pwc-archive/papers-with-abstracts huggingface dataset:
Are classical deep neural networks weakly adversarially robust?
Authors: Nuolin Sun, Linyuan Wang, Dongyang Li, Bin Yan, Lei LI
Date: 2025-05-28 00:00:00
Tasks: Adversarial Defense, Adversarial Robustness

Adversarial attacks have received increasing attention and it has been widely recognized that classical DNNs have weak adversarial robustness. The most commonly used adversarial defense method, adversarial training, improves the adversarial accuracy of DNNs by generating adversarial examples and retraining the model. However, adversarial training requires a significant computational overhead. In this paper, inspired by existing studies focusing on the clustering properties of DNN output features at each layer and the Progressive Feedforward Collapse phenomenon, we propose a method for adversarial example detection and image recognition that uses layer-wise features to construct feature paths and computes the correlation between the examples feature paths and the class-centered feature paths. Experimental results show that the recognition method achieves 82.77% clean accuracy and 44.17% adversarial accuracy on the ResNet-20 with PFC. Compared to the adversarial training method with 77.64% clean accuracy and 52.94% adversarial accuracy, our method exhibits a trade-off without relying on computationally expensive defense strategies. Furthermore, on the standard ResNet-18, our method maintains this advantage with respective metrics of 80.01% and 46.1%. This result reveals inherent adversarial robustness in DNNs, challenging the conventional understanding of the weak adversarial robustness in DNNs.
Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review
Authors: Mrinmay Sen, Shruti Aparna, Rohit Agarwal, Chalavadi Krishna Mohan
Date: 2025-06-03 00:00:00
Tasks: Fairness, Federated Learning, Survey

Federated Learning (FL) is a learning mechanism that falls under the distributed training umbrella, which collaboratively trains a shared global model without disclosing the raw data from different clients. This paper presents an extensive survey on the impact of partial client participation in federated learning. While much of the existing research focuses on addressing issues such as generalization, robustness, and fairness caused by data heterogeneity under the assumption of full client participation, limited attention has been given to the practical and theoretical challenges arising from partial client participation, which is common in real-world scenarios. This survey provides an in-depth review of existing FL methods designed to cope with partial client participation. We offer a comprehensive analysis supported by theoretical insights and empirical findings, along with a structured categorization of these methods, highlighting their respective advantages and disadvantages.
MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation
Authors: Mingyang Huang, Peng Zhang, Bang Zhang
Date: 2025-06-03 00:00:00
Tasks: Contrastive Learning, Motion Synthesis, RAG, Retrieval, Retrieval-augmented Generation, Semantic correspondence

Generating long-term, coherent, and realistic music-conditioned dance sequences remains a challenging task in human motion synthesis. Existing approaches exhibit critical limitations: motion graph methods rely on fixed template libraries, restricting creative generation; diffusion models, while capable of producing novel motions, often lack temporal coherence and musical alignment. To address these challenges, we propose $\textbf{MotionRAG-Diff}$, a hybrid framework that integrates Retrieval-Augmented Generation (RAG) with diffusion-based refinement to enable high-quality, musically coherent dance generation for arbitrary long-term music inputs. Our method introduces three core innovations: (1) A cross-modal contrastive learning architecture that aligns heterogeneous music and dance representations in a shared latent space, establishing unsupervised semantic correspondence without paired data; (2) An optimized motion graph system for efficient retrieval and seamless concatenation of motion segments, ensuring realism and temporal coherence across long sequences; (3) A multi-condition diffusion model that jointly conditions on raw music signals and contrastive features to enhance motion quality and global synchronization. Extensive experiments demonstrate that MotionRAG-Diff achieves state-of-the-art performance in motion quality, diversity, and music-motion synchronization accuracy. This work establishes a new paradigm for music-driven dance generation by synergizing retrieval-based template fidelity with diffusion-based creative enhancement.
GeneA-SLAM2: Dynamic SLAM with AutoEncoder-Preprocessed Genetic Keypoints Resampling and Depth Variance-Guided Dynamic Region Removal
Authors: Shufan Qing, Anzhen Li, Qiandi Wang, Yuefeng Niu, Mingchen Feng, Guoliang Hu, Jinqiao Wu, Fengtao Nan, Yingchun Fan
Date: 2025-06-03 00:00:00
Tasks: object-detection, Object Detection, Pose Estimation, Semantic Segmentation, Semantic SLAM

