pwc-archive/papers-with-abstracts huggingface dataset:
Position Paper: Towards Open Complex Human-AI Agents Collaboration System for Problem-Solving and Knowledge Management
Authors: Ju Wu, Calvin K. L. Or
Date: 2025-04-24 00:00:00
Tasks: Management, Position

This position paper critically surveys a broad spectrum of recent empirical developments on human-AI agents collaboration, highlighting both their technical achievements and persistent gaps. We observe a lack of a unifying theoretical framework that can coherently integrate these varied studies, especially when tackling open-ended, complex tasks. To address this, we propose a novel conceptual architecture: one that systematically interlinks the technical details of multi-agent coordination, knowledge management, cybernetic feedback loops, and higher-level control mechanisms. By mapping existing contributions, from symbolic AI techniques and connectionist LLM-based agents to hybrid organizational practices, onto this proposed framework (Hierarchical Exploration-Exploitation Net), our approach facilitates revision of legacy methods and inspires new work that fuses qualitative and quantitative paradigms. The paper's structure allows it to be read from any section, serving equally as a critical review of technical implementations and as a forward-looking reference for designing or extending human-AI symbioses. Together, these insights offer a stepping stone toward deeper co-evolution of human cognition and AI capability.
Improving Editability in Image Generation with Layer-wise Memory
Authors: Daneul Kim, Jaeah Lee, Jaesik Park
Date: 2025-05-02 00:00:00
Proceeding: CVPR 2025 1
Tasks: Disentanglement, Image Generation

Most real-world image editing tasks require multiple sequential edits to achieve desired results. Current editing approaches, primarily designed for single-object modifications, struggle with sequential editing: especially with maintaining previous edits along with adapting new objects naturally into the existing content. These limitations significantly hinder complex editing scenarios where multiple objects need to be modified while preserving their contextual relationships. We address this fundamental challenge through two key proposals: enabling rough mask inputs that preserve existing content while naturally integrating new elements and supporting consistent editing across multiple modifications. Our framework achieves this through layer-wise memory, which stores latent representations and prompt embeddings from previous edits. We propose Background Consistency Guidance that leverages memorized latents to maintain scene coherence and Multi-Query Disentanglement in cross-attention that ensures natural adaptation to existing content. To evaluate our method, we present a new benchmark dataset incorporating semantic alignment metrics and interactive editing scenarios. Through comprehensive experiments, we demonstrate superior performance in iterative image editing tasks with minimal user effort, requiring only rough masks while maintaining high-quality results throughout multiple editing steps.
Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues
Authors: Jinfeng Zhou, Yuxuan Chen, Jianing Yin, Yongkang Huang, Yihan Shi, Xikun Zhang, Libiao Peng, Rongsheng Zhang, Tangjie Lv, Zhipeng Hu, Hongning Wang, Minlie Huang
Date: 2025-04-24 00:00:00
Tasks: Sentence

Cognitive Restructuring (CR) is a psychotherapeutic process aimed at identifying and restructuring an individual's negative thoughts, arising from mental health challenges, into more helpful and positive ones via multi-turn dialogues. Clinician shortage and stigma urge the development of human-LLM interactive psychotherapy for CR. Yet, existing efforts implement CR via simple text rewriting, fixed-pattern dialogues, or a one-shot CR workflow, failing to align with the psychotherapeutic process for effective CR. To address this gap, we propose CRDial, a novel framework for CR, which creates multi-turn dialogues with specifically designed identification and restructuring stages of negative thoughts, integrates sentence-level supportive conversation strategies, and adopts a multi-channel loop mechanism to enable iterative CR. With CRDial, we distill Crisp, a large-scale and high-quality bilingual dialogue dataset, from LLM. We then train Crispers, Crisp-based conversational LLMs for CR, at 7B and 14B scales. Extensive human studies show the superiority of Crispers in pointwise, pairwise, and intervention evaluations.
InterLoc: LiDAR-based Intersection Localization using Road Segmentation with Automated Evaluation Method
Authors: Nguyen Hoang Khoi Tran, Julie Stephany Berrio, Mao Shan, Zhenxing Ming, Stewart Worrall
Date: 2025-05-01 00:00:00
Tasks: Benchmarking, Motion Planning, Pose Estimation, Road Segmentation, Vehicle Pose Estimation

