pwc-archive/papers-with-abstracts huggingface dataset:
AnyEdit: Edit Any Knowledge Encoded in Language Models
Authors: Houcheng Jiang, Junfeng Fang, Ningyu Zhang, Guojun Ma, Mingyang Wan, Xiang Wang, Xiangnan He, Tat-Seng Chua
Date: 2025-02-08 00:00:00
Tasks: Form, Image Editing, knowledge editing, Model Editing

Large language models (LLMs) often produce incorrect or outdated information, necessitating efficient and precise knowledge updates. Current model editing methods, however, struggle with long-form knowledge in diverse formats, such as poetry, code snippets, and mathematical derivations. These limitations arise from their reliance on editing a single token's hidden state, a limitation we term "efficacy barrier". To solve this, we propose AnyEdit, a new autoregressive editing paradigm. It decomposes long-form knowledge into sequential chunks and iteratively edits the key token in each chunk, ensuring consistent and accurate outputs. Theoretically, we ground AnyEdit in the Chain Rule of Mutual Information, showing its ability to update any knowledge within LLMs. Empirically, it outperforms strong baselines by 21.5% on benchmarks including UnKEBench, AKEW, and our new EditEverything dataset for long-form diverse-formatted knowledge. Additionally, AnyEdit serves as a plug-and-play framework, enabling current editing methods to update knowledge with arbitrary length and format, significantly advancing the scope and practicality of LLM knowledge editing.
A Survey on LLM-based News Recommender Systems
Authors: Rongyao Wang, Veronica Liesaputra, Zhiyi Huang
Date: 2025-02-13 00:00:00
Tasks: Benchmarking, Fairness, Language Modeling, Language Modelling, Large Language Model, Privacy Preserving, Recommendation Systems, Survey

News recommender systems play a critical role in mitigating the information overload problem. In recent years, due to the successful applications of large language model technologies, researchers have utilized Discriminative Large Language Models (DLLMs) or Generative Large Language Models (GLLMs) to improve the performance of news recommender systems. Although several recent surveys review significant challenges for deep learning-based news recommender systems, such as fairness, privacy-preserving, and responsibility, there is a lack of a systematic survey on Large Language Model (LLM)-based news recommender systems. In order to review different core methodologies and explore potential issues systematically, we categorize DLLM-based and GLLM-based news recommender systems under the umbrella of LLM-based news recommender systems. In this survey, we first overview the development of deep learning-based news recommender systems. Then, we review LLM-based news recommender systems based on three aspects: news-oriented modeling, user-oriented modeling, and prediction-oriented modeling. Next, we examine the challenges from various perspectives, including datasets, benchmarking tools, and methodologies. Furthermore, we conduct extensive experiments to analyze how large language model technologies affect the performance of different news recommender systems. Finally, we comprehensively explore the future directions for LLM-based news recommendations in the era of LLMs.
MITO: Enabling Non-Line-of-Sight Perception using Millimeter-waves through Real-World Datasets and Simulation Tools
Authors: Laura Dodds, Tara Boroushaki, Fadel Adib
Date: 2025-02-14 00:00:00
Tasks: Semantic Segmentation

We present MITO, the first dataset of multi-spectral millimeter-wave (mmWave) images of everyday objects. Unlike visible light, mmWave signals can image through everyday occlusions (e.g., cardboard boxes, fabric, plastic). However, due to the dearth of publicly-available mmWave images and the interdisciplinary challenges in collecting and processing mmWave signals, it remains difficult today for computer vision researchers to develop mmWave-based non-line-of-sight perception algorithms and models. To overcome these challenges, we introduce a real-world dataset and open-source simulation tool for mmWave imaging. The dataset is acquired using a UR5 robotic arm with two mmWave radars operating at different frequencies and an RGB-D camera. Through a signal processing pipeline, we capture and create over 580 real-world 3D mmWave images from over 76 different objects in the YCB dataset, a standard dataset for robotics manipulation. We provide real-world mmWave images in line-of-sight and non-line-of-sight, as well as RGB-D images and ground truth segmentation masks. We also develop an open-source simulation tool that can be used to generate synthetic mmWave images for any 3D triangle mesh, which achieves a median F-Score of 94% when compared to real-world mmWave images. We show the usefulness of this dataset and simulation tool in multiple CV tasks in non-line-of-sight. First, we perform object segmentation for mmWave images using the segment anything model (SAM), and achieve a median precision and recall of 92.6% and 64%. Second, we train a classifier that can recognize objects in non-line-of-sight. It is trained on synthetic images and can classify real-world images with 85% accuracy. We believe MITO will be a valuable resource for computer vision researchers in developing non-line-of-sight perception, similar to how early camera-based datasets shaped the field.
Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices
Authors: Mohamed Aboelenien Ahmed, Kilian Pfeiffer, Ramin Khalili, Heba Khdr, Jörg Henkel
Date: 2025-02-14 00:00:00
Tasks: Federated Learning

