pwc-archive/papers-with-abstracts huggingface dataset:
Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks
Authors: Andrew C. Li, Pashootan Vaezipoor, Rodrigo Toro Icarte, Sheila A. McIlraith
Date: 2022-06-03 00:00:00
Tasks: Deep Reinforcement Learning

Deep reinforcement learning has shown promise in discrete domains requiring complex reasoning, including games such as Chess, Go, and Hanabi. However, this type of reasoning is less often observed in long-horizon, continuous domains with high-dimensional observations, where instead RL research has predominantly focused on problems with simple high-level structure (e.g. opening a drawer or moving a robot as fast as possible). Inspired by combinatorially hard optimization problems, we propose a set of robotics tasks which admit many distinct solutions at the high-level, but require reasoning about states and rewards thousands of steps into the future for the best performance. Critically, while RL has traditionally suffered on complex, long-horizon tasks due to sparse rewards, our tasks are carefully designed to be solvable without specialized exploration. Nevertheless, our investigation finds that standard RL methods often neglect long-term effects due to discounting, while general-purpose hierarchical RL approaches struggle unless additional abstract domain knowledge can be exploited.
Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength
Authors: Jiageng Zhu, Hanchen Xie, Wael AbdAlmageed
Date: 2022-06-03 00:00:00
Tasks: Representation Learning

Causal representation learning has been proposed to encode relationships between factors presented in the high dimensional data. However, existing methods suffer from merely using a large amount of labeled data and ignore the fact that samples generated by the same causal mechanism follow the same causal relationships. In this paper, we seek to explore such information by leveraging do-operation to reduce supervision strength. We propose a framework that implements do-operation by swapping latent cause and effect factors encoded from a pair of inputs. Moreover, we also identify the inadequacy of existing causal representation metrics empirically and theoretically and introduce new metrics for better evaluation. Experiments conducted on both synthetic and real datasets demonstrate the superiorities of our method compared with state-of-the-art methods.
Radar Guided Dynamic Visual Attention for Resource-Efficient RGB Object Detection
Authors: Hemant Kumawat, Saibal Mukhopadhyay
Date: 2022-06-03 00:00:00
Tasks: Autonomous Vehicles, Object, object-detection, Object Detection

An autonomous system's perception engine must provide an accurate understanding of the environment for it to make decisions. Deep learning based object detection networks experience degradation in the performance and robustness for small and far away objects due to a reduction in object's feature map as we move to higher layers of the network. In this work, we propose a novel radar-guided spatial attention for RGB images to improve the perception quality of autonomous vehicles operating in a dynamic environment. In particular, our method improves the perception of small and long range objects, which are often not detected by the object detectors in RGB mode. The proposed method consists of two RGB object detectors, namely the Primary detector and a lightweight Secondary detector. The primary detector takes a full RGB image and generates primary detections. Next, the radar proposal framework creates regions of interest (ROIs) for object proposals by projecting the radar point cloud onto the 2D RGB image. These ROIs are cropped and fed to the secondary detector to generate secondary detections which are then fused with the primary detections via non-maximum suppression. This method helps in recovering the small objects by preserving the object's spatial features through an increase in their receptive field. We evaluate our fusion method on the challenging nuScenes dataset and show that our fusion method with SSD-lite as primary and secondary detector improves the baseline primary yolov3 detector's recall by 14% while requiring three times fewer computational resources.
[Re] Badder Seeds: Reproducing the Evaluation of Lexical Methods for Bias Measurement
Authors: Jille van der Togt, Lea Tiyavorabun, Matteo Rosati, Giulio Starace
Date: 2022-06-03 00:00:00

Combating bias in NLP requires bias measurement. Bias measurement is almost always achieved by using lexicons of seed terms, i.e. sets of words specifying stereotypes or dimensions of interest. This reproducibility study focuses on the original authors' main claim that the rationale for the construction of these lexicons needs thorough checking before usage, as the seeds used for bias measurement can themselves exhibit biases. The study aims to evaluate the reproducibility of the quantitative and qualitative results presented in the paper and the conclusions drawn thereof. We reproduce most of the results supporting the original authors' general claim: seed sets often suffer from biases that affect their performance as a baseline for bias metrics. Generally, our results mirror the original paper's. They are slightly different on select occasions, but not in ways that undermine the paper's general intent to show the fragility of seed sets.
Uncertainty Estimation in Machine Learning
Authors: Valentin Arkov
Date: 2022-06-03 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Learning Theory, regression

