they think little of the underlying abstract hierarchical latent information existing in examples, even if extracting these abstract hierarchical latent representations is useful to better predict the class labels of examples; (2) the idea of preassigned model on unseen datapoints is not suitable for modeling streaming data with evolving probability distribution. This challenge is referred as model flexibility. And so, with this in minds, the online deep learning model we need to design should have a variable underlying structure; (3) moreover, it is of utmost importance to fusion these abstract hierarchical latent representations to achieve better classification performance, and we should give different weights to different levels of implicit representation information when dealing with the data streaming where the data distribution changes. To address these issues, we propose a two-phase Online Deep Learning based on Auto-Encoder (ODLAE). Based on auto-encoder, considering reconstruction loss, we extract abstract hierarchical latent representations of instances; Based on predictive loss, we devise two fusion strategies: the output-level fusion strategy, which is obtained by fusing the classification results of encoder each hidden layer; and feature-level fusion strategy, which is leveraged self-attention mechanism to fusion every hidden layer output. Finally, in order to improve the robustness of the algorithm, we also try to utilize the denoising auto-encoder to yield hierarchical latent representations. Experimental results on different datasets are presented to verify the validity of our proposed algorithm (ODLAE) outperforms several baselines.
The Role of Pleura and Adipose in Lung Ultrasound AI
Authors: Gautam Rajendrakumar Gare, Wanwen Chen, Alex Ling Yu Hung, Edward Chen, Hai V. Tran, Tom Fox, Pete Lowery, Kevin Zamora, Bennett P deBoisblanc, Ricardo Luis Rodriguez, John Michael Galeotti
Date: 2022-01-19 00:00:00
Tasks: Diagnostic

In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis. We highlight their more prominent appearance when using high-frequency linear (HFL) instead of curvilinear ultrasound probes, showing HFL reveals better pleura detail. We compare the diagnostic utility of the pleura and adipose tissue using an HFL ultrasound probe. Masking the adipose tissue during training and inference (while retaining the pleural line and Merlin's space artifacts such as A-lines and B-lines) improved the AI model's diagnostic accuracy.
TriCoLo: Trimodal Contrastive Loss for Text to Shape Retrieval
Authors: Yue Ruan, Han-Hung Lee, Yiming Zhang, Ke Zhang, Angel X. Chang
Date: 2022-01-19 00:00:00
Tasks: Contrastive Learning, Multi-Task Learning, Representation Learning, Retrieval

Text-to-shape retrieval is an increasingly relevant problem with the growth of 3D shape data. Recent work on contrastive losses for learning joint embeddings over multimodal data has been successful at tasks such as retrieval and classification. Thus far, work on joint representation learning for 3D shapes and text has focused on improving embeddings through modeling of complex attention between representations, or multi-task learning. We propose a trimodal learning scheme over text, multi-view images and 3D shape voxels, and show that with large batch contrastive learning we achieve good performance on text-to-shape retrieval without complex attention mechanisms or losses. Our experiments serve as a foundation for follow-up work on building trimodal embeddings for text-image-shape.
Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound
Authors: Gautam Rajendrakumar Gare, Hai V. Tran, Bennett P deBoisblanc, Ricardo Luis Rodriguez, John Michael Galeotti
Date: 2022-01-18 00:00:00
Tasks: Contrastive Learning

With the onset of the COVID-19 pandemic, ultrasound has emerged as an effective tool for bedside monitoring of patients. Due to this, a large amount of lung ultrasound scans have been made available which can be used for AI based diagnosis and analysis. Several AI-based patient severity scoring models have been proposed that rely on scoring the appearance of the ultrasound scans. AI models are trained using ultrasound-appearance severity scores that are manually labeled based on standardized visual features. We address the challenge of labeling every ultrasound frame in the video clips. Our contrastive learning method treats the video clip severity labels as noisy weak severity labels for individual frames, thus requiring only video-level labels. We show that it performs better than the conventional cross-entropy loss based training. We combine frame severity predictions to come up with video severity predictions and show that the frame based model achieves comparable performance to a video based TSM model, on a large dataset combining public and private sources.
Lung Swapping Autoencoder: Learning a Disentangled Structure-texture Representation of Chest Radiographs
Authors: Lei Zhou, Joseph Bae, Huidong Liu, Gagandeep Singh, Jeremy Green, Amit Gupta, Dimitris Samaras, Prateek Prasanna
Date: 2022-01-18 00:00:00
Tasks: Data Augmentation

