pwc-archive/papers-with-abstracts huggingface dataset:
SIDL: A Real-World Dataset for Restoring Smartphone Images with Dirty Lenses
Authors: Sooyoung Choi, Sungyong Park, Heewon Kim
Date: 2025-04-11 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2025 4
Tasks: Image Restoration

Smartphone cameras are ubiquitous in daily life, yet their performance can be severely impacted by dirty lenses, leading to degraded image quality. This issue is often overlooked in image restoration research, which assumes ideal or controlled lens conditions. To address this gap, we introduced SIDL (Smartphone Images with Dirty Lenses), a novel dataset designed to restore images captured through contaminated smartphone lenses. SIDL contains diverse real-world images taken under various lighting conditions and environments. These images feature a wide range of lens contaminants, including water drops, fingerprints, and dust. Each contaminated image is paired with a clean reference image, enabling supervised learning approaches for restoration tasks. To evaluate the challenge posed by SIDL, various state-of-the-art restoration models were trained and compared on this dataset. Their performances achieved some level of restoration but did not adequately address the diverse and realistic nature of the lens contaminants in SIDL. This challenge highlights the need for more robust and adaptable image restoration techniques for restoring images with dirty lenses.
CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion
Authors: Boyuan Meng, Xiaohan Zhang, Peilin Li, Zhe Wu, Yiming Li, Wenkai Zhao, Beinan Yu, Hui-Liang Shen
Date: 2025-05-02 00:00:00
Tasks: Cross-Domain Few-Shot, Cross-Domain Few-Shot Object Detection, Few-Shot Object Detection, Object, object-detection, Object Detection

Cross-domain few-shot object detection (CD-FSOD) aims to detect novel objects across different domains with limited class instances. Feature confusion, including object-background confusion and object-object confusion, presents significant challenges in both cross-domain and few-shot settings. In this work, we introduce CDFormer, a cross-domain few-shot object detection transformer against feature confusion, to address these challenges. The method specifically tackles feature confusion through two key modules: object-background distinguishing (OBD) and object-object distinguishing (OOD). The OBD module leverages a learnable background token to differentiate between objects and background, while the OOD module enhances the distinction between objects of different classes. Experimental results demonstrate that CDFormer outperforms previous state-of-the-art approaches, achieving 12.9% mAP, 11.0% mAP, and 10.4% mAP improvements under the 1/5/10 shot settings, respectively, when fine-tuned.
A Study on Group Decision Making Problem Based on Fuzzy Reasoning and Bayesian Networks
Authors: Shui-jin Rong, Wei Guo, Da-qing Zhang
Date: 2025-04-30 00:00:00
Tasks: Decision Making

Aiming at the group decision - making problem with multi - objective attributes, this study proposes a group decision - making system that integrates fuzzy inference and Bayesian network. A fuzzy rule base is constructed by combining threshold values, membership functions, expert experience, and domain knowledge to address quantitative challenges such as scale differences and expert linguistic variables. A hierarchical Bayesian network is designed, featuring a directed acyclic graph with nodes selected by experts, and maximum likelihood estimation is used to dynamically optimize the conditional probability table, modeling the nonlinear correlations among multidimensional indices for posterior probability aggregation. In a comprehensive student evaluation case, this method is compared with the traditional weighted scoring approach. The results indicate that the proposed method demonstrates effectiveness in both rule criterion construction and ranking consistency, with a classification accuracy of 86.0% and an F1 value improvement of 53.4% over the traditional method. Additionally, computational experiments on real - world datasets across various group decision scenarios assess the method's performance and robustness, providing evidence of its reliability in diverse contexts.
A Survey on Large Language Model based Human-Agent Systems
Authors: Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Yankai Chen, Chunyu Miao, Hoang Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Xue Liu, Philip S. Yu
Date: 2025-05-01 00:00:00
Tasks: Human Agent Collaboration, Language Modeling, Language Modelling, Large Language Model

Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. However, fully autonomous LLM-based agents still face significant challenges, including limited reliability due to hallucinations, difficulty in handling complex tasks, and substantial safety and ethical risks, all of which limit their feasibility and trustworthiness in real-world applications. To overcome these limitations, LLM-based human-agent systems (LLM-HAS) incorporate human-provided information, feedback, or control into the agent system to enhance system performance, reliability and safety. This paper provides the first comprehensive and structured survey of LLM-HAS. It clarifies fundamental concepts, systematically presents core components shaping these systems, including environment & profiling, human feedback, interaction types, orchestration and communication, explores emerging applications, and discusses unique challenges and opportunities. By consolidating current knowledge and offering a structured overview, we aim to foster further research and innovation in this rapidly evolving interdisciplinary field. Paper lists and resources are available at https://github.com/HenryPengZou/Awesome-LLM-Based-Human-Agent-Systems.
Model Tensor Planning
Authors: An T. Le, Khai Nguyen, Minh Nhat Vu, João Carvalho, Jan Peters
Date: 2025-05-02 00:00:00
Tasks: model, Model Predictive Control, MuJoCo

Sampling-based model predictive control (MPC) offers strong performance in nonlinear and contact-rich robotic tasks, yet often suffers from poor exploration due to locally greedy sampling schemes. We propose \emph{Model Tensor Planning} (MTP), a novel sampling-based MPC framework that introduces high-entropy control trajectory generation through structured tensor sampling. By sampling over randomized multipartite graphs and interpolating control trajectories with B-splines and Akima splines, MTP ensures smooth and globally diverse control candidates. We further propose a simple $\beta$-mixing strategy that blends local exploitative and global exploratory samples within the modified Cross-Entropy Method (CEM) update, balancing control refinement and exploration. Theoretically, we show that MTP achieves asymptotic path coverage and maximum entropy in the control trajectory space in the limit of infinite tensor depth and width. Our implementation is fully vectorized using JAX and compatible with MuJoCo XLA, supporting \emph{Just-in-time} (JIT) compilation and batched rollouts for real-time control with online domain randomization. Through experiments on various challenging robotic tasks, ranging from dexterous in-hand manipulation to humanoid locomotion, we demonstrate that MTP outperforms standard MPC and evolutionary strategy baselines in task success and control robustness. Design and sensitivity ablations confirm the effectiveness of MTP tensor sampling structure, spline interpolation choices, and mixing strategy. Altogether, MTP offers a scalable framework for robust exploration in model-based planning and control.
Uncovering complementary information sharing in spider monkey collective foraging using higher-order spatial networks
Authors: Gabriel Ramos-Fernandez, Ross S. Walker, Matthew J. Silk, Denis Boyer, Sandra E. Smith-Aguilar
Date: 2025-05-02 00:00:00

Collectives are often able to process information in a distributed fashion, surpassing each individual member's processing capacity. In fission-fusion dynamics, where group members come together and split from others often, sharing complementary information about uniquely known foraging areas could allow a group to track a heterogenous foraging environment better than any group member on its own. We analyse the partial overlaps between individual core ranges, which we assume represent the knowledge of an individual during a given season. We identify sets of individuals whose overlap shows a balance between redundantly and uniquely known portions and we use simplicial complexes to represent these higher-order interactions. The structure of the simplicial complexes shows holes in various dimensions, revealing complementarity in the foraging information that is being shared. We propose that the complex spatial networks arising from fission-fusion dynamics allow for adaptive, collective processing of foraging information in dynamic environments.
Zero-Shot Document-Level Biomedical Relation Extraction via Scenario-based Prompt Design in Two-Stage with LLM
Authors: Lei Zhao, Ling Kang, Quan Guo
Date: 2025-05-02 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Relation Extraction