Most machine learning techniques are based upon statistical learning theory, often simplified for the sake of computing speed. This paper is focused on the uncertainty aspect of mathematical modeling in machine learning. Regression analysis is chosen to further investigate the evaluation aspect of uncertainty in model coefficients and, more importantly, in the output feature value predictions. A survey demonstrates major stages in the conventional least squares approach to the creation of the regression model, along with its uncertainty estimation. On the other hand, it is shown that in machine learning the model complexity and severe nonlinearity become serious obstacles to uncertainty evaluation. Furthermore, the process of machine model training demands high computing power, not available at the level of personal computers. This is why so-called pre-trained models are widely used in such areas of machine learning as natural language processing. The latest example of a pre-trained model is the Generative Pre-trained Transformer 3 with hundreds of billions of parameters and a half-terabyte training dataset. Similarly, mathematical models built from real data are growing in complexity which is accompanied by the growing amount of training data. However, when machine models and their predictions are used in decision-making, one needs to estimate uncertainty and evaluate accompanying risks. This problem could be resolved with non-parametric techniques at the expense of greater demand for computing power, which can be offered by modern supercomputers available, including those utilizing graphical and tensor processing units along with the conventional central processors.
Effects of Auxiliary Knowledge on Continual Learning
Authors: Giovanni Bellitto, Matteo Pennisi, Simone Palazzo, Lorenzo Bonicelli, Matteo Boschini, Simone Calderara, Concetto Spampinato
Date: 2022-06-03 00:00:00
Tasks: Continual Learning, image-classification, Image Classification

In Continual Learning (CL), a neural network is trained on a stream of data whose distribution changes over time. In this context, the main problem is how to learn new information without forgetting old knowledge (i.e., Catastrophic Forgetting). Most existing CL approaches focus on finding solutions to preserve acquired knowledge, so working on the past of the model. However, we argue that as the model has to continually learn new tasks, it is also important to put focus on the present knowledge that could improve following tasks learning. In this paper we propose a new, simple, CL algorithm that focuses on solving the current task in a way that might facilitate the learning of the next ones. More specifically, our approach combines the main data stream with a secondary, diverse and uncorrelated stream, from which the network can draw auxiliary knowledge. This helps the model from different perspectives, since auxiliary data may contain useful features for the current and the next tasks and incoming task classes can be mapped onto auxiliary classes. Furthermore, the addition of data to the current task is implicitly making the classifier more robust as we are forcing the extraction of more discriminative features. Our method can outperform existing state-of-the-art models on the most common CL Image Classification benchmarks.
Automatic Quantification of Volumes and Biventricular Function in Cardiac Resonance. Validation of a New Artificial Intelligence Approach
Authors: Ariel H. Curiale, MatÍas E. Calandrelli, Lucca Dellazoppa, Mariano Trevisan, Jorge Luis BociÁn, Juan Pablo Bonifacio, GermÁn Mato
Date: 2022-06-03 00:00:00

Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors. Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantification of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study. Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.
Detection of Fibrosis in Cine Magnetic Resonance Images Using Artificial Intelligence Techniques
Authors: Ariel. H. Curiale, Facundo Cabrera, Pablo Jimenez, Jorgelina Medus, GermÁn Mato, MatÍas E. Calandrelli
Date: 2022-06-03 00:00:00

Background: Artificial intelligence techniques have demonstrated great potential in cardiology, especially to detect imperceptible patterns for the human eye. In this sense, these techniques seem to be adequate to identify patterns in the myocardial texture which could lead to characterize and quantify fibrosis. Purpose: The aim of this study was to postulate a new artificial intelligence method to identify fibrosis in cine cardiac magnetic resonance (CMR) imaging. Methods: A retrospective observational study was carried out in a population of 75 subjects from a clinical center of San Carlos de Bariloche. The proposed method analyzes the myocardial texture in cine CMR images using a convolutional neural network to determine local myocardial tissue damage. Results: An accuracy of 89% for quantifying local tissue damage was observed for the validation data set and 70% for the test set. In addition, the qualitative analysis showed a high spatial correlation in lesion location. Conclusions: The postulated method enables to spatially identify fibrosis using only the information from cine nuclear magnetic resonance studies, demonstrating the potential of this technique to quantify myocardial viability in the future or to study the lesions etiology
On the duality between contrastive and non-contrastive self-supervised learning
Authors: Quentin Garrido, Yubei Chen, Adrien Bardes, Laurent Najman, Yann Lecun
Date: 2022-06-03 00:00:00
Tasks: Self-Supervised Learning