Federated fine-tuning offers a promising approach for tuning Large Language Models (LLMs) on edge devices while preserving data privacy. However, fine-tuning these models on edge devices remains challenging due to high memory, communication, and computational demands. Zero-order optimization with task alignment provides a potential solution, enabling fine-tuning with inference-level memory requirements but requires a longer convergence time. In this paper, we propose Federated Split-Perturbation Zero-order Optimization (FedSPZO) that divides the network into two blocks, applying a different number of perturbations per block in a computationally effective way, achieving faster convergence. Our evaluation shows a $2.5 - 7\times $ reduction in computation overhead compared to zero-order state of the art techniques in federated learning.
Mapping bathymetry of inland water bodies on the North Slope of Alaska with Landsat using Random Forest
Authors: Mark L. Carroll, Margaret R. Wooten, Claire E. Simpson, Caleb S. Spradlin, Melanie J. Frost, Mariana Blanco-Rojas, Zachary W. Williams, Jordan A. Caraballo-Vega, Christopher S. R. Neigh
Date: 2025-02-14 00:00:00

The North Slope of Alaska is dominated by small waterbodies that provide critical ecosystem services for local population and wildlife. Detailed information on the depth of the waterbodies is scarce due to the challenges with collecting such information. In this work we have trained a machine learning (Random Forest Regressor) model to predict depth from multispectral Landsat data in waterbodies across the North Slope of Alaska. The greatest challenge is the scarcity of in situ data, which is expensive and difficult to obtain, to train the model. We overcame this challenge by using modeled depth predictions from a prior study as synthetic training data to provide a more diverse training data pool for the Random Forest. The final Random Forest model was more robust than models trained directly on the in situ data and when applied to 208 Landsat 8 scenes from 2016 to 2018 yielded a map with an overall $r^{2}$ value of 0.76 on validation. The final map has been made available through the Oak Ridge National Laboratory Distribute Active Archive Center (ORNL-DAAC). This map represents a first of its kind regional assessment of waterbody depth with per pixel estimates of depth for the entire North Slope of Alaska.
Evaluating the Meta- and Object-Level Reasoning of Large Language Models for Question Answering
Authors: Nick Ferguson, Liane Guillou, Alan Bundy, Kwabena Nuamah
Date: 2025-02-14 00:00:00
Tasks: Mathematical Reasoning, Object, Question Answering

Large Language Models (LLMs) excel in natural language tasks but still face challenges in Question Answering (QA) tasks requiring complex, multi-step reasoning. We outline the types of reasoning required in some of these tasks, and reframe them in terms of meta-level reasoning (akin to high-level strategic reasoning or planning) and object-level reasoning (embodied in lower-level tasks such as mathematical reasoning). Franklin, a novel dataset with requirements of meta- and object-level reasoning, is introduced and used along with three other datasets to evaluate four LLMs at question answering tasks requiring multiple steps of reasoning. Results from human annotation studies suggest LLMs demonstrate meta-level reasoning with high frequency, but struggle with object-level reasoning tasks in some of the datasets used. Additionally, evidence suggests that LLMs find the object-level reasoning required for the questions in the Franklin dataset challenging, yet they do exhibit strong performance with respect to the meta-level reasoning requirements.
Exploring the Camera Bias of Person Re-identification
Authors: Myungseo Song, Jin-Woo Park, Jong-Seok Lee
Date: 2025-02-14 00:00:00
Tasks: Person Re-Identification