Existing semantic SLAM in dynamic environments mainly identify dynamic regions through object detection or semantic segmentation methods. However, in certain highly dynamic scenarios, the detection boxes or segmentation masks cannot fully cover dynamic regions. Therefore, this paper proposes a robust and efficient GeneA-SLAM2 system that leverages depth variance constraints to handle dynamic scenes. Our method extracts dynamic pixels via depth variance and creates precise depth masks to guide the removal of dynamic objects. Simultaneously, an autoencoder is used to reconstruct keypoints, improving the genetic resampling keypoint algorithm to obtain more uniformly distributed keypoints and enhance the accuracy of pose estimation. Our system was evaluated on multiple highly dynamic sequences. The results demonstrate that GeneA-SLAM2 maintains high accuracy in dynamic scenes compared to current methods. Code is available at: https://github.com/qingshufan/GeneA-SLAM2.
Towards Explicit Geometry-Reflectance Collaboration for Generalized LiDAR Segmentation in Adverse Weather
Authors: Longyu Yang, Ping Hu, Shangbo Yuan, Lu Zhang, Jun Liu, HengTao Shen, Xiaofeng Zhu
Date: 2025-06-03 00:00:00
Proceeding: CVPR 2025 1
Tasks: LIDAR Semantic Segmentation, Semantic Segmentation

Existing LiDAR semantic segmentation models often suffer from decreased accuracy when exposed to adverse weather conditions. Recent methods addressing this issue focus on enhancing training data through weather simulation or universal augmentation techniques. However, few works have studied the negative impacts caused by the heterogeneous domain shifts in the geometric structure and reflectance intensity of point clouds. In this paper, we delve into this challenge and address it with a novel Geometry-Reflectance Collaboration (GRC) framework that explicitly separates feature extraction for geometry and reflectance. Specifically, GRC employs a dual-branch architecture designed to independently process geometric and reflectance features initially, thereby capitalizing on their distinct characteristic. Then, GRC adopts a robust multi-level feature collaboration module to suppress redundant and unreliable information from both branches. Consequently, without complex simulation or augmentation, our method effectively extracts intrinsic information about the scene while suppressing interference, thus achieving better robustness and generalization in adverse weather conditions. We demonstrate the effectiveness of GRC through comprehensive experiments on challenging benchmarks, showing that our method outperforms previous approaches and establishes new state-of-the-art results.
Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT
Authors: Timothy Do, Pranav Saran, Harshita Poojary, Pranav Prabhu, Sean O'Brien, Vasu Sharma, Kevin Zhu
Date: 2025-05-24 00:00:00
Tasks: Classification

In this paper, we address the persistent challenges that figurative language expressions pose for natural language processing (NLP) systems, particularly in low-resource languages such as Konkani. We present a hybrid model that integrates a pre-trained Multilingual BERT (mBERT) with a bidirectional LSTM and a linear classifier. This architecture is fine-tuned on a newly introduced annotated dataset for metaphor classification, developed as part of this work. To improve the model's efficiency, we implement a gradient-based attention head pruning strategy. For metaphor classification, the pruned model achieves an accuracy of 78%. We also applied our pruning approach to expand on an existing idiom classification task, achieving 83% accuracy. These results demonstrate the effectiveness of attention head pruning for building efficient NLP tools in underrepresented languages.
From Theory to Practice with RAVEN-UCB: Addressing Non-Stationarity in Multi-Armed Bandits through Variance Adaptation
Authors: Junyi Fang, Yuxun Chen, Yuxin Chen, Chen Zhang
Date: 2025-06-03 00:00:00
Tasks: Multi-Armed Bandits

