pwc-archive/papers-with-abstracts huggingface dataset:
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight
Authors: Jiaxu Xing, Angel Romero, Leonard Bauersfeld, Davide Scaramuzza
Date: 2024-03-18 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning (RL)

Learning visuomotor policies for agile quadrotor flight presents significant difficulties, primarily from inefficient policy exploration caused by high-dimensional visual inputs and the need for precise and low-latency control. To address these challenges, we propose a novel approach that combines the performance of Reinforcement Learning (RL) and the sample efficiency of Imitation Learning (IL) in the task of vision-based autonomous drone racing. While RL provides a framework for learning high-performance controllers through trial and error, it faces challenges with sample efficiency and computational demands due to the high dimensionality of visual inputs. Conversely, IL efficiently learns from visual expert demonstrations, but it remains limited by the expert's performance and state distribution. To overcome these limitations, our policy learning framework integrates the strengths of both approaches. Our framework contains three phases: training a teacher policy using RL with privileged state information, distilling it into a student policy via IL, and adaptive fine-tuning via RL. Testing in both simulated and real-world scenarios shows our approach can not only learn in scenarios where RL from scratch fails but also outperforms existing IL methods in both robustness and performance, successfully navigating a quadrotor through a race course using only visual information. Videos of the experiments are available at https://rpg.ifi.uzh.ch/bootstrap-rl-with-il/index.html.
DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions
Authors: Yunxiao Shi, Manish Kumar Singh, Hong Cai, Fatih Porikli
Date: 2024-03-18 00:00:00
Proceeding: CVPR 2024 1
Tasks: Depth Completion

In this paper, we introduce a novel approach that harnesses both 2D and 3D attentions to enable highly accurate depth completion without requiring iterative spatial propagations. Specifically, we first enhance a baseline convolutional depth completion model by applying attention to 2D features in the bottleneck and skip connections. This effectively improves the performance of this simple network and sets it on par with the latest, complex transformer-based models. Leveraging the initial depths and features from this network, we uplift the 2D features to form a 3D point cloud and construct a 3D point transformer to process it, allowing the model to explicitly learn and exploit 3D geometric features. In addition, we propose normalization techniques to process the point cloud, which improves learning and leads to better accuracy than directly using point transformers off the shelf. Furthermore, we incorporate global attention on downsampled point cloud features, which enables long-range context while still being computationally feasible. We evaluate our method, DeCoTR, on established depth completion benchmarks, including NYU Depth V2 and KITTI, showcasing that it sets new state-of-the-art performance. We further conduct zero-shot evaluations on ScanNet and DDAD benchmarks and demonstrate that DeCoTR has superior generalizability compared to existing approaches.
FLex: Joint Pose and Dynamic Radiance Fields Optimization for Stereo Endoscopic Videos
Authors: Florian Philipp Stilz, Mert Asim Karaoglu, Felix Tristram, Nassir Navab, Benjamin Busam, Alexander Ladikos
Date: 2024-03-18 00:00:00
Tasks: Neural Rendering, Novel View Synthesis

Reconstruction of endoscopic scenes is an important asset for various medical applications, from post-surgery analysis to educational training. Neural rendering has recently shown promising results in endoscopic reconstruction with deforming tissue. However, the setup has been restricted to a static endoscope, limited deformation, or required an external tracking device to retrieve camera pose information of the endoscopic camera. With FLex we adress the challenging setup of a moving endoscope within a highly dynamic environment of deforming tissue. We propose an implicit scene separation into multiple overlapping 4D neural radiance fields (NeRFs) and a progressive optimization scheme jointly optimizing for reconstruction and camera poses from scratch. This improves the ease-of-use and allows to scale reconstruction capabilities in time to process surgical videos of 5,000 frames and more; an improvement of more than ten times compared to the state of the art while being agnostic to external tracking information. Extensive evaluations on the StereoMIS dataset show that FLex significantly improves the quality of novel view synthesis while maintaining competitive pose accuracy.
The POLAR Traverse Dataset: A Dataset of Stereo Camera Images Simulating Traverses across Lunar Polar Terrain under Extreme Lighting Conditions
Authors: Margaret Hansen, Uland Wong, Terrence Fong
Date: 2024-03-18 00:00:00
Tasks: Position, Visual Odometry