Recent approaches in self-supervised learning of image representations can be categorized into different families of methods and, in particular, can be divided into contrastive and non-contrastive approaches. While differences between the two families have been thoroughly discussed to motivate new approaches, we focus more on the theoretical similarities between them. By designing contrastive and covariance based non-contrastive criteria that can be related algebraically and shown to be equivalent under limited assumptions, we show how close those families can be. We further study popular methods and introduce variations of them, allowing us to relate this theoretical result to current practices and show the influence (or lack thereof) of design choices on downstream performance. Motivated by our equivalence result, we investigate the low performance of SimCLR and show how it can match VICReg's with careful hyperparameter tuning, improving significantly over known baselines. We also challenge the popular assumption that non-contrastive methods need large output dimensions. Our theoretical and quantitative results suggest that the numerical gaps between contrastive and non-contrastive methods in certain regimes can be closed given better network design choices and hyperparameter tuning. The evidence shows that unifying different SOTA methods is an important direction to build a better understanding of self-supervised learning.
Team VI-I2R Technical Report on EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021
Authors: Yi Cheng, Fen Fang, Ying Sun
Date: 2022-06-03 00:00:00
Tasks: Action Recognition, Domain Adaptation, Fine-grained Action Recognition, Optical Flow Estimation, Unsupervised Domain Adaptation

In this report, we present the technical details of our approach to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation (UDA) Challenge for Action Recognition. The EPIC-KITCHENS-100 dataset consists of daily kitchen activities focusing on the interaction between human hands and their surrounding objects. It is very challenging to accurately recognize these fine-grained activities, due to the presence of distracting objects and visually similar action classes, especially in the unlabelled target domain. Based on an existing method for video domain adaptation, i.e., TA3N, we propose to learn hand-centric features by leveraging the hand bounding box information for UDA on fine-grained action recognition. This helps reduce the distraction from background as well as facilitate the learning of domain-invariant features. To achieve high quality hand localization, we adopt an uncertainty-aware domain adaptation network, i.e., MEAA, to train a domain-adaptive hand detector, which only uses very limited hand bounding box annotations in the source domain but can generalize well to the unlabelled target domain. Our submission achieved the 1st place in terms of top-1 action recognition accuracy, using only RGB and optical flow modalities as input.
A Deep Reinforcement Learning Framework For Column Generation
Authors: Cheng Chi, Amine Mohamed Aboussalah, Elias B. Khalil, Juyoung Wang, Zoha Sherkat-Masoumi
Date: 2022-06-03 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Column Generation (CG) is an iterative algorithm for solving linear programs (LPs) with an extremely large number of variables (columns). CG is the workhorse for tackling large-scale \textit{integer} linear programs, which rely on CG to solve LP relaxations within a branch and price algorithm. Two canonical applications are the Cutting Stock Problem (CSP) and Vehicle Routing Problem with Time Windows (VRPTW). In VRPTW, for example, each binary variable represents the decision to include or exclude a \textit{route}, of which there are exponentially many; CG incrementally grows the subset of columns being used, ultimately converging to an optimal solution. We propose RLCG, the first Reinforcement Learning (RL) approach for CG. Unlike typical column selection rules which myopically select a column based on local information at each iteration, we treat CG as a sequential decision-making problem: the column selected in a given iteration affects subsequent column selections. This perspective lends itself to a Deep Reinforcement Learning approach that uses Graph Neural Networks (GNNs) to represent the variable-constraint structure in the LP of interest. We perform an extensive set of experiments using the publicly available BPPLIB benchmark for CSP and Solomon benchmark for VRPTW. RLCG converges faster and reduces the number of CG iterations by 22.4\% for CSP and 40.9\% for VRPTW on average compared to a commonly used greedy policy. Our code is available at https://github.com/chichengmessi/reinforcement-learning-for-column-generation.git.
Towards Group Learning: Distributed Weighting of Experts
Authors: Ben Abramowitz, Nicholas Mattei
Date: 2022-06-03 00:00:00
Tasks: Ensemble Learning, Federated Learning

Aggregating signals from a collection of noisy sources is a fundamental problem in many domains including crowd-sourcing, multi-agent planning, sensor networks, signal processing, voting, ensemble learning, and federated learning. The core question is how to aggregate signals from multiple sources (e.g. experts) in order to reveal an underlying ground truth. While a full answer depends on the type of signal, correlation of signals, and desired output, a problem common to all of these applications is that of differentiating sources based on their quality and weighting them accordingly. It is often assumed that this differentiation and aggregation is done by a single, accurate central mechanism or agent (e.g. judge). We complicate this model in two ways. First, we investigate the setting with both a single judge, and one with multiple judges. Second, given this multi-agent interaction of judges, we investigate various constraints on the judges' reporting space. We build on known results for the optimal weighting of experts and prove that an ensemble of sub-optimal mechanisms can perform optimally under certain conditions. We then show empirically that the ensemble approximates the performance of the optimal mechanism under a broader range of conditions.
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection
Authors: Linhai Ma, Liang Liang
Date: 2022-06-02 00:00:00
Tasks: Adversarial Robustness, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, object-detection, Object Detection, Semantic Segmentation