We empirically investigate the camera bias of person re-identification (ReID) models. Previously, camera-aware methods have been proposed to address this issue, but they are largely confined to training domains of the models. We measure the camera bias of ReID models on unseen domains and reveal that camera bias becomes more pronounced under data distribution shifts. As a debiasing method for unseen domain data, we revisit feature normalization on embedding vectors. While the normalization has been used as a straightforward solution, its underlying causes and broader applicability remain unexplored. We analyze why this simple method is effective at reducing bias and show that it can be applied to detailed bias factors such as low-level image properties and body angle. Furthermore, we validate its generalizability across various models and benchmarks, highlighting its potential as a simple yet effective test-time postprocessing method for ReID. In addition, we explore the inherent risk of camera bias in unsupervised learning of ReID models. The unsupervised models remain highly biased towards camera labels even for seen domain data, indicating substantial room for improvement. Based on observations of the negative impact of camera-biased pseudo labels on training, we suggest simple training strategies to mitigate the bias. By applying these strategies to existing unsupervised learning algorithms, we show that significant performance improvements can be achieved with minor modifications.
Leveraging V2X for Collaborative HD Maps Construction Using Scene Graph Generation
Authors: Gamal Elghazaly, Raphael Frank
Date: 2025-02-14 00:00:00
Tasks: Graph Generation, Scene Graph Generation

High-Definition (HD) maps play a crucial role in autonomous vehicle navigation, complementing onboard perception sensors for improved accuracy and safety. Traditional HD map generation relies on dedicated mapping vehicles, which are costly and fail to capture real-time infrastructure changes. This paper presents HDMapLaneNet, a novel framework leveraging V2X communication and Scene Graph Generation to collaboratively construct a localized geometric layer of HD maps. The approach extracts lane centerlines from front-facing camera images, represents them as graphs, and transmits the data for global aggregation to the cloud via V2X. Preliminary results on the nuScenes dataset demonstrate superior association prediction performance compared to a state-of-the-art method.
RealCam-I2V: Real-World Image-to-Video Generation with Interactive Complex Camera Control
Authors: Teng Li, Guangcong Zheng, Rui Jiang, Shuigenzhan, Tao Wu, Yehao Lu, Yining Lin, Xi Li
Date: 2025-02-14 00:00:00
Tasks: 3D Scene Reconstruction, Depth Estimation, Image to Video Generation, Video Generation

Recent advancements in camera-trajectory-guided image-to-video generation offer higher precision and better support for complex camera control compared to text-based approaches. However, they also introduce significant usability challenges, as users often struggle to provide precise camera parameters when working with arbitrary real-world images without knowledge of their depth nor scene scale. To address these real-world application issues, we propose RealCam-I2V, a novel diffusion-based video generation framework that integrates monocular metric depth estimation to establish 3D scene reconstruction in a preprocessing step. During training, the reconstructed 3D scene enables scaling camera parameters from relative to absolute values, ensuring compatibility and scale consistency across diverse real-world images. In inference, RealCam-I2V offers an intuitive interface where users can precisely draw camera trajectories by dragging within the 3D scene. To further enhance precise camera control and scene consistency, we propose scene-constrained noise shaping, which shapes high-level noise and also allows the framework to maintain dynamic, coherent video generation in lower noise stages. RealCam-I2V achieves significant improvements in controllability and video quality on the RealEstate10K and out-of-domain images. We further enables applications like camera-controlled looping video generation and generative frame interpolation. We will release our absolute-scale annotation, codes, and all checkpoints. Please see dynamic results in https://zgctroy.github.io/RealCam-I2V.
Using MRNet to Predict Lunar Rock Categories Detected by Chang'e 5 Probe
Authors: Jin Cui, Yifei Zou, Siyuan Zhang
Date: 2025-02-14 00:00:00