Well-labeled datasets of chest radiographs (CXRs) are difficult to acquire due to the high cost of annotation. Thus, it is desirable to learn a robust and transferable representation in an unsupervised manner to benefit tasks that lack labeled data. Unlike natural images, medical images have their own domain prior; e.g., we observe that many pulmonary diseases, such as the COVID-19, manifest as changes in the lung tissue texture rather than the anatomical structure. Therefore, we hypothesize that studying only the texture without the influence of structure variations would be advantageous for downstream prognostic and predictive modeling tasks. In this paper, we propose a generative framework, the Lung Swapping Autoencoder (LSAE), that learns factorized representations of a CXR to disentangle the texture factor from the structure factor. Specifically, by adversarial training, the LSAE is optimized to generate a hybrid image that preserves the lung shape in one image but inherits the lung texture of another. To demonstrate the effectiveness of the disentangled texture representation, we evaluate the texture encoder $Enc^t$ in LSAE on ChestX-ray14 (N=112,120), and our own multi-institutional COVID-19 outcome prediction dataset, COVOC (N=340 (Subset-1) + 53 (Subset-2)). On both datasets, we reach or surpass the state-of-the-art by finetuning $Enc^t$ in LSAE that is 77% smaller than a baseline Inception v3. Additionally, in semi-and-self supervised settings with a similar model budget, $Enc^t$ in LSAE is also competitive with the state-of-the-art MoCo. By "re-mixing" the texture and shape factors, we generate meaningful hybrid images that can augment the training set. This data augmentation method can further improve COVOC prediction performance. The improvement is consistent even when we directly evaluate the Subset-1 trained model on Subset-2 without any fine-tuning.
Contrastive Pretraining for Echocardiography Segmentation with Limited Data
Authors: Mohamed Saeed, Rand Muhtaseb, Mohammad Yaqub
Date: 2022-01-16 00:00:00
Tasks: Contrastive Learning, Image Segmentation, Left Ventricle Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Contrastive learning has proven useful in many applications where access to labelled data is limited. The lack of annotated data is particularly problematic in medical image segmentation as it is difficult to have clinical experts manually annotate large volumes of data such as cardiac structures in ultrasound images of the heart. In this paper, We propose a self supervised contrastive learning method to segment the left ventricle from echocardiography when limited annotated images exist. Furthermore, we study the effect of contrastive pretraining on two well-known segmentation networks, UNet and DeepLabV3. Our results show that contrastive pretraining helps improve the performance on left ventricle segmentation, particularly when annotated data is scarce. We show how to achieve comparable results to state-of-the-art fully supervised algorithms when we train our models in a self-supervised fashion followed by fine-tuning on just 5\% of the data. We show that our solution outperforms what is currently published on a large public dataset (EchoNet-Dynamic) achieving a Dice score of 0.9252. We also compare the performance of our solution on another smaller dataset (CAMUS) to demonstrate the generalizability of our proposed solution. The code is available at (https://github.com/BioMedIA-MBZUAI/contrastive-echo).
Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generation
Authors: Xian Liu, Yinghao Xu, Qianyi Wu, Hang Zhou, Wayne Wu, Bolei Zhou
Date: 2022-01-19 00:00:00
Tasks: NeRF

Animating high-fidelity video portrait with speech audio is crucial for virtual reality and digital entertainment. While most previous studies rely on accurate explicit structural information, recent works explore the implicit scene representation of Neural Radiance Fields (NeRF) for realistic generation. In order to capture the inconsistent motions as well as the semantic difference between human head and torso, some work models them via two individual sets of NeRF, leading to unnatural results. In this work, we propose Semantic-aware Speaking Portrait NeRF (SSP-NeRF), which creates delicate audio-driven portraits using one unified set of NeRF. The proposed model can handle the detailed local facial semantics and the global head-torso relationship through two semantic-aware modules. Specifically, we first propose a Semantic-Aware Dynamic Ray Sampling module with an additional parsing branch that facilitates audio-driven volume rendering. Moreover, to enable portrait rendering in one unified neural radiance field, a Torso Deformation module is designed to stabilize the large-scale non-rigid torso motions. Extensive evaluations demonstrate that our proposed approach renders more realistic video portraits compared to previous methods. Project page: https://alvinliu0.github.io/projects/SSP-NeRF
Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons
Authors: Apostolos F Psaros, Xuhui Meng, Zongren Zou, Ling Guo, George Em Karniadakis
Date: 2022-01-19 00:00:00
Tasks: BIG-bench Machine Learning, Uncertainty Quantification

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. To demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed, which we will make available as open-source library of all codes included in this framework.
Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification
Authors: Akshay Thakur, Souvik Chakraborty
Date: 2022-01-19 00:00:00
Tasks: Decoder, Image-to-Image Regression, Uncertainty Quantification

