pwc-archive/papers-with-abstracts huggingface dataset:
3D Skeleton-Based Action Recognition: A Review
Authors: Mengyuan Liu, Hong Liu, Qianshuo Hu, Bin Ren, Junsong Yuan, Jiaying Lin, Jiajun Wen
Date: 2025-06-01 00:00:00
Tasks: Action Recognition, Data Augmentation, Mamba, Skeleton Based Action Recognition

With the inherent advantages of skeleton representation, 3D skeleton-based action recognition has become a prominent topic in the field of computer vision. However, previous reviews have predominantly adopted a model-oriented perspective, often neglecting the fundamental steps involved in skeleton-based action recognition. This oversight tends to ignore key components of skeleton-based action recognition beyond model design and has hindered deeper, more intrinsic understanding of the task. To bridge this gap, our review aims to address these limitations by presenting a comprehensive, task-oriented framework for understanding skeleton-based action recognition. We begin by decomposing the task into a series of sub-tasks, placing particular emphasis on preprocessing steps such as modality derivation and data augmentation. The subsequent discussion delves into critical sub-tasks, including feature extraction and spatio-temporal modeling techniques. Beyond foundational action recognition networks, recently advanced frameworks such as hybrid architectures, Mamba models, large language models (LLMs), and generative models have also been highlighted. Finally, a comprehensive overview of public 3D skeleton datasets is presented, accompanied by an analysis of state-of-the-art algorithms evaluated on these benchmarks. By integrating task-oriented discussions, comprehensive examinations of sub-tasks, and an emphasis on the latest advancements, our review provides a fundamental and accessible structured roadmap for understanding and advancing the field of 3D skeleton-based action recognition.
Recent Advances in Medical Image Classification
Authors: Loan Dao, Ngoc Quoc Ly
Date: 2025-06-04 00:00:00
Tasks: Classification, Explainable artificial intelligence, image-classification, Image Classification, Medical Image Classification

Medical image classification is crucial for diagnosis and treatment, benefiting significantly from advancements in artificial intelligence. The paper reviews recent progress in the field, focusing on three levels of solutions: basic, specific, and applied. It highlights advances in traditional methods using deep learning models like Convolutional Neural Networks and Vision Transformers, as well as state-of-the-art approaches with Vision Language Models. These models tackle the issue of limited labeled data, and enhance and explain predictive results through Explainable Artificial Intelligence.
Trajectory Prediction Meets Large Language Models: A Survey
Authors: Yi Xu, Ruining Yang, Yitian Zhang, Yizhou Wang, Jianglin Lu, Mingyuan Zhang, Lili Su, Yun Fu
Date: 2025-06-03 00:00:00
Tasks: Language Modeling, Language Modelling, Prediction, Scene Understanding, Survey, Trajectory Prediction

Recent advances in large language models (LLMs) have sparked growing interest in integrating language-driven techniques into trajectory prediction. By leveraging their semantic and reasoning capabilities, LLMs are reshaping how autonomous systems perceive, model, and predict trajectories. This survey provides a comprehensive overview of this emerging field, categorizing recent work into five directions: (1) Trajectory prediction via language modeling paradigms, (2) Direct trajectory prediction with pretrained language models, (3) Language-guided scene understanding for trajectory prediction, (4) Language-driven data generation for trajectory prediction, (5) Language-based reasoning and interpretability for trajectory prediction. For each, we analyze representative methods, highlight core design choices, and identify open challenges. This survey bridges natural language processing and trajectory prediction, offering a unified perspective on how language can enrich trajectory prediction.
A Survey of Deep Learning Video Super-Resolution
Authors: Arbind Agrahari Baniya, Tsz-Kwan Lee, Peter Eklund, Sunil Aryal
Date: 2025-06-03 00:00:00
Tasks: Deep Learning, Super-Resolution, Survey, Video Super-Resolution

