pwc-archive/papers-with-abstracts huggingface dataset:
RQR3D: Reparametrizing the regression targets for BEV-based 3D object detection
Authors: Ozsel Kilinc, Cem Tarhan
Date: 2025-05-23 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Object, object-detection, Object Detection, Oriented Object Detection, regression

Accurate, fast, and reliable 3D perception is essential for autonomous driving. Recently, bird's-eye view (BEV)-based perception approaches have emerged as superior alternatives to perspective-based solutions, offering enhanced spatial understanding and more natural outputs for planning. Existing BEV-based 3D object detection methods, typically adhering to angle-based representation, directly estimate the size and orientation of rotated bounding boxes. We observe that BEV-based 3D object detection is analogous to aerial oriented object detection, where angle-based methods are recognized for being affected by discontinuities in their loss functions. Drawing inspiration from this domain, we propose Restricted Quadrilateral Representation to define 3D regression targets. RQR3D regresses the smallest horizontal bounding box encapsulating the oriented box, along with the offsets between the corners of these two boxes, thereby transforming the oriented object detection problem into a keypoint regression task. RQR3D is compatible with any 3D object detection approach. We employ RQR3D within an anchor-free single-stage object detection method and introduce an objectness head to address class imbalance problem. Furthermore, we introduce a simplified radar fusion backbone that eliminates the need for voxel grouping and processes the BEV-mapped point cloud with standard 2D convolutions, rather than sparse convolutions. Extensive evaluations on the nuScenes dataset demonstrate that RQR3D achieves state-of-the-art performance in camera-radar 3D object detection, outperforming the previous best method by +4% in NDS and +2.4% in mAP, and significantly reducing the translation and orientation errors, which are crucial for safe autonomous driving. These consistent gains highlight the robustness, precision, and real-world readiness of our approach.
PawPrint: Whose Footprints Are These? Identifying Animal Individuals by Their Footprints
Authors: Inpyo Song, Hyemin Hwang, JangWon Lee
Date: 2025-05-23 00:00:00
Tasks: Benchmarking

In the United States, as of 2023, pet ownership has reached 66% of households and continues to rise annually. This trend underscores the critical need for effective pet identification and monitoring methods, particularly as nearly 10 million cats and dogs are reported stolen or lost each year. However, traditional methods for finding lost animals like GPS tags or ID photos have limitations-they can be removed, face signal issues, and depend on someone finding and reporting the pet. To address these limitations, we introduce PawPrint and PawPrint+, the first publicly available datasets focused on individual-level footprint identification for dogs and cats. Through comprehensive benchmarking of both modern deep neural networks (e.g., CNN, Transformers) and classical local features, we observe varying advantages and drawbacks depending on substrate complexity and data availability. These insights suggest future directions for combining learned global representations with local descriptors to enhance reliability across diverse, real-world conditions. As this approach provides a non-invasive alternative to traditional ID tags, we anticipate promising applications in ethical pet management and wildlife conservation efforts.
LengthLogD: A Length-Stratified Ensemble Framework for Enhanced Peptide Lipophilicity Prediction via Multi-Scale Feature Integration
Authors: Shuang Wu, Meijie Wang, Lun Yu
Date: 2025-05-22 00:00:00
Tasks: Ensemble Learning, Prediction