The Multi-Armed Bandit (MAB) problem is challenging in non-stationary environments where reward distributions evolve dynamically. We introduce RAVEN-UCB, a novel algorithm that combines theoretical rigor with practical efficiency via variance-aware adaptation. It achieves tighter regret bounds than UCB1 and UCB-V, with gap-dependent regret of order $K \sigma_{\max}^2 \log T / \Delta$ and gap-independent regret of order $\sqrt{K T \log T}$. RAVEN-UCB incorporates three innovations: (1) variance-driven exploration using $\sqrt{\hat{\sigma}_k^2 / (N_k + 1)}$ in confidence bounds, (2) adaptive control via $\alpha_t = \alpha_0 / \log(t + \epsilon)$, and (3) constant-time recursive updates for efficiency. Experiments across non-stationary patterns - distributional changes, periodic shifts, and temporary fluctuations - in synthetic and logistics scenarios demonstrate its superiority over state-of-the-art baselines, confirming theoretical and practical robustness.
InterRVOS: Interaction-aware Referring Video Object Segmentation
Authors: Woojeong Jin, Seongchan Kim, Seungryong Kim
Date: 2025-06-03 00:00:00
Tasks: 8k, Object, Referring Video Object Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation, Video Understanding

Referring video object segmentation aims to segment the object in a video corresponding to a given natural language expression. While prior works have explored various referring scenarios, including motion-centric or multi-instance expressions, most approaches still focus on localizing a single target object in isolation. However, in comprehensive video understanding, an object's role is often defined by its interactions with other entities, which are largely overlooked in existing datasets and models. In this work, we introduce Interaction-aware referring video object sgementation (InterRVOS), a new task that requires segmenting both actor and target entities involved in an interaction. Each interactoin is described through a pair of complementary expressions from different semantic perspectives, enabling fine-grained modeling of inter-object relationships. To tackle this task, we propose InterRVOS-8K, the large-scale and automatically constructed dataset containing diverse interaction-aware expressions with corresponding masks, including challenging cases such as motion-only multi-instance expressions. We also present a baseline architecture, ReVIOSa, designed to handle actor-target segmentation from a single expression, achieving strong performance in both standard and interaction-focused settings. Furthermore, we introduce an actor-target-aware evalaution setting that enables a more targeted assessment of interaction understanding. Experimental results demonstrate that our approach outperforms prior methods in modeling complex object interactions for referring video object segmentation task, establishing a strong foundation for future research in interaction-centric video understanding. Our project page is available at \href{https://cvlab-kaist.github.io/InterRVOS}{https://cvlab-kaist.github.io/InterRVOS}.
Multi Layered Autonomy and AI Ecologies in Robotic Art Installations
Authors: Baoyang Chen, Xian Xu, Huamin Qu
Date: 2025-06-03 00:00:00
Tasks: Ethics

Symbiosis of Agents is a large-scale installation by Baoyang Chen (baoyangchen.com) that embeds AI-driven robots in an immersive, mirror-lined arena, probing the tension between machine agency and artistic authorship. Drawing on early cybernetics, rule-based conceptual art, and seminal robotic works, it orchestrates fluid exchanges among robotic arms, quadruped machines, their environment, and the public. A three tier faith system pilots the ecology: micro-level adaptive tactics, meso-level narrative drives, and a macro-level prime directive. This hierarchy lets behaviors evolve organically in response to environmental cues and even a viewer's breath, turning spectators into co-authors of the unfolding drama. Framed by a speculative terraforming scenario that recalls the historical exploitation of marginalized labor, the piece asks who bears responsibility in AI-mediated futures. Choreographed motion, AI-generated scripts, reactive lighting, and drifting fog cast the robots as collaborators rather than tools, forging a living, emergent artwork. Exhibited internationally, Symbiosis of Agents shows how cybernetic feedback, robotic experimentation, and conceptual rule-making can converge to redefine agency, authorship, and ethics in contemporary art.
How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment
Authors: Bin Ma, Yuyuan Feng, Minhua Lin, Enyan Dai
Date: 2025-06-03 00:00:00
Tasks: Data Augmentation, Drug Discovery, Financial Analysis