We present the POLAR Traverse Dataset: a dataset of high-fidelity stereo pair images of lunar-like terrain under polar lighting conditions designed to simulate a straight-line traverse. Images from individual traverses with different camera heights and pitches were recorded at 1 m intervals by moving a suspended stereo bar across a test bed filled with regolith simulant and shaped to mimic lunar south polar terrain. Ground truth geometry and camera position information was also recorded. This dataset is intended for developing and testing software algorithms that rely on stereo or monocular camera images, such as visual odometry, for use in the lunar polar environment, as well as to provide insight into the expected lighting conditions in lunar polar regions.
A Systematic Review of Generalization Research in Medical Image Classification
Authors: Sarah Matta, Mathieu Lamard, Philippe Zhang, Alexandre Le Guilcher, Laurent Borderie, Béatrice Cochener, Gwenolé Quellec
Date: 2024-03-18 00:00:00
Tasks: Articles, Classification, Domain Adaptation, Domain Generalization, image-classification, Image Classification, Medical Image Analysis, Medical Image Classification

Numerous Deep Learning (DL) classification models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice. Despite early advances in DL model validation and implementation, which encourage healthcare institutions to adopt them, a fundamental questions remain: how can these models effectively handle domain shift? This question is crucial to limit DL models performance degradation. Medical data are dynamic and prone to domain shift, due to multiple factors. Two main shift types can occur over time: 1) covariate shift mainly arising due to updates to medical equipment and 2) concept shift caused by inter-grader variability. To mitigate the problem of domain shift, existing surveys mainly focus on domain adaptation techniques, with an emphasis on covariate shift. More generally, no work has reviewed the state-of-the-art solutions while focusing on the shift types. This paper aims to explore existing domain generalization methods for DL-based classification models through a systematic review of literature. It proposes a taxonomy based on the shift type they aim to solve. Papers were searched and gathered on Scopus till 10 April 2023, and after the eligibility screening and quality evaluation, 77 articles were identified. Exclusion criteria included: lack of methodological novelty (e.g., reviews, benchmarks), experiments conducted on a single mono-center dataset, or articles not written in English. The results of this paper show that learning based methods are emerging, for both shift types. Finally, we discuss future challenges, including the need for improved evaluation protocols and benchmarks, and envisioned future developments to achieve robust, generalized models for medical image classification.
ThermoNeRF: Joint RGB and Thermal Novel View Synthesis for Building Facades using Multimodal Neural Radiance Fields
Authors: Mariam Hassan, Florent Forest, Olga Fink, Malcolm Mielle
Date: 2024-03-18 00:00:00
Tasks: 3D geometry, Image Generation, NeRF, Novel View Synthesis

Thermal scene reconstruction holds great potential for various applications, such as analyzing building energy consumption and performing non-destructive infrastructure testing. However, existing methods typically require dense scene measurements and often rely on RGB images for 3D geometry reconstruction, projecting thermal information post-reconstruction. This can lead to inconsistencies between the reconstructed geometry and temperature data and their actual values. To address this challenge, we propose ThermoNeRF, a novel multimodal approach based on Neural Radiance Fields that jointly renders new RGB and thermal views of a scene, and ThermoScenes, a dataset of paired RGB+thermal images comprising 8 scenes of building facades and 8 scenes of everyday objects. To address the lack of texture in thermal images, ThermoNeRF uses paired RGB and thermal images to learn scene density, while separate networks estimate color and temperature data. Unlike comparable studies, our focus is on temperature reconstruction and experimental results demonstrate that ThermoNeRF achieves an average mean absolute error of 1.13C and 0.41C for temperature estimation in buildings and other scenes, respectively, representing an improvement of over 50% compared to using concatenated RGB+thermal data as input to a standard NeRF. Code and dataset are available online.
Development of Automated Neural Network Prediction for Echocardiographic Left ventricular Ejection Fraction
Authors: Yuting Zhang, Boyang Liu, Karina V. Bunting, David Brind, Alexander Thorley, Andreas Karwath, Wenqi Lu, Diwei Zhou, Xiaoxia Wang, Alastair R. Mobley, Otilia Tica, Georgios Gkoutos, Dipak Kotecha, Jinming Duan
Date: 2024-03-18 00:00:00
Tasks: Ensemble Learning

The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF). In order to quantify LVEF automatically and accurately, this paper proposes a new pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey's method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline's accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF. This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson's correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p<0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment. This study demonstrates that an automated neural network-based calculation of LVEF is comparable to expert clinicians performing time-consuming, frame-by-frame manual evaluation of cardiac systolic function.
N-Modal Contrastive Losses with Applications to Social Media Data in Trimodal Space
Authors: William Theisen, Walter Scheirer
Date: 2024-03-18 00:00:00