China's Chang'e 5 mission has been a remarkable success, with the chang'e 5 lander traveling on the Oceanus Procellarum to collect images of the lunar surface. Over the past half century, people have brought back some lunar rock samples, but its quantity does not meet the need for research. Under current circumstances, people still mainly rely on the analysis of rocks on the lunar surface through the detection of lunar rover. The Oceanus Procellarum, chosen by Chang'e 5 mission, contains various kind of rock species. Therefore, we first applied to the National Astronomical Observatories of the China under the Chinese Academy of Sciences for the Navigation and Terrain Camera (NaTeCam) of the lunar surface image, and established a lunar surface rock image data set CE5ROCK. The data set contains 100 images, which randomly divided into training, validation and test set. Experimental results show that the identification accuracy testing on convolutional neural network (CNN) models like AlexNet or MobileNet is about to 40.0%. In order to make full use of the global information in Moon images, this paper proposes the MRNet (MoonRockNet) network architecture. The encoding structure of the network uses VGG16 for feature extraction, and the decoding part adds dilated convolution and commonly used U-Net structure on the original VGG16 decoding structure, which is more conducive to identify more refined but more sparsely distributed types of lunar rocks. We have conducted extensive experiments on the established CE5ROCK data set, and the experimental results show that MRNet can achieve more accurate rock type identification, and outperform other existing mainstream algorithms in the identification performance.
A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters
Authors: Shasvat Desai, Debasmita Ghose, Deep Chakraborty
Date: 2025-02-12 00:00:00
Tasks: Contrastive Learning

Visual contrastive learning aims to learn representations by contrasting similar (positive) and dissimilar (negative) pairs of data samples. The design of these pairs significantly impacts representation quality, training efficiency, and computational cost. A well-curated set of pairs leads to stronger representations and faster convergence. As contrastive pre-training sees wider adoption for solving downstream tasks, data curation becomes essential for optimizing its effectiveness. In this survey, we attempt to create a taxonomy of existing techniques for positive and negative pair curation in contrastive learning, and describe them in detail.
Vision-Language Models for Edge Networks: A Comprehensive Survey
Authors: Ahmed Sharshar, Latif U. Khan, Waseem Ullah, Mohsen Guizani
Date: 2025-02-11 00:00:00
Tasks: Autonomous Vehicles, Image Captioning, Knowledge Distillation, Model Compression, Quantization, Question Answering, Survey, Visual Question Answering

Vision Large Language Models (VLMs) combine visual understanding with natural language processing, enabling tasks like image captioning, visual question answering, and video analysis. While VLMs show impressive capabilities across domains such as autonomous vehicles, smart surveillance, and healthcare, their deployment on resource-constrained edge devices remains challenging due to processing power, memory, and energy limitations. This survey explores recent advancements in optimizing VLMs for edge environments, focusing on model compression techniques, including pruning, quantization, knowledge distillation, and specialized hardware solutions that enhance efficiency. We provide a detailed discussion of efficient training and fine-tuning methods, edge deployment challenges, and privacy considerations. Additionally, we discuss the diverse applications of lightweight VLMs across healthcare, environmental monitoring, and autonomous systems, illustrating their growing impact. By highlighting key design strategies, current challenges, and offering recommendations for future directions, this survey aims to inspire further research into the practical deployment of VLMs, ultimately making advanced AI accessible in resource-limited settings.
Survey on AI-Generated Media Detection: From Non-MLLM to MLLM
Authors: Yueying Zou, Peipei Li, Zekun Li, Huaibo Huang, Xing Cui, Xuannan Liu, Chenghanyu Zhang, Ran He
Date: 2025-02-07 00:00:00