Online localization of road intersections is beneficial for autonomous vehicle localization, mapping and motion planning. Intersections offer strong landmarks to correct vehicle pose estimation in GNSS dropouts and anchor new sensor data in up-to-date maps. They are also decisive routing nodes in road network graphs. Despite that importance, intersection localization has not been widely studied, with existing methods either ignore the rich semantic information already computed onboard or depend on scarce, hand-labeled intersection datasets. To close that gap, this paper presents a LiDAR-based method for online vehicle-centric intersection localization. We fuse semantic road segmentation with vehicle local pose to detect intersection candidates in a bird's eye view (BEV) representation. We then refine those candidates by analyzing branch topology and correcting the intersection point in a least squares formulation. To evaluate our method, we introduce an automated benchmarking pipeline that pairs localized intersection points with OpenStreetMap (OSM) intersection nodes using precise GNSS/INS ground-truth poses. Experiments on SemanticKITTI show that the method outperforms the latest learning-based baseline in accuracy and reliability. Moreover, sensitivity tests demonstrate that our method is robust to challenging segmentation error levels, highlighting its applicability in the real world.
Efficient Vocabulary-Free Fine-Grained Visual Recognition in the Age of Multimodal LLMs
Authors: Hari Chandana Kuchibhotla, Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramanian
Date: 2025-05-02 00:00:00
Tasks: Fine-Grained Visual Recognition

Fine-grained Visual Recognition (FGVR) involves distinguishing between visually similar categories, which is inherently challenging due to subtle inter-class differences and the need for large, expert-annotated datasets. In domains like medical imaging, such curated datasets are unavailable due to issues like privacy concerns and high annotation costs. In such scenarios lacking labeled data, an FGVR model cannot rely on a predefined set of training labels, and hence has an unconstrained output space for predictions. We refer to this task as Vocabulary-Free FGVR (VF-FGVR), where a model must predict labels from an unconstrained output space without prior label information. While recent Multimodal Large Language Models (MLLMs) show potential for VF-FGVR, querying these models for each test input is impractical because of high costs and prohibitive inference times. To address these limitations, we introduce \textbf{Nea}rest-Neighbor Label \textbf{R}efinement (NeaR), a novel approach that fine-tunes a downstream CLIP model using labels generated by an MLLM. Our approach constructs a weakly supervised dataset from a small, unlabeled training set, leveraging MLLMs for label generation. NeaR is designed to handle the noise, stochasticity, and open-endedness inherent in labels generated by MLLMs, and establishes a new benchmark for efficient VF-FGVR.
GeloVec: Higher Dimensional Geometric Smoothing for Coherent Visual Feature Extraction in Image Segmentation
Authors: Boris Kriuk, Matey Yordanov
Date: 2025-05-02 00:00:00
Tasks: Computational Efficiency, Image Segmentation, Segmentation, Semantic Segmentation

This paper introduces GeloVec, a new CNN-based attention smoothing framework for semantic segmentation that addresses critical limitations in conventional approaches. While existing attention-backed segmentation methods suffer from boundary instability and contextual discontinuities during feature mapping, our framework implements a higher-dimensional geometric smoothing method to establish a robust manifold relationships between visually coherent regions. GeloVec combines modified Chebyshev distance metrics with multispatial transformations to enhance segmentation accuracy through stabilized feature extraction. The core innovation lies in the adaptive sampling weights system that calculates geometric distances in n-dimensional feature space, achieving superior edge preservation while maintaining intra-class homogeneity. The multispatial transformation matrix incorporates tensorial projections with orthogonal basis vectors, creating more discriminative feature representations without sacrificing computational efficiency. Experimental validation across multiple benchmark datasets demonstrates significant improvements in segmentation performance, with mean Intersection over Union (mIoU) gains of 2.1%, 2.7%, and 2.4% on Caltech Birds-200, LSDSC, and FSSD datasets respectively compared to state-of-the-art methods. GeloVec's mathematical foundation in Riemannian geometry provides theoretical guarantees on segmentation stability. Importantly, our framework maintains computational efficiency through parallelized implementation of geodesic transformations and exhibits strong generalization capabilities across disciplines due to the absence of information loss during transformations.
Lessons from Deploying Learning-based CSI Localization on a Large-Scale ISAC Platform
Authors: Tianyu Zhang, Dongheng Zhang, Ruixu Geng, Xuecheng Xie, Shuai Yang, Yan Chen
Date: 2025-04-24 00:00:00
Tasks: Indoor Localization, Integrated sensing and communication, ISAC