Video super-resolution (VSR) is a prominent research topic in low-level computer vision, where deep learning technologies have played a significant role. The rapid progress in deep learning and its applications in VSR has led to a proliferation of tools and techniques in the literature. However, the usage of these methods is often not adequately explained, and decisions are primarily driven by quantitative improvements. Given the significance of VSR's potential influence across multiple domains, it is imperative to conduct a comprehensive analysis of the elements and deep learning methodologies employed in VSR research. This methodical analysis will facilitate the informed development of models tailored to specific application needs. In this paper, we present an overarching overview of deep learning-based video super-resolution models, investigating each component and discussing its implications. Furthermore, we provide a synopsis of key components and technologies employed by state-of-the-art and earlier VSR models. By elucidating the underlying methodologies and categorising them systematically, we identified trends, requirements, and challenges in the domain. As a first-of-its-kind survey of deep learning-based VSR models, this work also establishes a multi-level taxonomy to guide current and future VSR research, enhancing the maturation and interpretation of VSR practices for various practical applications.
Towards Geometry Problem Solving in the Large Model Era: A Survey
Authors: Yurui Zhao, Xiang Wang, Jiahong Liu, Irwin King, Zhitao Huang
Date: 2025-06-03 00:00:00
Tasks: Geometry Problem Solving, Logical Reasoning

Geometry problem solving (GPS) represents a critical frontier in artificial intelligence, with profound applications in education, computer-aided design, and computational graphics. Despite its significance, automating GPS remains challenging due to the dual demands of spatial understanding and rigorous logical reasoning. Recent advances in large models have enabled notable breakthroughs, particularly for SAT-level problems, yet the field remains fragmented across methodologies, benchmarks, and evaluation frameworks. This survey systematically synthesizes GPS advancements through three core dimensions: (1) benchmark construction, (2) textual and diagrammatic parsing, and (3) reasoning paradigms. We further propose a unified analytical paradigm, assess current limitations, and identify emerging opportunities to guide future research toward human-level geometric reasoning, including automated benchmark generation and interpretable neuro-symbolic integration.
SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation
Authors: Siqi Chen, Xinyu Dong, Haolei Xu, Xingyu Wu, Fei Tang, Hang Zhang, Yuchen Yan, Linjuan Wu, Wenqi Zhang, Guiyang Hou, Yongliang Shen, Weiming Lu, Yueting Zhuang
Date: 2025-06-03 00:00:00
Tasks: Benchmarking, Style Transfer, Vector Graphics

Large Language Models (LLMs) and Multimodal LLMs have shown promising capabilities for SVG processing, yet existing benchmarks suffer from limited real-world coverage, lack of complexity stratification, and fragmented evaluation paradigms. We introduce SVGenius, a comprehensive benchmark comprising 2,377 queries across three progressive dimensions: understanding, editing, and generation. Built on real-world data from 24 application domains with systematic complexity stratification, SVGenius evaluates models through 8 task categories and 18 metrics. We assess 22 mainstream models spanning different scales, architectures, training paradigms, and accessibility levels. Our analysis reveals that while proprietary models significantly outperform open-source counterparts, all models exhibit systematic performance degradation with increasing complexity, indicating fundamental limitations in current approaches; however, reasoning-enhanced training proves more effective than pure scaling for overcoming these limitations, though style transfer remains the most challenging capability across all model types. SVGenius establishes the first systematic evaluation framework for SVG processing, providing crucial insights for developing more capable vector graphics models and advancing automated graphic design applications. Appendix and supplementary materials (including all data and code) are available at https://zju-real.github.io/SVGenius.
高比例新能源电力系统优化调度研究
Authors: 耿风翔，林建成，宋卿尧，王雷 ，吕雪峰
Date: 2024-06-28 00:00:00
Proceeding: 今日制造与升级 2024 6

新能源如风能和太阳能的出力具有很强的随机性和间歇性，这给电力系统的调度和运行带来了很大的困难。为保证电力系统运行的稳定性，现提出“双碳”背景下含高比例新能源电力系统优化调度方法。首先通过构建电力系统优化调度模型，缓解并网波动问题 ；其次进行电力系统的整体优化调度，实现电力系统的可持续发展并减少碳排放。实验结果表明，经由高比例新能源电力系统优化调度方法调度后的风电场实际数值与调度指令较为一致，证明该方法具有较高的可行性和有效性，为高比例新能源电力系统的优化调度提供了参考和指导。
Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking
Authors: You Wu, Xucheng Wang, Xiangyang Yang, Mengyuan Liu, Dan Zeng, Hengzhou Ye, Shuiwang Li
Date: 2025-04-12 00:00:00
Proceeding: CVPR 2025 1
Tasks: Knowledge Distillation

