pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal Dataset for Localization, Mapping and Crop Monitoring in Citrus Tree Farms
Authors: Hanzhe Teng, Yipeng Wang, Xiaoao Song, Konstantinos Karydis
Date: 2023-09-27 00:00:00
Tasks: object-detection, Object Detection, Scene Understanding

In this work we introduce the CitrusFarm dataset, a comprehensive multimodal sensory dataset collected by a wheeled mobile robot operating in agricultural fields. The dataset offers stereo RGB images with depth information, as well as monochrome, near-infrared and thermal images, presenting diverse spectral responses crucial for agricultural research. Furthermore, it provides a range of navigational sensor data encompassing wheel odometry, LiDAR, inertial measurement unit (IMU), and GNSS with Real-Time Kinematic (RTK) as the centimeter-level positioning ground truth. The dataset comprises seven sequences collected in three fields of citrus trees, featuring various tree species at different growth stages, distinctive planting patterns, as well as varying daylight conditions. It spans a total operation time of 1.7 hours, covers a distance of 7.5 km, and constitutes 1.3 TB of data. We anticipate that this dataset can facilitate the development of autonomous robot systems operating in agricultural tree environments, especially for localization, mapping and crop monitoring tasks. Moreover, the rich sensing modalities offered in this dataset can also support research in a range of robotics and computer vision tasks, such as place recognition, scene understanding, object detection and segmentation, and multimodal learning. The dataset, in conjunction with related tools and resources, is made publicly available at https://github.com/UCR-Robotics/Citrus-Farm-Dataset.
BASED: Bundle-Adjusting Surgical Endoscopic Dynamic Video Reconstruction using Neural Radiance Fields
Authors: Shreya Saha, Zekai Liang, Shan Lin, Jingpei Lu, Michael Yip, Sainan Liu
Date: 2023-09-27 00:00:00
Tasks: NeRF, Video Reconstruction

Reconstruction of deformable scenes from endoscopic videos is important for many applications such as intraoperative navigation, surgical visual perception, and robotic surgery. It is a foundational requirement for realizing autonomous robotic interventions for minimally invasive surgery. However, previous approaches in this domain have been limited by their modular nature and are confined to specific camera and scene settings. Our work adopts the Neural Radiance Fields (NeRF) approach to learning 3D implicit representations of scenes that are both dynamic and deformable over time, and furthermore with unknown camera poses. We demonstrate this approach on endoscopic surgical scenes from robotic surgery. This work removes the constraints of known camera poses and overcomes the drawbacks of the state-of-the-art unstructured dynamic scene reconstruction technique, which relies on the static part of the scene for accurate reconstruction. Through several experimental datasets, we demonstrate the versatility of our proposed model to adapt to diverse camera and scene settings, and show its promise for both current and future robotic surgical systems.
Conversion of single-energy computed tomography to parametric maps of dual-energy computed tomography using convolutional neural network
Authors: SangWook Kim, JiMin Lee, Jungye Kim, Bitbyeol Kim, Chang Heon Choi, Seongmoon Jung
Date: 2023-09-26 00:00:00
Tasks: Multi-Task Learning