With the advent of artificial intelligence (AI), many researchers are attempting to extract structured information from document-level biomedical literature by fine-tuning large language models (LLMs). However, they face significant challenges such as the need for expensive hardware, like high-performance GPUs and the high labor costs associated with annotating training datasets, especially in biomedical realm. Recent research on LLMs, such as GPT-4 and Llama3, has shown promising performance in zero-shot settings, inspiring us to explore a novel approach to achieve the same results from unannotated full documents using general LLMs with lower hardware and labor costs. Our approach combines two major stages: named entity recognition (NER) and relation extraction (RE). NER identifies chemical, disease and gene entities from the document with synonym and hypernym extraction using an LLM with a crafted prompt. RE extracts relations between entities based on predefined relation schemas and prompts. To enhance the effectiveness of prompt, we propose a five-part template structure and a scenario-based prompt design principles, along with evaluation method to systematically assess the prompts. Finally, we evaluated our approach against fine-tuning and pre-trained models on two biomedical datasets: ChemDisGene and CDR. The experimental results indicate that our proposed method can achieve comparable accuracy levels to fine-tuning and pre-trained models but with reduced human and hardware expenses.
Solving Sokoban using Hierarchical Reinforcement Learning with Landmarks
Authors: Sergey Pastukhov
Date: 2025-04-06 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Sokoban

We introduce a novel hierarchical reinforcement learning (HRL) framework that performs top-down recursive planning via learned subgoals, successfully applied to the complex combinatorial puzzle game Sokoban. Our approach constructs a six-level policy hierarchy, where each higher-level policy generates subgoals for the level below. All subgoals and policies are learned end-to-end from scratch, without any domain knowledge. Our results show that the agent can generate long action sequences from a single high-level call. While prior work has explored 2-3 level hierarchies and subgoal-based planning heuristics, we demonstrate that deep recursive goal decomposition can emerge purely from learning, and that such hierarchies can scale effectively to hard puzzle domains.
Exploring a Large Language Model for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Development
Authors: Filipi Miranda Soares, Antonio Mauro Saraiva, Luís Ferreira Pires, Luiz Olavo Bonino da Silva Santos, Dilvan de Abreu Moreira, Fernando Elias Corrêa, Kelly Rosa Braghetto, Debora Pignatari Drucker, Alexandre Cláudio Botazzo Delbem
Date: 2025-04-25 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Managing scientific names in ontologies that represent species taxonomies is challenging due to the ever-evolving nature of these taxonomies. Manually maintaining these names becomes increasingly difficult when dealing with thousands of scientific names. To address this issue, this paper investigates the use of ChatGPT-4 to automate the development of the :Organism module in the Agricultural Product Types Ontology (APTO) for species classification. Our methodology involved leveraging ChatGPT-4 to extract data from the GBIF Backbone API and generate OWL files for further integration in APTO. Two alternative approaches were explored: (1) issuing a series of prompts for ChatGPT-4 to execute tasks via the BrowserOP plugin and (2) directing ChatGPT-4 to design a Python algorithm to perform analogous tasks. Both approaches rely on a prompting method where we provide instructions, context, input data, and an output indicator. The first approach showed scalability limitations, while the second approach used the Python algorithm to overcome these challenges, but it struggled with typographical errors in data handling. This study highlights the potential of Large language models like ChatGPT-4 to streamline the management of species names in ontologies. Despite certain limitations, these tools offer promising advancements in automating taxonomy-related tasks and improving the efficiency of ontology development.
Hierarchical Context Learning of object components for unsupervised semantic segmentation
Authors: Dong Bao, Jun Zhou, Gervase Tuxworth, Jue Zhang, Yongsheng Gao
Date: 2025-04-29 00:00:00
Proceeding: Pattern Recognition 2025 4
Tasks: Object, Self-Supervised Learning, Semantic Segmentation, Unsupervised Semantic Segmentation