Single-stream architectures using Vision Transformer (ViT) backbones show great potential for real-time UAV tracking recently. However, frequent occlusions from obstacles like buildings and trees expose a major drawback: these models often lack strategies to handle occlusions effectively. New methods are needed to enhance the occlusion resilience of single-stream ViT models in aerial tracking. In this work, we propose to learn Occlusion-Robust Representations (ORR) based on ViTs for UAV tracking by enforcing an invariance of the feature representation of a target with respect to random masking operations modeled by a spatial Cox process. Hopefully, this random masking approximately simulates target occlusions, thereby enabling us to learn ViTs that are robust to target occlusion for UAV tracking. This framework is termed ORTrack. Additionally, to facilitate real-time applications, we propose an Adaptive Feature-Based Knowledge Distillation (AFKD) method to create a more compact tracker, which adaptively mimics the behavior of the teacher model ORTrack according to the task's difficulty. This student model, dubbed ORTrack-D, retains much of ORTrack's performance while offering higher efficiency. Extensive experiments on multiple benchmarks validate the effectiveness of our method, demonstrating its state-of-the-art performance. Codes is available at https://github.com/wuyou3474/ORTrack.
CP-Bench: Evaluating Large Language Models for Constraint Modelling
Authors: Kostis Michailidis, Dimos Tsouros, Tias Guns
Date: 2025-06-06 00:00:00
Tasks: Code Generation

Combinatorial problems are present in a wide range of industries. Constraint Programming (CP) is a well-suited problem-solving paradigm, but its core process, namely constraint modelling, is a bottleneck for wider adoption. Aiming to alleviate this bottleneck, recent studies have explored using Large Language Models (LLMs) as modelling assistants, transforming combinatorial problem descriptions to executable constraint models, similar to coding assistants. However, the existing evaluation datasets for constraint modelling are often limited to small, homogeneous, or domain-specific instances, which do not capture the diversity of real-world scenarios. This work addresses this gap by introducing CP-Bench, a novel benchmark dataset that includes a diverse set of well-known combinatorial problem classes sourced from the CP community, structured explicitly for evaluating LLM-driven CP modelling. With this dataset, and given the variety of constraint modelling frameworks, we compare and evaluate the modelling capabilities of LLMs for three distinct constraint modelling systems, which vary in abstraction level and underlying syntax: the high-level MiniZinc language and Python-based CPMpy library, and the lower-level Python interface of the OR-Tools CP-SAT solver. In order to enhance the ability of LLMs to produce valid constraint models, we systematically evaluate the use of prompt-based and inference-time compute methods adapted from existing LLM-based code generation research. Our results underscore the modelling convenience provided by Python-based frameworks, as well as the effectiveness of documentation-rich system prompts, which, augmented with repeated sampling and self-verification, achieve further improvements, reaching up to 70\% accuracy on this new, highly challenging benchmark.
Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs
Authors: Wenxuan Huo, Qiang He, Gang Zhu, Weifeng Huang
Date: 2025-06-06 00:00:00

Partial differential equations (PDEs) serve as the cornerstone of mathematical physics. In recent years, Physics-Informed Neural Networks (PINNs) have significantly reduced the dependence on large datasets by embedding physical laws directly into the training of neural networks. However, when dealing with complex problems, the accuracy of PINNs still has room for improvement. To address this issue, we introduce the Over-PINNs framework, which leverages automatic differentiation (AD) to generate higher-order auxiliary equations that impose additional physical constraints. These equations are incorporated as extra loss terms in the training process, effectively enhancing the model's ability to capture physical information through an "overdetermined" approach. Numerical results illustrate that this method exhibits strong versatility in solving various types of PDEs. It achieves a significant improvement in solution accuracy without incurring substantial additional computational costs.
Digital audio tampering detection based on spatio-temporal representation learning of electrical network frequency.
Authors: Chunyan Zeng, Shuai Kong, Zhifeng Wang, Xiangkui Wan, Yunfan Chen, Kun Li, Yuhao Zhao
Date: 2024-03-27 00:00:00
Proceeding: Springer 2024 3
Tasks: Representation Learning