Graph Neural Networks (GNNs) have become essential tools for analyzing graph-structured data in domains such as drug discovery and financial analysis, leading to growing demands for model transparency. Recent advances in explainable GNNs have addressed this need by revealing important subgraphs that influence predictions, but these explanation mechanisms may inadvertently expose models to security risks. This paper investigates how such explanations potentially leak critical decision logic that can be exploited for model stealing. We propose {\method}, a novel stealing framework that integrates explanation alignment for capturing decision logic with guided data augmentation for efficient training under limited queries, enabling effective replication of both the predictive behavior and underlying reasoning patterns of target models. Experiments on molecular graph datasets demonstrate that our approach shows advantages over conventional methods in model stealing. This work highlights important security considerations for the deployment of explainable GNNs in sensitive domains and suggests the need for protective measures against explanation-based attacks. Our code is available at https://github.com/beanmah/EGSteal.
EyeNavGS: A 6-DoF Navigation Dataset and Record-n-Replay Software for Real-World 3DGS Scenes in VR
Authors: Zihao Ding, Cheng-Tse Lee, Mufeng Zhu, Tao Guan, Yuan-Chun Sun, Cheng-Hsin Hsu, Yao Liu
Date: 2025-06-03 00:00:00
Tasks: 3DGS

3D Gaussian Splatting (3DGS) is an emerging media representation that reconstructs real-world 3D scenes in high fidelity, enabling 6-degrees-of-freedom (6-DoF) navigation in virtual reality (VR). However, developing and evaluating 3DGS-enabled applications and optimizing their rendering performance, require realistic user navigation data. Such data is currently unavailable for photorealistic 3DGS reconstructions of real-world scenes. This paper introduces EyeNavGS (EyeNavGS), the first publicly available 6-DoF navigation dataset featuring traces from 46 participants exploring twelve diverse, real-world 3DGS scenes. The dataset was collected at two sites, using the Meta Quest Pro headsets, recording the head pose and eye gaze data for each rendered frame during free world standing 6-DoF navigation. For each of the twelve scenes, we performed careful scene initialization to correct for scene tilt and scale, ensuring a perceptually-comfortable VR experience. We also release our open-source SIBR viewer software fork with record-and-replay functionalities and a suite of utility tools for data processing, conversion, and visualization. The EyeNavGS dataset and its accompanying software tools provide valuable resources for advancing research in 6-DoF viewport prediction, adaptive streaming, 3D saliency, and foveated rendering for 3DGS scenes. The EyeNavGS dataset is available at: https://symmru.github.io/EyeNavGS/.
Is PMBOK Guide the Right Fit for AI? Re-evaluating Project Management in the Face of Artificial Intelligence Projects
Authors: Alexey Burdakov, Max Jaihyun Ahn
Date: 2025-06-02 00:00:00
Tasks: Management

This paper critically evaluates the applicability of the Project Management Body of Knowledge (PMBOK) Guide framework to Artificial Intelligence (AI) software projects, highlighting key limitations and proposing tailored adaptations. Unlike traditional projects, AI initiatives rely heavily on complex data, iterative experimentation, and specialized expertise while navigating significant ethical considerations. Our analysis identifies gaps in the PMBOK Guide, including its limited focus on data management, insufficient support for iterative development, and lack of guidance on ethical and multidisciplinary challenges. To address these deficiencies, we recommend integrating data lifecycle management, adopting iterative and AI project management frameworks, and embedding ethical considerations within project planning and execution. Additionally, we explore alternative approaches that better align with AI's dynamic and exploratory nature. We aim to enhance project management practices for AI software projects by bridging these gaps.
Polishing Every Facet of the GEM: Testing Linguistic Competence of LLMs and Humans in Korean
Authors: Sungho Kim, Nayeon Kim, Taehee Jeon, SangKeun Lee
Date: 2025-06-02 00:00:00
Tasks: Multiple-choice