Unsupervised Semantic Segmentation (USS) aims to learn semantically rich and dense representations without
relying on labels. Recent advances in self-supervised learning have demonstrated the potential of pretrained
vision transformers to capture patch-level semantic information, offering a promising direction to USS.
However, existing methods face challenges in constructing a discriminative spatial token embedding space that
consistently and effectively represents the well-structured semantic relationships among object components.
Inspired by Edwin Hancock’s pioneer work on hierarchical pattern analysis, we highlight the critical role
of hierarchical context to overcome this limitation. By modeling spatial relationships at multiple levels of
granularity, hierarchical context helps align related object parts while distinguishing them across semantic
groups. Based on this insight, we introduce Hierarchical Context Learning (HCL), a novel approach for USS that
enhances semantic consistency by integrating hierarchical context. HCL incorporates a novel parallel multi-level
vision transformer backbone to aggregate multi-level contextual information into object component tokens.
To uncover the semantic structure of objects, we propose Momentum-based Global Foreground–Background
Clustering (MoGoClustering) to cluster object components into coherent semantic groups and then calculate
their semantic centroids. To enforce intra-group semantic consistency and maximize inter-group separation
across spatial scales, we design a foreground–background-aware contrastive loss based on MoGoClustering.
Our method achieves state-of-the-art performance on the COCO-Stuff and Pascal VOC datasets, demonstrating
its ability to learn robust, context-aware, and discriminative object component semantics for USS. The code is
available at: https://github.com/dbaofd/HCL.
FinBERT-QA: Financial Question Answering with pre-trained BERT Language Models
Authors: Bithiah Yuan
Date: 2025-04-24 00:00:00
Tasks: Answer Selection, Information Retrieval, Question Answering, Re-Ranking, Specificity, Task 2

Motivated by the emerging demand in the financial industry for the automatic analysis of unstructured and structured data at scale, Question Answering (QA) systems can provide lucrative and competitive advantages to companies by facilitating the decision making of financial advisers. Consequently, we propose a novel financial QA system using the transformer-based pre-trained BERT language model to address the limitations of data scarcity and language specificity in the financial domain. Our system focuses on financial non-factoid answer selection, which retrieves a set of passage-level texts and selects the most relevant as the answer. To increase efficiency, we formulate the answer selection task as a re-ranking problem, in which our system consists of an Answer Retriever using BM25, a simple information retrieval approach, to first return a list of candidate answers, and an Answer Re-ranker built with variants of pre-trained BERT language models to re-rank and select the most relevant answers. We investigate various learning, further pre-training, and fine-tuning approaches for BERT. Our experiments suggest that FinBERT-QA, a model built from applying the Transfer and Adapt further fine-tuning and pointwise learning approach, is the most effective, improving the state-of-the-art results of task 2 of the FiQA dataset by 16% on MRR, 17% on NDCG, and 21% on Precision@1.
SCOPE-MRI: Bankart Lesion Detection as a Case Study in Data Curation and Deep Learning for Challenging Diagnoses
Authors: Sahil Sethi, Sai Reddy, Mansi Sakarvadia, Jordan Serotte, Darlington Nwaudo, Nicholas Maassen, Lewis Shi
Date: 2025-04-29 00:00:00
Tasks: Diagnostic, Lesion Detection, Specificity