The social media landscape of conflict dynamics has grown increasingly multi-modal. Recent advancements in model architectures such as CLIP have enabled researchers to begin studying the interplay between the modalities of text and images in a shared latent space. However, CLIP models fail to handle situations on social media when modalities present in a post expand above two. Social media dynamics often require understanding the interplay between not only text and images, but video as well. In this paper we explore an extension of the contrastive loss function to allow for any number of modalities, and demonstrate its usefulness in trimodal spaces on social media. By extending CLIP into three dimensions we can further aide understanding social media landscapes where all three modalities are present (an increasingly common situation). We use a newly collected public data set of Telegram posts containing all three modalities to train, and then demonstrate the usefulness of, a trimodal model in two OSINT scenarios: classifying a social media artifact post as either pro-Russian or pro-Ukrainian and identifying which account a given artifact originated from. While trimodal CLIP models have been explored before (though not on social media data), we also display a novel quadmodal CLIP model. This model can learn the interplay between text, image, video, and audio. We demonstrate new state-of-the-art baseline results on retrieval for quadmodel models moving forward.
Deep learning automates Cobb angle measurement compared with multi-expert observers
Authors: Keyu Li, Hanxue Gu, Roy Colglazier, Robert Lark, Elizabeth Hubbard, Robert French, Denise Smith, Jikai Zhang, Erin McCrum, Anthony Catanzano, Joseph Cao, Leah Waldman, Maciej A. Mazurowski, Benjamin Alman
Date: 2024-03-18 00:00:00

Scoliosis, a prevalent condition characterized by abnormal spinal curvature leading to deformity, requires precise assessment methods for effective diagnosis and management. The Cobb angle is a widely used scoliosis quantification method that measures the degree of curvature between the tilted vertebrae. Yet, manual measuring of Cobb angles is time-consuming and labor-intensive, fraught with significant interobserver and intraobserver variability. To address these challenges and the lack of interpretability found in certain existing automated methods, we have created fully automated software that not only precisely measures the Cobb angle but also provides clear visualizations of these measurements. This software integrates deep neural network-based spine region detection and segmentation, spine centerline identification, pinpointing the most significantly tilted vertebrae, and direct visualization of Cobb angles on the original images. Upon comparison with the assessments of 7 expert readers, our algorithm exhibited a mean deviation in Cobb angle measurements of 4.17 degrees, notably surpassing the manual approach's average intra-reader discrepancy of 5.16 degrees. The algorithm also achieved intra-class correlation coefficients (ICC) exceeding 0.96 and Pearson correlation coefficients above 0.944, reflecting robust agreement with expert assessments and superior measurement reliability. Through the comprehensive reader study and statistical analysis, we believe this algorithm not only ensures a higher consensus with expert readers but also enhances interpretability and reproducibility during assessments. It holds significant promise for clinical application, potentially aiding physicians in more accurate scoliosis assessment and diagnosis, thereby improving patient care.
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Authors: Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, QIngwei Lin, Victor Rühle, Yuqing Yang, Chin-Yew Lin, H. Vicky Zhao, Lili Qiu, Dongmei Zhang
Date: 2024-03-19 00:00:00
Tasks: GSM8K, Language Modelling, Text Compression, token-classification, Token Classification

This paper focuses on task-agnostic prompt compression for better generalizability and efficiency. Considering the redundancy in natural language, existing approaches compress prompts by removing tokens or lexical units according to their information entropy obtained from a causal language model such as LLaMa-7B. The challenge is that information entropy may be a suboptimal compression metric: (i) it only leverages unidirectional context and may fail to capture all essential information needed for prompt compression; (ii) it is not aligned with the prompt compression objective. To address these issues, we propose a data distillation procedure to derive knowledge from an LLM to compress prompts without losing crucial information, and meantime, introduce an extractive text compression dataset. We formulate prompt compression as a token classification problem to guarantee the faithfulness of the compressed prompt to the original one, and use a Transformer encoder as the base architecture to capture all essential information for prompt compression from the full bidirectional context. Our approach leads to lower latency by explicitly learning the compression objective with smaller models such as XLM-RoBERTa-large and mBERT. We evaluate our method on both in-domain and out-of-domain datasets, including MeetingBank, LongBench, ZeroScrolls, GSM8K, and BBH. Despite its small size, our model shows significant performance gains over strong baselines and demonstrates robust generalization ability across different LLMs. Additionally, our model is 3x-6x faster than existing prompt compression methods, while accelerating the end-to-end latency by 1.6x-2.9x with compression ratios of 2x-5x. Our code is available at https://aka.ms/LLMLingua-2.
Non-Stationary Dueling Bandits Under a Weighted Borda Criterion
Authors: Joe Suk, Arpit Agarwal
Date: 2024-03-19 00:00:00