We propose a novel \textit{capsule} based deep encoder-decoder model for surrogate modeling and uncertainty quantification of systems in mechanics from sparse data. The proposed framework is developed by adapting Capsule Network (CapsNet) architecture into image-to-image regression encoder-decoder network. Specifically, the aim is to exploit the benefits of CapsNet over convolution neural network (CNN) $-$ retaining pose and position information related to an entity to name a few. The performance of proposed approach is illustrated by solving an elliptic stochastic partial differential equation (SPDE), which also governs systems in mechanics such as steady heat conduction, ground water flow or other diffusion processes, based uncertainty quantification problem with an input dimensionality of $1024$. However, the problem definition does not the restrict the random diffusion field to a particular covariance structure, and the more strenuous task of response prediction for an arbitrary diffusion field is solved. The obtained results from performance evaluation indicate that the proposed approach is accurate, efficient, and robust.
SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation
Authors: Yash More, Prashanthi Ramachandran, Priyam Panda, Arup Mondal, Harpreet Virk, Debayan Gupta
Date: 2022-01-19 00:00:00
Tasks: Federated Learning, Privacy Preserving

Federated learning enables multiple data owners to jointly train a machine learning model without revealing their private datasets. However, a malicious aggregation server might use the model parameters to derive sensitive information about the training dataset used. To address such leakage, differential privacy and cryptographic techniques have been investigated in prior work, but these often result in large communication overheads or impact model performance. To mitigate this centralization of power, we propose SCOTCH, a decentralized m-party secure-computation framework for federated aggregation that deploys MPC primitives, such as secret sharing. Our protocol is simple, efficient, and provides strict privacy guarantees against curious aggregators or colluding data-owners with minimal communication overheads compared to other existing state-of-the-art privacy-preserving federated learning frameworks. We evaluate our framework by performing extensive experiments on multiple datasets with promising results. SCOTCH can train the standard MLP NN with the training dataset split amongst 3 participating users and 3 aggregating servers with 96.57% accuracy on MNIST, and 98.40% accuracy on the Extended MNIST (digits) dataset, while providing various optimizations.
Enhancing the Security & Privacy of Wearable Brain-Computer Interfaces
Authors: Zahra Tarkhani, Lorena Qendro, Malachy O'Connor Brown, Oscar Hill, Cecilia Mascolo, Anil Madhavapeddy
Date: 2022-01-19 00:00:00

Brain computing interfaces (BCI) are used in a plethora of safety/privacy-critical applications, ranging from healthcare to smart communication and control. Wearable BCI setups typically involve a head-mounted sensor connected to a mobile device, combined with ML-based data processing. Consequently, they are susceptible to a multiplicity of attacks across the hardware, software, and networking stacks used that can leak users' brainwave data or at worst relinquish control of BCI-assisted devices to remote attackers. In this paper, we: (i) analyse the whole-system security and privacy threats to existing wearable BCI products from an operating system and adversarial machine learning perspective; and (ii) introduce Argus, the first information flow control system for wearable BCI applications that mitigates these attacks. Argus' domain-specific design leads to a lightweight implementation on Linux ARM platforms suitable for existing BCI use-cases. Our proof of concept attacks on real-world BCI devices (Muse, NeuroSky, and OpenBCI) led us to discover more than 300 vulnerabilities across the stacks of six major attack vectors. Our evaluation shows Argus is highly effective in tracking sensitive dataflows and restricting these attacks with an acceptable memory and performance overhead (<15%).
Anytime PSRO for Two-Player Zero-Sum Games
Authors: Stephen Mcaleer, Kevin Wang, John Lanier, Marc Lanctot, Pierre Baldi, Tuomas Sandholm, Roy Fox
Date: 2022-01-19 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Vocal Bursts Valence Prediction

Policy space response oracles (PSRO) is a multi-agent reinforcement learning algorithm that has achieved state-of-the-art performance in very large two-player zero-sum games. PSRO is based on the tabular double oracle (DO) method, an algorithm that is guaranteed to converge to a Nash equilibrium, but may increase exploitability from one iteration to the next. We propose anytime double oracle (ADO), a tabular double oracle algorithm for 2-player zero-sum games that is guaranteed to converge to a Nash equilibrium while decreasing exploitability from one iteration to the next. Unlike DO, in which the restricted distribution is based on the restricted game formed by each player's strategy sets, ADO finds the restricted distribution for each player that minimizes its exploitability against any policy in the full, unrestricted game. We also propose a method of finding this restricted distribution via a no-regret algorithm updated against best responses, called RM-BR DO. Finally, we propose anytime PSRO (APSRO), a version of ADO that calculates best responses via reinforcement learning. In experiments on Leduc poker and random normal form games, we show that our methods achieve far lower exploitability than DO and PSRO and decrease exploitability monotonically.
On the Complexity of a Practical Primal-Dual Coordinate Method
Authors: Ahmet Alacaoglu, Volkan Cevher, Stephen J. Wright
Date: 2022-01-19 00:00:00

We prove complexity bounds for the primal-dual algorithm with random extrapolation and coordinate descent (PURE-CD), which has been shown to obtain good practical performance for solving convex-concave min-max problems with bilinear coupling. Our complexity bounds either match or improve the best-known results in the literature for both dense and sparse (strongly)-convex-(strongly)-concave problems.