While deep learning has shown strong performance in musculoskeletal imaging, existing work has largely focused on pathologies where diagnosis is not a clinical challenge, leaving more difficult problems underexplored, such as detecting Bankart lesions (anterior-inferior glenoid labral tears) on standard MRIs. Diagnosing these lesions is challenging due to their subtle imaging features, often leading to reliance on invasive MRI arthrograms (MRAs). This study introduces ScopeMRI, the first publicly available, expert-annotated dataset for shoulder pathologies, and presents a deep learning (DL) framework for detecting Bankart lesions on both standard MRIs and MRAs. ScopeMRI includes 586 shoulder MRIs (335 standard, 251 MRAs) from 558 patients who underwent arthroscopy. Ground truth labels were derived from intraoperative findings, the gold standard for diagnosis. Separate DL models for MRAs and standard MRIs were trained using a combination of CNNs and transformers. Predictions from sagittal, axial, and coronal views were ensembled to optimize performance. The models were evaluated on a 20% hold-out test set (117 MRIs: 46 MRAs, 71 standard MRIs). The models achieved an AUC of 0.91 and 0.93, sensitivity of 83% and 94%, and specificity of 91% and 86% for standard MRIs and MRAs, respectively. Notably, model performance on non-invasive standard MRIs matched or surpassed radiologists interpreting MRAs. External validation demonstrated initial generalizability across imaging protocols. This study demonstrates that DL models can achieve radiologist-level diagnostic performance on standard MRIs, reducing the need for invasive MRAs. By releasing ScopeMRI and a modular codebase for training and evaluating deep learning models on 3D medical imaging data, we aim to accelerate research in musculoskeletal imaging and support the development of new datasets for clinically challenging diagnostic tasks.
PhysioSync: Temporal and Cross-Modal Contrastive Learning Inspired by Physiological Synchronization for EEG-Based Emotion Recognition
Authors: Kai Cui, Jia Li, Yu Liu, Xuesong Zhang, Zhenzhen Hu, Meng Wang
Date: 2025-04-24 00:00:00
Tasks: Contrastive Learning, EEG, Emotion Recognition

Electroencephalography (EEG) signals provide a promising and involuntary reflection of brain activity related to emotional states, offering significant advantages over behavioral cues like facial expressions. However, EEG signals are often noisy, affected by artifacts, and vary across individuals, complicating emotion recognition. While multimodal approaches have used Peripheral Physiological Signals (PPS) like GSR to complement EEG, they often overlook the dynamic synchronization and consistent semantics between the modalities. Additionally, the temporal dynamics of emotional fluctuations across different time resolutions in PPS remain underexplored. To address these challenges, we propose PhysioSync, a novel pre-training framework leveraging temporal and cross-modal contrastive learning, inspired by physiological synchronization phenomena. PhysioSync incorporates Cross-Modal Consistency Alignment (CM-CA) to model dynamic relationships between EEG and complementary PPS, enabling emotion-related synchronizations across modalities. Besides, it introduces Long- and Short-Term Temporal Contrastive Learning (LS-TCL) to capture emotional synchronization at different temporal resolutions within modalities. After pre-training, cross-resolution and cross-modal features are hierarchically fused and fine-tuned to enhance emotion recognition. Experiments on DEAP and DREAMER datasets demonstrate PhysioSync's advanced performance under uni-modal and cross-modal conditions, highlighting its effectiveness for EEG-centered emotion recognition.
T-REX: Vision-Based System for Autonomous Leaf Detection and Grasp Estimation
Authors: Srecharan Selvam, Abhisesh Silwal, George Kantor
Date: 2025-05-03 00:00:00

T-Rex (The Robot for Extracting Leaf Samples) is a gantry-based robotic system developed for autonomous leaf localization, selection, and grasping in greenhouse environments. The system integrates a 6-degree-of-freedom manipulator with a stereo vision pipeline to identify and interact with target leaves. YOLOv8 is used for real-time leaf segmentation, and RAFT-Stereo provides dense depth maps, allowing the reconstruction of 3D leaf masks. These observations are processed through a leaf grasping algorithm that selects the optimal leaf based on clutter, visibility, and distance, and determines a grasp point by analyzing local surface flatness, top-down approachability, and margin from edges. The selected grasp point guides a trajectory executed by ROS-based motion controllers, driving a custom microneedle-equipped end-effector to clamp the leaf and simulate tissue sampling. Experiments conducted with artificial plants under varied poses demonstrate that the T-Rex system can consistently detect, plan, and perform physical interactions with plant-like targets, achieving a grasp success rate of 66.6\%. This paper presents the system architecture, implementation, and testing of T-Rex as a step toward plant sampling automation in Controlled Environment Agriculture (CEA).