Peptide compounds demonstrate considerable potential as therapeutic agents due to their high target affinity and low toxicity, yet their drug development is constrained by their low membrane permeability. Molecular weight and peptide length have significant effects on the logD of peptides, which in turn influences their ability to cross biological membranes. However, accurate prediction of peptide logD remains challenging due to the complex interplay between sequence, structure, and ionization states. This study introduces LengthLogD, a predictive framework that establishes specialized models through molecular length stratification while innovatively integrating multi-scale molecular representations. We constructed feature spaces across three hierarchical levels: atomic (10 molecular descriptors), structural (1024-bit Morgan fingerprints), and topological (3 graph-based features including Wiener index), optimized through stratified ensemble learning. An adaptive weight allocation mechanism specifically developed for long peptides significantly enhances model generalizability. Experimental results demonstrate superior performance across all categories: short peptides (R^2=0.855), medium peptides (R^2=0.816), and long peptides (R^2=0.882), with a 34.7% reduction in prediction error for long peptides compared to conventional single-model approaches. Ablation studies confirm: 1) The length-stratified strategy contributes 41.2% to performance improvement; 2) Topological features account for 28.5% of predictive importance. Compared to state-of-the-art models, our method maintains short peptide prediction accuracy while achieving a 25.7% increase in the coefficient of determination (R^2) for long peptides. This research provides a precise logD prediction tool for peptide drug development, particularly demonstrating unique value in optimizing long peptide lead compounds.
LPCM: Learning-based Predictive Coding for LiDAR Point Cloud Compression
Authors: Chang Sun, Hui Yuan, Shiqi Jiang, Da Ai, Wei zhang, Raouf Hamzaoui
Date: 2025-05-26 00:00:00
Tasks: Quantization

Since the data volume of LiDAR point clouds is very huge, efficient compression is necessary to reduce their storage and transmission costs. However, existing learning-based compression methods do not exploit the inherent angular resolution of LiDAR and ignore the significant differences in the correlation of geometry information at different bitrates. The predictive geometry coding method in the geometry-based point cloud compression (G-PCC) standard uses the inherent angular resolution to predict the azimuth angles. However, it only models a simple linear relationship between the azimuth angles of neighboring points. Moreover, it does not optimize the quantization parameters for residuals on each coordinate axis in the spherical coordinate system. We propose a learning-based predictive coding method (LPCM) with both high-bitrate and low-bitrate coding modes. LPCM converts point clouds into predictive trees using the spherical coordinate system. In high-bitrate coding mode, we use a lightweight Long-Short-Term Memory-based predictive (LSTM-P) module that captures long-term geometry correlations between different coordinates to efficiently predict and compress the elevation angles. In low-bitrate coding mode, where geometry correlation degrades, we introduce a variational radius compression (VRC) module to directly compress the point radii. Then, we analyze why the quantization of spherical coordinates differs from that of Cartesian coordinates and propose a differential evolution (DE)-based quantization parameter selection method, which improves rate-distortion performance without increasing coding time. Experimental results on the LiDAR benchmark \textit{SemanticKITTI} and the MPEG-specified \textit{Ford} datasets show that LPCM outperforms G-PCC and other learning-based methods.
Power allocation for cell-free MIMO integrated sensing and communication
Authors: Guoqing Xia, Pei Xiao, Qu Luo, Bing Ji, Yue Zhang, Huiyu Zhou
Date: 2025-05-26 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we investigate integrated sensing and communication (ISAC) in a cell-free (CF) multiple-input multiple-output (MIMO) network with single-antenna access points (APs), where each AP functions either as a transmitter for both sensing and communication or as a receiver for target-reflected signals. We derive closed-form Cramer-Rao lower bounds (CRLBs) for location and velocity estimation under arbitrary power allocation ratios, assuming the radar cross-section (RCS) is deterministic and unknown over the observation interval. A power allocation optimization problem is formulated to maximize the communication signal-to-interference-plus-noise ratio (SINR), subject to CRLB-based sensing constraints and per-transmitter power limits. To solve the resulting nonlinear and non-convex problem, we propose a penalty function and projection-based modified conjugate gradient algorithm with inexact line search (PP-MCG-ILS), and an alternative method based on a modified steepest descent approach (PP-MSD-ILS). Additionally, for power minimization in pure sensing scenarios, we introduce a penalty function-based normalized conjugate gradient algorithm (P-NCG-ILS). We analyze the convergence behavior and qualitatively compare the computational complexity of the proposed algorithms. Simulation results confirm the accuracy of the derived CRLBs and demonstrate the effectiveness of the proposed power allocation strategies in enhancing both sensing and overall ISAC performance.
The Study of Human Preference Based on Integrated Analysis of N1 and LPP Components
Authors: Siyuan Li, Xiangze Meng, Yijian Yang, Yiwen Xu, Yunfei Wang, Chenghu Qiu, Hanyi Jiang, Pin Wu, Shegnbo Chen, Xiao Wei, Hao Wang, Lan Ni, Huiran Zhang
Date: 2025-05-26 00:00:00
Tasks: Brain Computer Interface, EEG, ERP, Marketing