Objectives: We propose a deep learning (DL) multi-task learning framework using convolutional neural network (CNN) for a direct conversion of single-energy CT (SECT) to three different parametric maps of dual-energy CT (DECT): Virtual-monochromatic image (VMI), effective atomic number (EAN), and relative electron density (RED). Methods: We propose VMI-Net for conversion of SECT to 70, 120, and 200 keV VMIs. In addition, EAN-Net and RED-Net were also developed to convert SECT to EAN and RED. We trained and validated our model using 67 patients collected between 2019 and 2020. SECT images with 120 kVp acquired by the DECT (IQon spectral CT, Philips) were used as input, while the VMIs, EAN, and RED acquired by the same device were used as target. The performance of the DL framework was evaluated by absolute difference (AD) and relative difference (RD). Results: The VMI-Net converted 120 kVp SECT to the VMIs with AD of 9.02 Hounsfield Unit, and RD of 0.41% compared to the ground truth VMIs. The ADs of the converted EAN and RED were 0.29 and 0.96, respectively, while the RDs were 1.99% and 0.50% for the converted EAN and RED, respectively. Conclusions: SECT images were directly converted to the three parametric maps of DECT (i.e., VMIs, EAN, and RED). By using this model, one can generate the parametric information from SECT images without DECT device. Our model can help investigate the parametric information from SECT retrospectively. Advances in knowledge: Deep learning framework enables converting SECT to various high-quality parametric maps of DECT.
SEPT: Towards Efficient Scene Representation Learning for Motion Prediction
Authors: Zhiqian Lan, YuXuan Jiang, Yao Mu, Chen Chen, Shengbo Eben Li
Date: 2023-09-26 00:00:00
Tasks: Autonomous Vehicles, Feature Engineering, Motion Forecasting, motion prediction, Prediction, Representation Learning, Self-Supervised Learning

Motion prediction is crucial for autonomous vehicles to operate safely in complex traffic environments. Extracting effective spatiotemporal relationships among traffic elements is key to accurate forecasting. Inspired by the successful practice of pretrained large language models, this paper presents SEPT, a modeling framework that leverages self-supervised learning to develop powerful spatiotemporal understanding for complex traffic scenes. Specifically, our approach involves three masking-reconstruction modeling tasks on scene inputs including agents' trajectories and road network, pretraining the scene encoder to capture kinematics within trajectory, spatial structure of road network, and interactions among roads and agents. The pretrained encoder is then finetuned on the downstream forecasting task. Extensive experiments demonstrate that SEPT, without elaborate architectural design or manual feature engineering, achieves state-of-the-art performance on the Argoverse 1 and Argoverse 2 motion forecasting benchmarks, outperforming previous methods on all main metrics by a large margin.
Boosting High Resolution Image Classification with Scaling-up Transformers
Authors: Yi Wang
Date: 2023-09-26 00:00:00
Tasks: Classification, Data Augmentation, Diversity, image-classification, Image Classification, Transfer Learning

We present a holistic approach for high resolution image classification that won second place in the ICCV/CVPPA2023 Deep Nutrient Deficiency Challenge. The approach consists of a full pipeline of: 1) data distribution analysis to check potential domain shift, 2) backbone selection for a strong baseline model that scales up for high resolution input, 3) transfer learning that utilizes published pretrained models and continuous fine-tuning on small sub-datasets, 4) data augmentation for the diversity of training data and to prevent overfitting, 5) test-time augmentation to improve the prediction's robustness, and 6) "data soups" that conducts cross-fold model prediction average for smoothened final test results.
A Topological Machine Learning Pipeline for Classification
Authors: Francesco Conti, Davide Moroni, Maria Antonietta Pascali
Date: 2023-09-26 00:00:00
Tasks: Classification

In this work, we develop a pipeline that associates Persistence Diagrams to digital data via the most appropriate filtration for the type of data considered. Using a grid search approach, this pipeline determines optimal representation methods and parameters. The development of such a topological pipeline for Machine Learning involves two crucial steps that strongly affect its performance: firstly, digital data must be represented as an algebraic object with a proper associated filtration in order to compute its topological summary, the Persistence Diagram. Secondly, the persistence diagram must be transformed with suitable representation methods in order to be introduced in a Machine Learning algorithm. We assess the performance of our pipeline, and in parallel, we compare the different representation methods on popular benchmark datasets. This work is a first step toward both an easy and ready-to-use pipeline for data classification using persistent homology and Machine Learning, and to understand the theoretical reasons why, given a dataset and a task to be performed, a pair (filtration, topological representation) is better than another.
DECO: Dense Estimation of 3D Human-Scene Contact In The Wild
Authors: Shashank Tripathi, Agniv Chatterjee, Jean-Claude Passy, Hongwei Yi, Dimitrios Tzionas, Michael J. Black
Date: 2023-09-26 00:00:00
Proceeding: ICCV 2023 1
Tasks: Contact Detection, Dense contact estimation, Human-Object Interaction Detection