We introduce the $\underline{Ko}rean \underline{G}rammar \underline{E}valuation Bench\underline{M}ark (KoGEM)$, designed to assess the linguistic competence of LLMs and humans in Korean. KoGEM consists of 1.5k multiple-choice QA pairs covering five main categories and 16 subcategories. The zero-shot evaluation of 27 LLMs of various sizes and types reveals that while LLMs perform remarkably well on straightforward tasks requiring primarily definitional knowledge, they struggle with tasks that demand the integration of real-world experiential knowledge, such as phonological rules and pronunciation. Furthermore, our in-depth analysis suggests that incorporating such experiential knowledge could enhance the linguistic competence of LLMs. With KoGEM, we not only highlight the limitations of current LLMs in linguistic competence but also uncover hidden facets of LLMs in linguistic competence, paving the way for enhancing comprehensive language understanding. Our code and dataset are available at: https://github.com/SungHo3268/KoGEM.
ChatCFD: an End-to-End CFD Agent with Domain-specific Structured Thinking
Authors: E Fan, Weizong Wang, Tianhan Zhang
Date: 2025-05-28 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Computational Fluid Dynamics (CFD) is essential for scientific and engineering advancements but is limited by operational complexity and the need for extensive expertise. This paper presents ChatCFD, a large language model-driven pipeline that automates CFD workflows within the OpenFOAM framework. It enables users to configure and execute complex simulations from natural language prompts or published literature with minimal expertise. The innovation is its structured approach to database construction, configuration validation, and error reflection, integrating CFD and OpenFOAM knowledge with general language models to improve accuracy and adaptability. Validation shows ChatCFD can autonomously reproduce published CFD results, handling complex, unseen configurations beyond basic examples, a task challenging for general language models.
M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image Quality Assessment
Authors: Chuan Cui, Kejiang Chen, Zhihua Wei, Wen Shen, Weiming Zhang, Nenghai Yu
Date: 2025-02-21 00:00:00
Tasks: Image Quality Assessment

The rapid advancement of AI-generated image (AGI) models has introduced significant challenges in evaluating their quality, which requires considering multiple dimensions such as perceptual quality, prompt correspondence, and authenticity. To address these challenges, we propose M3-AGIQA, a comprehensive framework for AGI quality assessment that is Multimodal, Multi-Round, and Multi-Aspect. Our approach leverages the capabilities of Multimodal Large Language Models (MLLMs) as joint text and image encoders and distills advanced captioning capabilities from online MLLMs into a local model via Low-Rank Adaptation (LoRA) fine-tuning. The framework includes a structured multi-round evaluation mechanism, where intermediate image descriptions are generated to provide deeper insights into the quality, correspondence, and authenticity aspects. To align predictions with human perceptual judgments, a predictor constructed by an xLSTM and a regression head is incorporated to process sequential logits and predict Mean Opinion Scores (MOSs). Extensive experiments conducted on multiple benchmark datasets demonstrate that M3-AGIQA achieves state-of-the-art performance, effectively capturing nuanced aspects of AGI quality. Furthermore, cross-dataset validation confirms its strong generalizability. The code is available at https://github.com/strawhatboy/M3-AGIQA.
Dense Match Summarization for Faster Two-view Estimation
Authors: Jonathan Astermark, Anders Heyden, Viktor Larsson
Date: 2025-06-03 00:00:00
Proceeding: CVPR 2025 1

In this paper, we speed up robust two-view relative pose from dense correspondences. Previous work has shown that dense matchers can significantly improve both accuracy and robustness in the resulting pose. However, the large number of matches comes with a significantly increased runtime during robust estimation in RANSAC. To avoid this, we propose an efficient match summarization scheme which provides comparable accuracy to using the full set of dense matches, while having 10-100x faster runtime. We validate our approach on standard benchmark datasets together with multiple state-of-the-art dense matchers.
Lung Cancer Detection Using Machine Learning Methods
Authors: Dipak Debnath Arka; Sad Md. Tafhim; Rawnak Muntaha Anan; Nusaibah Rahat; Samiu Mostafa Ishan; Sifat Tanvir
Date: 2024-04-05 00:00:00
Proceeding: IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) 2024 4
Tasks: Lung Cancer Diagnosis, Lung Disease Classification, Specificity

Cancer-related death is now more common than ever with cancer of the lungs being the leading cause of it. In such regards, the survival percentage for individuals with lung cancer must be increased through early identification. In this paper, we propose a machine learning-based approach for lung cancer detection using data from patients. The proposed method has three stages: preprocessing, feature extraction, and classification. In preprocessing, we extracted the necessary data from the dataset we collected. In the feature extraction stage, we processed the raw data while keeping the original information unchanged, in order for the classifier to work on. Finally, in the classification stage, we use 6 different classifier models to classify the extracted features. We evaluated the proposed method on a publicly available dataset from Kaggle of lung cancer causes and age. The results of the trial confirm the feasibility of the suggested strategy to achieve high accuracy, sensitivity, specificity, and AUC score. In conclusion, using machine learning, The suggested approach offers a powerful and efficient way to detect lung cancer. The proposed method can potentially assist in the detection of lung cancer, thereby improving the survival rate of lung cancer patients.