In $K$-armed dueling bandits, the learner receives preference feedback between arms, and the regret of an arm is defined in terms of its suboptimality to a $\textit{winner}$ arm. The $\textit{non-stationary}$ variant of the problem, motivated by concerns of changing user preferences, has received recent interest (Saha and Gupta, 2022; Buening and Saha, 2023; Suk and Agarwal, 2023). The goal here is to design algorithms with low {\em dynamic regret}, ideally without foreknowledge of the amount of change. The notion of regret here is tied to a notion of winner arm, most typically taken to be a so-called Condorcet winner or a Borda winner. However, the aforementioned results mostly focus on the Condorcet winner. In comparison, the Borda version of this problem has received less attention which is the focus of this work. We establish the first optimal and adaptive dynamic regret upper bound $\tilde{O}(\tilde{L}^{1/3} K^{1/3} T^{2/3} )$, where $\tilde{L}$ is the unknown number of significant Borda winner switches. We also introduce a novel $\textit{weighted Borda score}$ framework which generalizes both the Borda and Condorcet problems. This framework surprisingly allows a Borda-style regret analysis of the Condorcet problem and establishes improved bounds over the theoretical state-of-art in regimes with a large number of arms or many spurious changes in Condorcet winner. Such a generalization was not known and could be of independent interest.
On Safety in Safe Bayesian Optimization
Authors: Christian Fiedler, Johanna Menn, Lukas Kreisköther, Sebastian Trimpe
Date: 2024-03-19 00:00:00
Tasks: Bayesian Optimization

Optimizing an unknown function under safety constraints is a central task in robotics, biomedical engineering, and many other disciplines, and increasingly safe Bayesian Optimization (BO) is used for this. Due to the safety critical nature of these applications, it is of utmost importance that theoretical safety guarantees for these algorithms translate into the real world. In this work, we investigate three safety-related issues of the popular class of SafeOpt-type algorithms. First, these algorithms critically rely on frequentist uncertainty bounds for Gaussian Process (GP) regression, but concrete implementations typically utilize heuristics that invalidate all safety guarantees. We provide a detailed analysis of this problem and introduce Real-\b{eta}-SafeOpt, a variant of the SafeOpt algorithm that leverages recent GP bounds and thus retains all theoretical guarantees. Second, we identify assuming an upper bound on the reproducing kernel Hilbert space (RKHS) norm of the target function, a key technical assumption in SafeOpt-like algorithms, as a central obstacle to real-world usage. To overcome this challenge, we introduce the Lipschitz-only Safe Bayesian Optimization (LoSBO) algorithm, which guarantees safety without an assumption on the RKHS bound, and empirically show that this algorithm is not only safe, but also exhibits superior performance compared to the state-of-the-art on several function classes. Third, SafeOpt and derived algorithms rely on a discrete search space, making them difficult to apply to higher-dimensional problems. To widen the applicability of these algorithms, we introduce Lipschitz-only GP-UCB (LoS-GP-UCB), a variant of LoSBO applicable to moderately high-dimensional problems, while retaining safety.
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes
Authors: He Wang, Laixi Shi, Yuejie Chi
Date: 2024-03-19 00:00:00
Tasks: Reinforcement Learning (RL)

In offline reinforcement learning (RL), the absence of active exploration calls for attention on the model robustness to tackle the sim-to-real gap, where the discrepancy between the simulated and deployed environments can significantly undermine the performance of the learned policy. To endow the learned policy with robustness in a sample-efficient manner in the presence of high-dimensional state-action space, this paper considers the sample complexity of distributionally robust linear Markov decision processes (MDPs) with an uncertainty set characterized by the total variation distance using offline data. We develop a pessimistic model-based algorithm and establish its sample complexity bound under minimal data coverage assumptions, which outperforms prior art by at least $\widetilde{O}(d)$, where $d$ is the feature dimension. We further improve the performance guarantee of the proposed algorithm by incorporating a carefully-designed variance estimator.
Neural Differential Algebraic Equations
Authors: James Koch, Madelyn Shapiro, Himanshu Sharma, Draguna Vrabie, Jan Drgona
Date: 2024-03-19 00:00:00

Differential-Algebraic Equations (DAEs) describe the temporal evolution of systems that obey both differential and algebraic constraints. Of particular interest are systems that contain implicit relationships between their components, such as conservation relationships. Here, we present Neural Differential-Algebraic Equations (NDAEs) suitable for data-driven modeling of DAEs. This methodology is built upon the concept of the Universal Differential Equation; that is, a model constructed as a system of Neural Ordinary Differential Equations informed by theory from particular science domains. In this work, we show that the proposed NDAEs abstraction is suitable for relevant system-theoretic data-driven modeling tasks. Presented examples include (i) the inverse problem of tank-manifold dynamics and (ii) discrepancy modeling of a network of pumps, tanks, and pipes. Our experiments demonstrate the proposed method's robustness to noise and extrapolation ability to (i) learn the behaviors of the system components and their interaction physics and (ii) disambiguate between data trends and mechanistic relationships contained in the system.