Human preference research is a significant domain in psychology and psychophysiology, with broad applications in psychiatric evaluation and daily life quality enhancement. This study explores the neural mechanisms of human preference judgments through the analysis of event-related potentials (ERPs), specifically focusing on the early N1 component and the late positive potential (LPP). Using a mixed-image dataset covering items such as hats, fruits, snacks, scarves, drinks, and pets, we elicited a range of emotional responses from participants while recording their brain activity via EEG. Our work innovatively combines the N1 and LPP components to reveal distinct patterns across different preference levels. The N1 component, particularly in frontal regions, showed increased amplitude for preferred items, indicating heightened early visual attention. Similarly, the LPP component exhibited larger amplitudes for both preferred and non-preferred items, reflecting deeper emotional engagement and cognitive evaluation. In addition, we introduced a relationship model that integrates these ERP components to assess the intensity and direction of preferences, providing a novel method for interpreting EEG data in the context of emotional responses. These findings offer valuable insights into the cognitive and emotional processes underlying human preferences and present new possibilities for brain-computer interface applications, personalized marketing, and product design.
Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation
Authors: Khellaf Rémi, Bellet Aurélien, Josse Julie
Date: 2025-05-23 00:00:00
Tasks: Causal Inference, Federated Learning

Causal inference typically assumes centralized access to individual-level data. Yet, in practice, data are often decentralized across multiple sites, making centralization infeasible due to privacy, logistical, or legal constraints. We address this by estimating the Average Treatment Effect (ATE) from decentralized observational data using federated learning, which enables inference through the exchange of aggregate statistics rather than individual-level data. We propose a novel method to estimate propensity scores in a (non-)parametric manner by computing a federated weighted average of local scores, using two theoretically grounded weighting schemes -- Membership Weights (MW) and Density Ratio Weights (DW) -- that balance communication efficiency and model flexibility. These federated scores are then used to construct two ATE estimators: the Federated Inverse Propensity Weighting estimator (Fed-IPW) and its augmented variant (Fed-AIPW). Unlike meta-analysis methods, which fail when any site violates positivity, our approach leverages heterogeneity in treatment assignment across sites to improve overlap. We show that Fed-IPW and Fed-AIPW perform well under site-level heterogeneity in sample sizes, treatment mechanisms, and covariate distributions, with theoretical analysis and experiments on simulated and real-world data highlighting their strengths and limitations relative to meta-analysis and related methods.
Evolving Machine Learning: A Survey
Authors: Ignacio Cabrera Martin, Subhaditya Mukherjee, Almas Baimagambetov, Joaquin Vanschoren, Nikolaos Polatidis
Date: 2025-05-23 00:00:00
Tasks: Meta-Learning, Survey

In an era defined by rapid data evolution, traditional machine learning (ML) models often fall short in adapting to dynamic environments. Evolving Machine Learning (EML) has emerged as a critical paradigm, enabling continuous learning and adaptation in real-time data streams. This survey presents a comprehensive analysis of EML, focusing on five core challenges: data drift, concept drift, catastrophic forgetting, skewed learning, and network adaptation. We systematically review over 120 studies, categorizing state-of-the-art methods across supervised, unsupervised, and semi-supervised approaches. The survey explores diverse evaluation metrics, benchmark datasets, and real-world applications, offering a comparative lens on the effectiveness and limitations of current techniques. Additionally, we highlight the growing role of adaptive neural architectures, meta-learning, and ensemble strategies in addressing evolving data complexities. By synthesizing insights from recent literature, this work not only maps the current landscape of EML but also identifies critical gaps and opportunities for future research. Our findings aim to guide researchers and practitioners in developing robust, ethical, and scalable EML systems for real-world deployment.
Continuous Learning for Children's ASR: Overcoming Catastrophic Forgetting with Elastic Weight Consolidation and Synaptic Intelligence
Authors: Edem Ahadzi, Vishwanath Pratap Singh, Tomi Kinnunen, Ville Hautamaki
Date: 2025-05-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In this work, we present the first study addressing automatic speech recognition (ASR) for children in an online learning setting. This is particularly important for both child-centric applications and the privacy protection of minors, where training models with sequentially arriving data is critical. The conventional approach of model fine-tuning often suffers from catastrophic forgetting. To tackle this issue, we explore two established techniques: elastic weight consolidation (EWC) and synaptic intelligence (SI). Using a custom protocol on the MyST corpus, tailored to the online learning setting, we achieve relative word error rate (WER) reductions of 5.21% with EWC and 4.36% with SI, compared to the fine-tuning baseline.
Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion
Authors: Zhanqiu Hu, Jian Meng, Yash Akhauri, Mohamed S. Abdelfattah, Jae-sun Seo, Zhiru Zhang, Udit Gupta
Date: 2025-05-27 00:00:00
Tasks: Denoising, Language Modeling, Language Modelling