In recent years, Channel State Information (CSI), recognized for its fine-grained spatial characteristics, has attracted increasing attention in WiFi-based indoor localization. However, despite its potential, CSI-based approaches have yet to achieve the same level of deployment scale and commercialization as those based on Received Signal Strength Indicator (RSSI). A key limitation lies in the fact that most existing CSI-based systems are developed and evaluated in controlled, small-scale environments, limiting their generalizability. To bridge this gap, we explore the deployment of a large-scale CSI-based localization system involving over 400 Access Points (APs) in a real-world building under the Integrated Sensing and Communication (ISAC) paradigm. We highlight two critical yet often overlooked factors: the underutilization of unlabeled data and the inherent heterogeneity of CSI measurements. To address these challenges, we propose a novel CSI-based learning framework for WiFi localization, tailored for large-scale ISAC deployments on the server side. Specifically, we employ a novel graph-based structure to model heterogeneous CSI data and reduce redundancy. We further design a pretext pretraining task that incorporates spatial and temporal priors to effectively leverage large-scale unlabeled CSI data. Complementarily, we introduce a confidence-aware fine-tuning strategy to enhance the robustness of localization results. In a leave-one-smartphone-out experiment spanning five floors and 25, 600 m2, we achieve a median localization error of 2.17 meters and a floor accuracy of 99.49%. This performance corresponds to an 18.7% reduction in mean absolute error (MAE) compared to the best-performing baseline.
Transferable Adversarial Attacks on Black-Box Vision-Language Models
Authors: Kai Hu, Weichen Yu, Li Zhang, Alexander Robey, Andy Zou, Chengming Xu, Haoqi Hu, Matt Fredrikson
Date: 2025-05-02 00:00:00
Tasks: Image Captioning, Object Recognition, Question Answering, Visual Question Answering

Vision Large Language Models (VLLMs) are increasingly deployed to offer advanced capabilities on inputs comprising both text and images. While prior research has shown that adversarial attacks can transfer from open-source to proprietary black-box models in text-only and vision-only contexts, the extent and effectiveness of such vulnerabilities remain underexplored for VLLMs. We present a comprehensive analysis demonstrating that targeted adversarial examples are highly transferable to widely-used proprietary VLLMs such as GPT-4o, Claude, and Gemini. We show that attackers can craft perturbations to induce specific attacker-chosen interpretations of visual information, such as misinterpreting hazardous content as safe, overlooking sensitive or restricted material, or generating detailed incorrect responses aligned with the attacker's intent. Furthermore, we discover that universal perturbations -- modifications applicable to a wide set of images -- can consistently induce these misinterpretations across multiple proprietary VLLMs. Our experimental results on object recognition, visual question answering, and image captioning show that this vulnerability is common across current state-of-the-art models, and underscore an urgent need for robust mitigations to ensure the safe and secure deployment of VLLMs.
What Makes for a Good Saliency Map? Comparing Strategies for Evaluating Saliency Maps in Explainable AI (XAI)
Authors: Felix Kares, Timo Speith, Hanwei Zhang, Markus Langer
Date: 2025-04-23 00:00:00