The majority of Digital Audio Tampering Detection (DATD) methods, which are based on
Electrical Network Frequency (ENF), predominantly concentrate on the static spatial infor-
mation of ENF. Unfortunately, this focus neglects the temporal variation present in the ENF
time series. This limitation significantly hampers the ENF feature representation capability,
consequently diminishing the overall accuracy of tampering detection. To address this gap,
our paper introduces an innovative digital audio tampering detection method founded on ENF
spatio-temporal feature representation learning. To enhance the feature representation capa-
bility and subsequently improve tampering detection accuracy, we propose the construction
of a parallel spatio-temporal network model. This model incorporates both Convolutional
Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) network
architectures. Through this hybrid model, we aim to deeply extract both ENF spatial and
temporal feature information. In the process of extracting spatial and temporal features of
ENF, we utilize high-precision Discrete Fourier Transform (DFT) analysis on digital audio.
This analysis allows us to extract ENF phase sequences, which are then adaptively divided
into frames through frame shifting. The result is feature matrices of uniform size, effectively
representing the spatial features of ENF. Concurrently, phase sequences are segmented into
frames based on ENF time changes to capture the temporal features of ENF. Subsequently,
deep spatial and temporal features are extracted using CNN and BiLSTM, respectively. To
further enhance the representation capability of the spatio-temporal features, we introduce
an attention mechanism. This mechanism dynamically assigns weights to the deep spatial
and temporal features, providing a nuanced and refined representation. Finally, a deep neural
network is employed to discern whether the audio has undergone tampering. Our experi-
mental results validate the effectiveness of our approach, showcasing superior performance
compared to six state-of-the-art methods across three public databases for digital audio tam-
pering detection. This comprehensive methodology, focusing on both spatial and temporal
aspects of ENF, establishes a robust foundation for advancing the field of DATD and con-
tributes significantly to improving detection accuracy.
Sentinel: SOTA model to protect against prompt injections
Authors: Dror Ivry, Oran Nahum
Date: 2025-06-05 00:00:00

Large Language Models (LLMs) are increasingly powerful but remain vulnerable to prompt injection attacks, where malicious inputs cause the model to deviate from its intended instructions. This paper introduces Sentinel, a novel detection model, qualifire/prompt-injection-sentinel, based on the \answerdotai/ModernBERT-large architecture. By leveraging ModernBERT's advanced features and fine-tuning on an extensive and diverse dataset comprising a few open-source and private collections, Sentinel achieves state-of-the-art performance. This dataset amalgamates varied attack types, from role-playing and instruction hijacking to attempts to generate biased content, alongside a broad spectrum of benign instructions, with private datasets specifically targeting nuanced error correction and real-world misclassifications. On a comprehensive, unseen internal test set, Sentinel demonstrates an average accuracy of 0.987 and an F1-score of 0.980. Furthermore, when evaluated on public benchmarks, it consistently outperforms strong baselines like protectai/deberta-v3-base-prompt-injection-v2. This work details Sentinel's architecture, its meticulous dataset curation, its training methodology, and a thorough evaluation, highlighting its superior detection capabilities.
DLNet: Direction-Aware Feature Integration for Robust Lane Detection in Complex Environments
Authors: Zhaoxuan Lu, Lyuchao Liao, Ruimin Li, Fumin Zou, Sijing Cai and Guangjie Han.
Date: 2025-06-09 00:00:00
Proceeding: Prepare for publication in TITS 2025 6
Tasks: Autonomous Driving, Lane Detection