Understanding how humans use physical contact to interact with the world is key to enabling human-centric artificial intelligence. While inferring 3D contact is crucial for modeling realistic and physically-plausible human-object interactions, existing methods either focus on 2D, consider body joints rather than the surface, use coarse 3D body regions, or do not generalize to in-the-wild images. In contrast, we focus on inferring dense, 3D contact between the full body surface and objects in arbitrary images. To achieve this, we first collect DAMON, a new dataset containing dense vertex-level contact annotations paired with RGB images containing complex human-object and human-scene contact. Second, we train DECO, a novel 3D contact detector that uses both body-part-driven and scene-context-driven attention to estimate vertex-level contact on the SMPL body. DECO builds on the insight that human observers recognize contact by reasoning about the contacting body parts, their proximity to scene objects, and the surrounding scene context. We perform extensive evaluations of our detector on DAMON as well as on the RICH and BEHAVE datasets. We significantly outperform existing SOTA methods across all benchmarks. We also show qualitatively that DECO generalizes well to diverse and challenging real-world human interactions in natural images. The code, data, and models are available at https://deco.is.tue.mpg.de.
ObVi-SLAM: Long-Term Object-Visual SLAM
Authors: Amanda Adkins, Taijing Chen, Joydeep Biswas
Date: 2023-09-26 00:00:00
Tasks: Object, Visual Odometry

Robots responsible for tasks over long time scales must be able to localize consistently and scalably amid geometric, viewpoint, and appearance changes. Existing visual SLAM approaches rely on low-level feature descriptors that are not robust to such environmental changes and result in large map sizes that scale poorly over long-term deployments. In contrast, object detections are robust to environmental variations and lead to more compact representations, but most object-based SLAM systems target short-term indoor deployments with close objects. In this paper, we introduce ObVi-SLAM to overcome these challenges by leveraging the best of both approaches. ObVi-SLAM uses low-level visual features for high-quality short-term visual odometry; and to ensure global, long-term consistency, ObVi-SLAM builds an uncertainty-aware long-term map of persistent objects and updates it after every deployment. By evaluating ObVi-SLAM on data from 16 deployment sessions spanning different weather and lighting conditions, we empirically show that ObVi-SLAM generates accurate localization estimates consistent over long-time scales in spite of varying appearance conditions.
SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers
Authors: Daniel Silver, Tirthak Patel, Aditya Ranjan, Harshitta Gandhi, William Cutler, Devesh Tiwari
Date: 2023-09-26 00:00:00
Tasks: Quantum Machine Learning

Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs. However, these efforts have yet to solve unsupervised similarity detection tasks due to the challenge of porting them to run on quantum computers. To overcome this challenge, we propose SLIQ, the first open-sourced work for resource-efficient quantum similarity detection networks, built with practical and effective quantum learning and variance-reducing algorithms.
APIS: A paired CT-MRI dataset for ischemic stroke segmentation challenge
Authors: Santiago Gómez, Daniel Mantilla, Gustavo Garzón, Edgar Rangel, Andrés Ortiz, Franklin Sierra-Jerez, Fabio Martínez
Date: 2023-09-26 00:00:00
Tasks: Lesion Segmentation, Prognosis

Stroke is the second leading cause of mortality worldwide. Immediate attention and diagnosis play a crucial role regarding patient prognosis. The key to diagnosis consists in localizing and delineating brain lesions. Standard stroke examination protocols include the initial evaluation from a non-contrast CT scan to discriminate between hemorrhage and ischemia. However, non-contrast CTs may lack sensitivity in detecting subtle ischemic changes in the acute phase. As a result, complementary diffusion-weighted MRI studies are captured to provide valuable insights, allowing to recover and quantify stroke lesions. This work introduced APIS, the first paired public dataset with NCCT and ADC studies of acute ischemic stroke patients. APIS was presented as a challenge at the 20th IEEE International Symposium on Biomedical Imaging 2023, where researchers were invited to propose new computational strategies that leverage paired data and deal with lesion segmentation over CT sequences. Despite all the teams employing specialized deep learning tools, the results suggest that the ischemic stroke segmentation task from NCCT remains challenging. The annotated dataset remains accessible to the public upon registration, inviting the scientific community to deal with stroke characterization from NCCT but guided with paired DWI information.
CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process
Authors: Sapir Kontente, Roy Orfaig, Ben-Zion Bobrovsky
Date: 2023-09-26 00:00:00
Tasks: Autonomous Driving, Lane Detection