Saliency maps are a popular approach for explaining classifications of (convolutional) neural networks. However, it remains an open question as to how best to evaluate salience maps, with three families of evaluation methods commonly being used: subjective user measures, objective user measures, and mathematical metrics. We examine three of the most popular saliency map approaches (viz., LIME, Grad-CAM, and Guided Backpropagation) in a between subject study (N=166) across these families of evaluation methods. We test 1) for subjective measures, if the maps differ with respect to user trust and satisfaction; 2) for objective measures, if the maps increase users' abilities and thus understanding of a model; 3) for mathematical metrics, which map achieves the best ratings across metrics; and 4) whether the mathematical metrics can be associated with objective user measures. To our knowledge, our study is the first to compare several salience maps across all these evaluation methods$-$with the finding that they do not agree in their assessment (i.e., there was no difference concerning trust and satisfaction, Grad-CAM improved users' abilities best, and Guided Backpropagation had the most favorable mathematical metrics). Additionally, we show that some mathematical metrics were associated with user understanding, although this relationship was often counterintuitive. We discuss these findings in light of general debates concerning the complementary use of user studies and mathematical metrics in the evaluation of explainable AI (XAI) approaches.
Edge Detection based on Channel Attention and Inter-region Independence Test
Authors: Ru-yu Yan, Da-qing Zhang
Date: 2025-05-02 00:00:00
Tasks: Edge Detection

Existing edge detection methods often suffer from noise amplification and excessive retention of non-salient details, limiting their applicability in high-precision industrial scenarios. To address these challenges, we propose CAM-EDIT, a novel framework that integrates Channel Attention Mechanism (CAM) and Edge Detection via Independence Testing (EDIT). The CAM module adaptively enhances discriminative edge features through multi-channel fusion, while the EDIT module employs region-wise statistical independence analysis (using Fisher's exact test and chi-square test) to suppress uncorrelated noise.Extensive experiments on BSDS500 and NYUDv2 datasets demonstrate state-of-the-art performance. Among the nine comparison algorithms, the F-measure scores of CAM-EDIT are 0.635 and 0.460, representing improvements of 19.2\% to 26.5\% over traditional methods (Canny, CannySR), and better than the latest learning based methods (TIP2020, MSCNGP). Noise robustness evaluations further reveal a 2.2\% PSNR improvement under Gaussian noise compared to baseline methods. Qualitative results exhibit cleaner edge maps with reduced artifacts, demonstrating its potential for high-precision industrial applications.
Search Timelines: Visualizing Search History to Enable Cross-Session Exploratory Search
Authors: Orland Hoeber, Md Nazmul Islam, Miriam Boon, Dale Storie, Veronica Ramshaw
Date: 2025-04-23 00:00:00

Purpose: The timespan over which exploratory searching can occur, as well as the scope and volume of the search activities undertaken, can make it difficult for searchers to remember key details about their search activities. These difficulties are present both in the midst of searching as well as when resuming a search that spans multiple sessions. In this paper, we present a search interface designed to support cross-session exploratory search in a public digital library context. Methods: Search Timelines provides a visualization of current and past search activities via a dynamic timeline of the search activity (queries and saved resources). This timeline is presented at two levels of detail. An overview timeline is provided alongside the search results in a typical search engine results page design. A detailed timeline is provided in the workspace, where searchers can review the history of their search activities and their saved resources. A controlled laboratory study was conducted to compare this approach to a baseline interface modelled after a typical public digital library search/workspace interface. Results: Participants who used Search Timelines reported higher levels of user engagement, usability, and perceived knowledge gain, during an initial search session and when resuming the search after a 7-8 day interval. This came at the expense of the searchers taking more time to complete the search task, which we view as positive evidence of engagement in cross-session exploratory search processes. Conclusion: Search Timelines serves as an example of how lightweight visualization approaches can be used to enhance typical search interface designs to support exploratory search. The results highlight the value of providing persistent representations of past search activities within the search interface.
Edge-preserving Image Denoising via Multi-scale Adaptive Statistical Independence Testing
Authors: Ruyu Yan, Da-qing Zhang
Date: 2025-05-02 00:00:00
Tasks: Denoising, Edge Detection, Image Denoising, statistical independence testing