Diffusion language models offer parallel token generation and inherent bidirectionality, promising more efficient and powerful sequence modeling compared to autoregressive approaches. However, state-of-the-art diffusion models (e.g., Dream 7B, LLaDA 8B) suffer from slow inference. While they match the quality of similarly sized Autoregressive (AR) Models (e.g., Qwen2.5 7B, Llama3 8B), their iterative denoising requires multiple full-sequence forward passes, resulting in high computational costs and latency, particularly for long input prompts and long-context scenarios. Furthermore, parallel token generation introduces token incoherence problems, and current sampling heuristics suffer from significant quality drops with decreasing denoising steps. We address these limitations with two training-free techniques. First, we propose FreeCache, a Key-Value (KV) approximation caching technique that reuses stable KV projections across denoising steps, effectively reducing the computational cost of DLM inference. Second, we introduce Guided Diffusion, a training-free method that uses a lightweight pretrained autoregressive model to supervise token unmasking, dramatically reducing the total number of denoising iterations without sacrificing quality. We conduct extensive evaluations on open-source reasoning benchmarks, and our combined methods deliver up to a 34x end-to-end speedup without compromising accuracy. For the first time, diffusion language models achieve a comparable and even faster latency as the widely adopted autoregressive models. Our work successfully paved the way for scaling up the diffusion language model to a broader scope of applications across different domains.
DeepInverse: A Python package for solving imaging inverse problems with deep learning
Authors: Julián Tachella, Matthieu Terris, Samuel Hurault, Andrew Wang, Dongdong Chen, Minh-Hai Nguyen, Maxime Song, Thomas Davies, Leo Davy, Jonathan Dong, Paul Escande, Johannes Hertrich, Zhiyuan Hu, Tobías I. Liaudat, Nils Laurent, Brett Levac, Mathurin Massias, Thomas Moreau, Thibaut Modrzyk, Brayan Monroy, Sebastian Neumayer, Jérémy Scanvic, Florian Sarron, Victor Sechaud, Georg Schramm, Romain Vo, Pierre Weiss
Date: 2025-05-26 00:00:00
Tasks: Image Reconstruction

DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems. The library covers all crucial steps in image reconstruction from the efficient implementation of forward operators (e.g., optics, MRI, tomography), to the definition and resolution of variational problems and the design and training of advanced neural network architectures. In this paper, we describe the main functionality of the library and discuss the main design choices.
SpikeGen: Generative Framework for Visual Spike Stream Processing
Authors: Gaole Dai, Menghang Dong, Rongyu Zhang, Ruichuan An, Shanghang Zhang, Tiejun Huang
Date: 2025-05-23 00:00:00
Tasks: Deblurring, Novel View Synthesis, Video Deblurring