Lane detection plays a crucial role in autonomous driving by providing vital data to ensure safe navigation. Modern algorithms rely on anchor-based detectors, which are then followed by a label-assignment process to categorize training detections as positive or negative instances based on learned geometric attributes. Accurate label assignment has great impact on the model performance, that is usually relying on a pre-defined classical cost function evaluating GT-prediction alignment. However, classical label assignment methods face limitations due to their reliance on predefined cost functions derived from low-dimensional models, potentially impacting their optimality. Our research introduces MatchNet, a deep learning submodule-based approach aimed at improving the label assignment process. Integrated into a state-of-the-art lane detection network such as the Cross Layer Refinement Network for Lane Detection (CLRNet), MatchNet replaces the conventional label assignment process with a submodule network. The integrated model, CLRmatchNet, surpasses CLRNet, showing substantial improvements in scenarios involving curved lanes, with remarkable improvement across all backbones of +2.8% for ResNet34, +2.3% for ResNet101, and +2.96% for DLA34. In addition, it maintains or even improves comparable results in other sections. Our method boosts the confidence level in lane detection, allowing an increase in the confidence threshold. Our code is available at: https://github.com/sapirkontente/CLRmatchNet.git
Multi-unit soft sensing permits few-shot learning
Authors: Bjarne Grimstad, Kristian Løvland, Lars S. Imsland
Date: 2023-09-27 00:00:00
Tasks: Few-Shot Learning

Recent literature has explored various ways to improve soft sensors by utilizing learning algorithms with transferability. A performance gain is generally attained when knowledge is transferred among strongly related soft sensor learning tasks. A particularly relevant case for transferability is when developing soft sensors of the same type for similar, but physically different processes or units. Then, the data from each unit presents a soft sensor learning task, and it is reasonable to expect strongly related tasks. Applying methods that exploit transferability in this setting leads to what we call multi-unit soft sensing. This paper formulates multi-unit soft sensing as a probabilistic, hierarchical model, which we implement using a deep neural network. The learning capabilities of the model are studied empirically on a large-scale industrial case by developing virtual flow meters (a type of soft sensor) for 80 petroleum wells. We investigate how the model generalizes with the number of wells/units. Interestingly, we demonstrate that multi-unit models learned from data from many wells, permit few-shot learning of virtual flow meters for new wells. Surprisingly, regarding the difficulty of the tasks, few-shot learning on 1-3 data points often leads to high performance on new wells.
Large Language Model Routing with Benchmark Datasets
Authors: Tal Shnitzer, Anthony Ou, Mírian Silva, Kate Soule, Yuekai Sun, Justin Solomon, Neil Thompson, Mikhail Yurochkin
Date: 2023-09-27 00:00:00
Tasks: Binary Classification, Language Modeling, Language Modelling, Large Language Model, model

There is a rapidly growing number of open-source Large Language Models (LLMs) and benchmark datasets to compare them. While some models dominate these benchmarks, no single model typically achieves the best accuracy in all tasks and use cases. In this work, we address the challenge of selecting the best LLM out of a collection of models for new tasks. We propose a new formulation for the problem, in which benchmark datasets are repurposed to learn a "router" model for this LLM selection, and we show that this problem can be reduced to a collection of binary classification tasks. We demonstrate the utility and limitations of learning model routers from various benchmark datasets, where we consistently improve performance upon using any single model for all tasks.
Learning the Efficient Frontier
Date: 2023-09-27 00:00:00
Proceeding: NeurIPS 2023 11