Edge detection is crucial in image processing, but existing methods often produce overly detailed edge maps, affecting clarity. Fixed-window statistical testing faces issues like scale mismatch and computational redundancy. To address these, we propose a novel Multi-scale Adaptive Independence Testing-based Edge Detection and Denoising (EDD-MAIT), a Multi-scale Adaptive Statistical Testing-based edge detection and denoising method that integrates a channel attention mechanism with independence testing. A gradient-driven adaptive window strategy adjusts window sizes dynamically, improving detail preservation and noise suppression. EDD-MAIT achieves better robustness, accuracy, and efficiency, outperforming traditional and learning-based methods on BSDS500 and BIPED datasets, with improvements in F-score, MSE, PSNR, and reduced runtime. It also shows robustness against Gaussian noise, generating accurate and clean edge maps in noisy environments.
Fine-Tuning Without Forgetting: Adaptation of YOLOv8 Preserves COCO Performance
Authors: Vishal Gandhi, Sagar Gandhi
Date: 2025-05-02 00:00:00

The success of large pre-trained object detectors hinges on their adaptability to diverse downstream tasks. While fine-tuning is the standard adaptation method, specializing these models for challenging fine-grained domains necessitates careful consideration of feature granularity. The critical question remains: how deeply should the pre-trained backbone be fine-tuned to optimize for the specialized task without incurring catastrophic forgetting of the original general capabilities? Addressing this, we present a systematic empirical study evaluating the impact of fine-tuning depth. We adapt a standard YOLOv8n model to a custom, fine-grained fruit detection dataset by progressively unfreezing backbone layers (freeze points at layers 22, 15, and 10) and training. Performance was rigorously evaluated on both the target fruit dataset and, using a dual-head evaluation architecture, on the original COCO validation set. Our results demonstrate unequivocally that deeper fine-tuning (unfreezing down to layer 10) yields substantial performance gains (e.g., +10\% absolute mAP50) on the fine-grained fruit task compared to only training the head. Strikingly, this significant adaptation and specialization resulted in negligible performance degradation (<0.1\% absolute mAP difference) on the COCO benchmark across all tested freeze levels. We conclude that adapting mid-to-late backbone features is highly effective for fine-grained specialization. Critically, our results demonstrate this adaptation can be achieved without the commonly expected penalty of catastrophic forgetting, presenting a compelling case for exploring deeper fine-tuning strategies, particularly when targeting complex domains or when maximizing specialized performance is paramount.
PsyCounAssist: A Full-Cycle AI-Powered Psychological Counseling Assistant System
Authors: Xianghe Liu, Jiaqi Xu, Tao Sun
Date: 2025-04-23 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition, Photoplethysmography (PPG)

Psychological counseling is a highly personalized and dynamic process that requires therapists to continuously monitor emotional changes, document session insights, and maintain therapeutic continuity. In this paper, we introduce PsyCounAssist, a comprehensive AI-powered counseling assistant system specifically designed to augment psychological counseling practices. PsyCounAssist integrates multimodal emotion recognition combining speech and photoplethysmography (PPG) signals for accurate real-time affective analysis, automated structured session reporting using large language models (LLMs), and personalized AI-generated follow-up support. Deployed on Android-based tablet devices, the system demonstrates practical applicability and flexibility in real-world counseling scenarios. Experimental evaluation confirms the reliability of PPG-based emotional classification and highlights the system's potential for non-intrusive, privacy-aware emotional support. PsyCounAssist represents a novel approach to ethically and effectively integrating AI into psychological counseling workflows.
Towards the Resistance of Neural Network Watermarking to Fine-tuning
Authors: Ling Tang, Yuefeng Chen, Hui Xue, Quanshi Zhang
Date: 2025-05-02 00:00:00

This paper proves a new watermarking method to embed the ownership information into a deep neural network (DNN), which is robust to fine-tuning. Specifically, we prove that when the input feature of a convolutional layer only contains low-frequency components, specific frequency components of the convolutional filter will not be changed by gradient descent during the fine-tuning process, where we propose a revised Fourier transform to extract frequency components from the convolutional filter. Additionally, we also prove that these frequency components are equivariant to weight scaling and weight permutations. In this way, we design a watermark module to encode the watermark information to specific frequency components in a convolutional filter. Preliminary experiments demonstrate the effectiveness of our method.