The rapid advancement of autonomous driving systems has created a pressing need for accurate and robust lane detection to ensure driving safety and reliability. However, lane detection still faces several critical challenges in real-world scenarios: (1) severe occlusions caused by urban traffic and complex road layouts; (2) the diﬃculty of handling sharp curves and large curvature variations; and (3) varying lighting conditions that blur or degrade lane markings. To address these challenges, we propose DLNet, a novel direction-aware feature integration framework that integrates both low-level geometric details and high-level semantic cues. In particular, the approach includes: 
(i) a Multi-Skip Feature Attention Block (MSFAB) to refine local lane features by adaptively fusing multi-scale representations, 
(ii) a Context-Aware Feature Pyramid Network (CAFPN) to enhance global context modeling under adverse conditions, and 
(iii) a Directional Lane IoU (DLIoU) loss function that explicitly encodes lane directionality and curvature, providing more accurate lane overlap estimation. Extensive experiments conducted on two benchmark datasets, CULane and CurveLanes, show DLNet achieves new state-of-the-art results, with F150 and F175 scores of 81.23% and 64.75% on CULane, an F150 score of 86.51% on CurveLanes and a high F1 score of 97.62 on the TUSimple dataset. The source code and pretrained models will be made publicly available at https://github.com/RDXiaoLu/DLNet.git.
FaceCloak: Learning to Protect Face Templates
Authors: Sudipta Banerjee, Anubhav Jain, Chinmay Hegde, Nasir Memon
Date: 2025-04-08 00:00:00

Generative models can reconstruct face images from encoded representations (templates) bearing remarkable likeness to the original face raising security and privacy concerns. We present FaceCloak, a neural network framework that protects face templates by generating smart, renewable binary cloaks. Our method proactively thwarts inversion attacks by cloaking face templates with unique disruptors synthesized from a single face template on the fly while provably retaining biometric utility and unlinkability. Our cloaked templates can suppress sensitive attributes while generalizing to novel feature extraction schemes and outperforms leading baselines in terms of biometric matching and resiliency to reconstruction attacks. FaceCloak-based matching is extremely fast (inference time cost=0.28ms) and light-weight (0.57MB).
OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation
Authors: Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini, Bo Sun, Yakov Bart, Weimin Lyu, Jiri Gesi, Tian Wang, Jing Huang, Yu Su, Upol Ehsan, Malihe Alikhani, Toby Jia-Jun Li, Lydia Chilton, Dakuo Wang
Date: 2025-06-05 00:00:00

Can large language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating ``believable'' human behaviors, evaluating their ability to mimic real user behaviors remains an open challenge, largely due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual human user. To address this gap, we introduce OPERA, a novel dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. OPERA is the first public dataset that comprehensively captures: user personas, browser observations, fine-grained web actions, and self-reported just-in-time rationales. We developed both an online questionnaire and a custom browser plugin to gather this dataset with high fidelity. Using OPERA, we establish the first benchmark to evaluate how well current LLMs can predict a specific user's next action and rationale with a given persona and <observation, action, rationale> history. This dataset lays the groundwork for future research into LLM agents that aim to act as personalized digital twins for human.
Neural Estimation for Scaling Entropic Multimarginal Optimal Transport
Authors: Dor Tsur, Ziv Goldfeld, Kristjan Greenewald, Haim Permuter
Date: 2025-05-31 00:00:00

Multimarginal optimal transport (MOT) is a powerful framework for modeling interactions between multiple distributions, yet its applicability is bottlenecked by a high computational overhead. Entropic regularization provides computational speedups via the multimarginal Sinkhorn algorithm, whose time complexity, for a dataset size $n$ and $k$ marginals, generally scales as $O(n^k)$. However, this dependence on the dataset size $n$ is computationally prohibitive for many machine learning problems. In this work, we propose a new computational framework for entropic MOT, dubbed Neural Entropic MOT (NEMOT), that enjoys significantly improved scalability. NEMOT employs neural networks trained using mini-batches, which transfers the computational complexity from the dataset size to the size of the mini-batch, leading to substantial gains. We provide formal guarantees on the accuracy of NEMOT via non-asymptotic error bounds. We supplement these with numerical results that demonstrate the performance gains of NEMOT over Sinkhorn's algorithm, as well as extensions to neural computation of multimarginal entropic Gromov-Wasserstein alignment. In particular, orders-of-magnitude speedups are observed relative to the state-of-the-art, with a notable increase in the feasible number of samples and marginals. NEMOT seamlessly integrates as a module in large-scale machine learning pipelines, and can serve to expand the practical applicability of entropic MOT for tasks involving multimarginal data.