The efficient frontier (EF) is a fundamental resource allocation problem where one has to find an optimal portfolio maximizing a reward at a given level of risk. This optimal solution is traditionally found by solving a convex optimization problem. In this paper, we introduce NeuralEF: a fast neural approximation framework that robustly forecasts the result of the EF convex optimization problem with respect to heterogeneous linear constraints and variable number of optimization inputs. By reformulating an optimization problem as a sequence to sequence problem, we show that NeuralEF is a viable solution to accelerate large-scale simulation while handling discontinuous behavior.
Importance-Weighted Offline Learning Done Right
Authors: Germano Gabbianelli, Gergely Neu, Matteo Papini
Date: 2023-09-27 00:00:00
Tasks: All

We study the problem of offline policy optimization in stochastic contextual bandit problems, where the goal is to learn a near-optimal policy based on a dataset of decision data collected by a suboptimal behavior policy. Rather than making any structural assumptions on the reward function, we assume access to a given policy class and aim to compete with the best comparator policy within this class. In this setting, a standard approach is to compute importance-weighted estimators of the value of each policy, and select a policy that minimizes the estimated value up to a "pessimistic" adjustment subtracted from the estimates to reduce their random fluctuations. In this paper, we show that a simple alternative approach based on the "implicit exploration" estimator of \citet{Neu2015} yields performance guarantees that are superior in nearly all possible terms to all previous results. Most notably, we remove an extremely restrictive "uniform coverage" assumption made in all previous works. These improvements are made possible by the observation that the upper and lower tails importance-weighted estimators behave very differently from each other, and their careful control can massively improve on previous results that were all based on symmetric two-sided concentration inequalities. We also extend our results to infinite policy classes in a PAC-Bayesian fashion, and showcase the robustness of our algorithm to the choice of hyper-parameters by means of numerical simulations.
Algebraic and Statistical Properties of the Ordinary Least Squares Interpolator
Authors: Dennis Shen, Dogyoon Song, Peng Ding, Jasjeet S. Sekhon
Date: 2023-09-27 00:00:00
Tasks: Causal Inference

Deep learning research has uncovered the phenomenon of benign overfitting for overparameterized statistical models, which has drawn significant theoretical interest in recent years. Given its simplicity and practicality, the ordinary least squares (OLS) interpolator has become essential to gain foundational insights into this phenomenon. While properties of OLS are well established in classical, underparameterized settings, its behavior in high-dimensional, overparameterized regimes is less explored (unlike for ridge or lasso regression) though significant progress has been made of late. We contribute to this growing literature by providing fundamental algebraic and statistical results for the minimum $\ell_2$-norm OLS interpolator. In particular, we provide algebraic equivalents of (i) the leave-$k$-out residual formula, (ii) Cochran's formula, and (iii) the Frisch-Waugh-Lovell theorem in the overparameterized regime. These results aid in understanding the OLS interpolator's ability to generalize and have substantive implications for causal inference. Under the Gauss-Markov model, we present statistical results such as an extension of the Gauss-Markov theorem and an analysis of variance estimation under homoskedastic errors for the overparameterized regime. To substantiate our theoretical contributions, we conduct simulations that further explore the stochastic properties of the OLS interpolator.
Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need
Authors: Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein
Date: 2023-09-27 00:00:00
Tasks: All, Efficient Exploration

We present a new algorithm based on posterior sampling for learning in constrained Markov decision processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achieves near-optimal regret bounds while being advantageous empirically compared to the existing algorithms. Our main theoretical result is a Bayesian regret bound for each cost component of \tilde{O} (HS \sqrt{AT}) for any communicating CMDP with S states, A actions, and bound on the hitting time H. This regret bound matches the lower bound in order of time horizon T and is the best-known regret bound for communicating CMDPs in the infinite-horizon undiscounted setting. Empirical results show that, despite its simplicity, our posterior sampling algorithm outperforms the existing algorithms for constrained reinforcement learning.