It is known that Deep Neural networks (DNNs) are vulnerable to adversarial attacks, and the adversarial robustness of DNNs could be improved by adding adversarial noises to training data (e.g., the standard adversarial training (SAT)). However, inappropriate noises added to training data may reduce a model's performance, which is termed the trade-off between accuracy and robustness. This problem has been sufficiently studied for the classification of whole images but has rarely been explored for image analysis tasks in the medical application domain, including image segmentation, landmark detection, and object detection tasks. In this study, we show that, for those medical image analysis tasks, the SAT method has a severe issue that limits its practical use: it generates a fixed and unified level of noise for all training samples for robust DNN training. A high noise level may lead to a large reduction in model performance and a low noise level may not be effective in improving robustness. To resolve this issue, we design an adaptive-margin adversarial training (AMAT) method that generates sample-wise adaptive adversarial noises for robust DNN training. In contrast to the existing, classification-oriented adversarial training methods, our AMAT method uses a loss-defined-margin strategy so that it can be applied to different tasks as long as the loss functions are well-defined. We successfully apply our AMAT method to state-of-the-art DNNs, using five publicly available datasets. The experimental results demonstrate that: (1) our AMAT method can be applied to the three seemingly different tasks in the medical image application domain; (2) AMAT outperforms the SAT method in adversarial robustness; (3) AMAT has a minimal reduction in prediction accuracy on clean data, compared with the SAT method; and (4) AMAT has almost the same training time cost as SAT.
Using UAS Imagery and Computer Vision to Support Site-Specific Weed Control in Corn
Authors: Ranjan Sapkota, Paulo Flores
Date: 2022-06-02 00:00:00

Currently, weed control in a corn field is performed by a blanket application of herbicides that do not consider spatial distribution information of weeds and also uses an extensive amount of chemical herbicides. To reduce the amount of chemicals, we used drone-based high-resolution imagery and computer-vision techniques to perform site-specific weed control in corn.
3D-Augmented Contrastive Knowledge Distillation for Image-based Object Pose Estimation
Authors: Zhidan Liu, Zhen Xing, Xiangdong Zhou, Yijiang Chen, Guichun Zhou
Date: 2022-06-02 00:00:00
Tasks: Contrastive Learning, Knowledge Distillation, Object, Pose Estimation

Image-based object pose estimation sounds amazing because in real applications the shape of object is oftentimes not available or not easy to take like photos. Although it is an advantage to some extent, un-explored shape information in 3D vision learning problem looks like "flaws in jade". In this paper, we deal with the problem in a reasonable new setting, namely 3D shape is exploited in the training process, and the testing is still purely image-based. We enhance the performance of image-based methods for category-agnostic object pose estimation by exploiting 3D knowledge learned by a multi-modal method. Specifically, we propose a novel contrastive knowledge distillation framework that effectively transfers 3D-augmented image representation from a multi-modal model to an image-based model. We integrate contrastive learning into the two-stage training procedure of knowledge distillation, which formulates an advanced solution to combine these two approaches for cross-modal tasks. We experimentally report state-of-the-art results compared with existing category-agnostic image-based methods by a large margin (up to +5% improvement on ObjectNet3D dataset), demonstrating the effectiveness of our method.
Binding Dancers Into Attractors
Authors: Franziska Kaltenberger, Sebastian Otte, Martin V. Butz
Date: 2022-06-01 00:00:00

To effectively perceive and process observations in our environment, feature binding and perspective taking are crucial cognitive abilities. Feature binding combines observed features into one entity, called a Gestalt. Perspective taking transfers the percept into a canonical, observer-centered frame of reference. Here we propose a recurrent neural network model that solves both challenges. We first train an LSTM to predict 3D motion dynamics from a canonical perspective. We then present similar motion dynamics with novel viewpoints and feature arrangements. Retrospective inference enables the deduction of the canonical perspective. Combined with a robust mutual-exclusive softmax selection scheme, random feature arrangements are reordered and precisely bound into known Gestalt percepts. To corroborate evidence for the architecture's cognitive validity, we examine its behavior on the silhouette illusion, which elicits two competitive Gestalt interpretations of a rotating dancer. Our system flexibly binds the information of the rotating figure into the alternative attractors resolving the illusion's ambiguity and imagining the respective depth interpretation and the corresponding direction of rotation. We finally discuss the potential universality of the proposed mechanisms.