The proliferation of AI-generated media poses significant challenges to information authenticity and social trust, making reliable detection methods highly demanded. Methods for detecting AI-generated media have evolved rapidly, paralleling the advancement of Multimodal Large Language Models (MLLMs). Current detection approaches can be categorized into two main groups: Non-MLLM-based and MLLM-based methods. The former employs high-precision, domain-specific detectors powered by deep learning techniques, while the latter utilizes general-purpose detectors based on MLLMs that integrate authenticity verification, explainability, and localization capabilities. Despite significant progress in this field, there remains a gap in literature regarding a comprehensive survey that examines the transition from domain-specific to general-purpose detection methods. This paper addresses this gap by providing a systematic review of both approaches, analyzing them from single-modal and multi-modal perspectives. We present a detailed comparative analysis of these categories, examining their methodological similarities and differences. Through this analysis, we explore potential hybrid approaches and identify key challenges in forgery detection, providing direction for future research. Additionally, as MLLMs become increasingly prevalent in detection tasks, ethical and security considerations have emerged as critical global concerns. We examine the regulatory landscape surrounding Generative AI (GenAI) across various jurisdictions, offering valuable insights for researchers and practitioners in this field.
Preference Alignment on Diffusion Model: A Comprehensive Survey for Image Generation and Editing
Authors: Sihao Wu, Xiaonan Si, Chi Xing, Jianhong Wang, Gaojie Jin, Guangliang Cheng, Lijun Zhang, Xiaowei Huang
Date: 2025-02-10 00:00:00
Tasks: Autonomous Driving, Image Generation

The integration of preference alignment with diffusion models (DMs) has emerged as a transformative approach to enhance image generation and editing capabilities. Although integrating diffusion models with preference alignment strategies poses significant challenges for novices at this intersection, comprehensive and systematic reviews of this subject are still notably lacking. To bridge this gap, this paper extensively surveys preference alignment with diffusion models in image generation and editing. First, we systematically review cutting-edge optimization techniques such as reinforcement learning with human feedback (RLHF), direct preference optimization (DPO), and others, highlighting their pivotal role in aligning preferences with DMs. Then, we thoroughly explore the applications of aligning preferences with DMs in autonomous driving, medical imaging, robotics, and more. Finally, we comprehensively discuss the challenges of preference alignment with DMs. To our knowledge, this is the first survey centered on preference alignment with DMs, providing insights to drive future innovation in this dynamic area.
Handwritten Text Recognition: A Survey
Authors: Carlos Garrido-Munoz, Antonio Rios-Vila, Jorge Calvo-Zaragoza
Date: 2025-02-12 00:00:00
Tasks: Benchmarking, Handwritten Text Recognition, HTR, Survey

Handwritten Text Recognition (HTR) has become an essential field within pattern recognition and machine learning, with applications spanning historical document preservation to modern data entry and accessibility solutions. The complexity of HTR lies in the high variability of handwriting, which makes it challenging to develop robust recognition systems. This survey examines the evolution of HTR models, tracing their progression from early heuristic-based approaches to contemporary state-of-the-art neural models, which leverage deep learning techniques. The scope of the field has also expanded, with models initially capable of recognizing only word-level content progressing to recent end-to-end document-level approaches. Our paper categorizes existing work into two primary levels of recognition: (1) \emph{up to line-level}, encompassing word and line recognition, and (2) \emph{beyond line-level}, addressing paragraph- and document-level challenges. We provide a unified framework that examines research methodologies, recent advances in benchmarking, key datasets in the field, and a discussion of the results reported in the literature. Finally, we identify pressing research challenges and outline promising future directions, aiming to equip researchers and practitioners with a roadmap for advancing the field.
Face Deepfakes - A Comprehensive Review
Authors: Tharindu Fernando, Darshana Priyasad, Sridha Sridharan, Arun Ross, Clinton Fookes
Date: 2025-02-13 00:00:00
Tasks: Face Swapping

In recent years, remarkable advancements in deep- fake generation technology have led to unprecedented leaps in its realism and capabilities. Despite these advances, we observe a notable lack of structured and deep analysis deepfake technology. The principal aim of this survey is to contribute a thorough theoretical analysis of state-of-the-art face deepfake generation and detection methods. Furthermore, we provide a coherent and systematic evaluation of the implications of deepfakes on face biometric recognition approaches. In addition, we outline key applications of face deepfake technology, elucidating both positive and negative applications of the technology, provide a detailed discussion regarding the gaps in existing research, and propose key research directions for further investigation.