Neuromorphic Visual Systems, such as spike cameras, have attracted considerable attention due to their ability to capture clear textures under dynamic conditions. This capability effectively mitigates issues related to motion and aperture blur. However, in contrast to conventional RGB modalities that provide dense spatial information, these systems generate binary, spatially sparse frames as a trade-off for temporally rich visual streams. In this context, generative models emerge as a promising solution to address the inherent limitations of sparse data. These models not only facilitate the conditional fusion of existing information from both spike and RGB modalities but also enable the conditional generation based on latent priors. In this study, we introduce a robust generative processing framework named SpikeGen, designed for visual spike streams captured by spike cameras. We evaluate this framework across multiple tasks involving mixed spike-RGB modalities, including conditional image/video deblurring, dense frame reconstruction from spike streams, and high-speed scene novel-view synthesis. Supported by comprehensive experimental results, we demonstrate that leveraging the latent space operation abilities of generative models allows us to effectively address the sparsity of spatial information while fully exploiting the temporal richness of spike streams, thereby promoting a synergistic enhancement of different visual modalities.
Clip4Retrofit: Enabling Real-Time Image Labeling on Edge Devices via Cross-Architecture CLIP Distillation
Authors: Li Zhong, Ahmed Ghazal, Jun-Jun Wan, Frederik Zilly, Patrick Mackens, Joachim E. Vollrath, Bogdan Sorin Coseriu
Date: 2025-05-23 00:00:00
Tasks: Autonomous Driving, cross-modal alignment, Edge-computing, Few-Shot Learning

Foundation models like CLIP (Contrastive Language-Image Pretraining) have revolutionized vision-language tasks by enabling zero-shot and few-shot learning through cross-modal alignment. However, their computational complexity and large memory footprint make them unsuitable for deployment on resource-constrained edge devices, such as in-car cameras used for image collection and real-time processing. To address this challenge, we propose Clip4Retrofit, an efficient model distillation framework that enables real-time image labeling on edge devices. The framework is deployed on the Retrofit camera, a cost-effective edge device retrofitted into thousands of vehicles, despite strict limitations on compute performance and memory. Our approach distills the knowledge of the CLIP model into a lightweight student model, combining EfficientNet-B3 with multi-layer perceptron (MLP) projection heads to preserve cross-modal alignment while significantly reducing computational requirements. We demonstrate that our distilled model achieves a balance between efficiency and performance, making it ideal for deployment in real-world scenarios. Experimental results show that Clip4Retrofit can perform real-time image labeling and object identification on edge devices with limited resources, offering a practical solution for applications such as autonomous driving and retrofitting existing systems. This work bridges the gap between state-of-the-art vision-language models and their deployment in resource-constrained environments, paving the way for broader adoption of foundation models in edge computing.
To Glue or Not to Glue? Classical vs Learned Image Matching for Mobile Mapping Cameras to Textured Semantic 3D Building Models
Authors: Simone Gaisbauer, Prabin Gyawali, Qilin Zhang, Olaf Wysocki, Boris Jutzi
Date: 2025-05-23 00:00:00
Tasks: Image Registration, Visual Localization

Feature matching is a necessary step for many computer vision and photogrammetry applications such as image registration, structure-from-motion, and visual localization. Classical handcrafted methods such as SIFT feature detection and description combined with nearest neighbour matching and RANSAC outlier removal have been state-of-the-art for mobile mapping cameras. With recent advances in deep learning, learnable methods have been introduced and proven to have better robustness and performance under complex conditions. Despite their growing adoption, a comprehensive comparison between classical and learnable feature matching methods for the specific task of semantic 3D building camera-to-model matching is still missing. This submission systematically evaluates the effectiveness of different feature-matching techniques in visual localization using textured CityGML LoD2 models. We use standard benchmark datasets (HPatches, MegaDepth-1500) and custom datasets consisting of facade textures and corresponding camera images (terrestrial and drone). For the latter, we evaluate the achievable accuracy of the absolute pose estimated using a Perspective-n-Point (PnP) algorithm, with geometric ground truth derived from geo-referenced trajectory data. The results indicate that the learnable feature matching methods vastly outperform traditional approaches regarding accuracy and robustness on our challenging custom datasets with zero to 12 RANSAC-inliers and zero to 0.16 area under the curve. We believe that this work will foster the development of model-based visual localization methods. Link to the code: https://github.com/simBauer/To\_Glue\_or\_not\_to\_Glue