pwc-archive/papers-with-abstracts huggingface dataset:
UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler
Authors: Luigi Piccinelli, Christos Sakaridis, Yung-Hsu Yang, Mattia Segu, Siyuan Li, Wim Abbeloos, Luc van Gool
Date: 2025-02-27 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However, the remarkable accuracy of recent MMDE methods is confined to their training domains. These methods fail to generalize to unseen domains even in the presence of moderate domain gaps, which hinders their practical applicability. We propose a new model, UniDepthV2, capable of reconstructing metric 3D scenes from solely single images across domains. Departing from the existing MMDE paradigm, UniDepthV2 directly predicts metric 3D points from the input image at inference time without any additional information, striving for a universal and flexible MMDE solution. In particular, UniDepthV2 implements a self-promptable camera module predicting a dense camera representation to condition depth features. Our model exploits a pseudo-spherical output representation, which disentangles the camera and depth representations. In addition, we propose a geometric invariance loss that promotes the invariance of camera-prompted depth features. UniDepthV2 improves its predecessor UniDepth model via a new edge-guided loss which enhances the localization and sharpness of edges in the metric depth outputs, a revisited, simplified and more efficient architectural design, and an additional uncertainty-level output which enables downstream tasks requiring confidence. Thorough evaluations on ten depth datasets in a zero-shot regime consistently demonstrate the superior performance and generalization of UniDepthV2. Code and models are available at https://github.com/lpiccinelli-eth/UniDepth
WalnutData: A UAV Remote Sensing Dataset of Green Walnuts and Model Evaluation
Authors: Mingjie Wu, Chenggui Yang, Huihua Wang, Chen Xue, Yibo Wang, Haoyu Wang, Yansong Wang, Can Peng, Yuqi Han, Ruoyu Li, Lijun Yun, Zaiqing Chen, Songfan Shi, Luhao Fang, Shuyi Wan, Tingfeng Li, Shuangyao Liu, Haotian Feng
Date: 2025-02-27 00:00:00
Tasks: 2D Object Detection, Object Detection

The UAV technology is gradually maturing and can provide extremely powerful support for smart agriculture and precise monitoring. Currently, there is no dataset related to green walnuts in the field of agricultural computer vision. Thus, in order to promote the algorithm design in the field of agricultural computer vision, we used UAV to collect remote-sensing data from 8 walnut sample plots. Considering that green walnuts are subject to various lighting conditions and occlusion, we constructed a large-scale dataset with a higher-granularity of target features - WalnutData. This dataset contains a total of 30,240 images and 706,208 instances, and there are 4 target categories: being illuminated by frontal light and unoccluded (A1), being backlit and unoccluded (A2), being illuminated by frontal light and occluded (B1), and being backlit and occluded (B2). Subsequently, we evaluated many mainstream algorithms on WalnutData and used these evaluation results as the baseline standard. The dataset and all evaluation results can be obtained at https://github.com/1wuming/WalnutData.
Community Detection by ELPMeans: An Unsupervised Approach That Uses Laplacian Centrality and Clustering
Authors: Shahin Momenzadeh, Rojiar Pir Mohammadiani
Date: 2025-02-27 00:00:00
Tasks: Community Detection

Community detection in network analysis has become more intricate due to the recent hike in social networks (Cai et al., 2024). This paper suggests a new approach named ELPMeans that strives to address this challenge. For community detection in the whole network, ELPMeans combines Laplacian, Hierarchical Clustering as well as K-means algorithms. Our technique employs Laplacian centrality and minimum distance metrics for central node identification while k-means learning is used for efficient convergence to final community structure. Remarkably, ELPMeans is an unsupervised method which is not only simple to implement but also effectively tackles common problems such as random initialization of central nodes, or finding of number of communities (K). Experimental results show that our algorithm improves accuracy and reduces time complexity considerably outperforming recent approaches on real world networks. Moreover, our approach has a wide applicability range in various community detection tasks even with nonconvex shapes and no prior knowledge about the number of communities present.
Attention Distillation: A Unified Approach to Visual Characteristics Transfer
Authors: Yang Zhou, Xu Gao, Zichong Chen, Hui Huang
Date: 2025-02-27 00:00:00
Proceeding: CVPR 2025 1
Tasks: Denoising, Image Generation

Recent advances in generative diffusion models have shown a notable inherent understanding of image style and semantics. In this paper, we leverage the self-attention features from pretrained diffusion networks to transfer the visual characteristics from a reference to generated images. Unlike previous work that uses these features as plug-and-play attributes, we propose a novel attention distillation loss calculated between the ideal and current stylization results, based on which we optimize the synthesized image via backpropagation in latent space. Next, we propose an improved Classifier Guidance that integrates attention distillation loss into the denoising sampling process, further accelerating the synthesis and enabling a broad range of image generation applications. Extensive experiments have demonstrated the extraordinary performance of our approach in transferring the examples' style, appearance, and texture to new images in synthesis. Code is available at https://github.com/xugao97/AttentionDistillation.
A Fast Blind Deblurring Algorithm Using Local Gradient Product Prior
Authors: Jixuan Liang, Yanshan Li
Date: 2024-03-18 00:00:00
Proceeding: ICASSP 2024 3
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring

Blind image deblurring is the restoration of latent clear images from blurred images without knowing the blur kernel.
Recently, a large number of priors have been proposed to effectively address the ill-posed nature of blind deblurring.
However, most methods approximate the proposed priors and only use the first-order term of the blurred image. We
observe the gradient inner product of clear images is significantly larger than that of blurry ones. In this paper, we
propose a novel Local Gradient Product (LGP) prior based on this observation. This prior not only offers a more accurate approximation but also uses the relationship between image pixels involving quadratic terms. The employment of
the LGP prior avoids the inversion of large matrices, thereby improving solution efficiency. Extensive experiments demonstrate that our algorithm achieves state-of-the-art performance and competitive running speed on benchmark datasets. Our MATLAB code and experimental results are available at github.com/JixuanLiang/deblur-LGP.
HVI: A New color space for Low-light Image Enhancement
Authors: Qingsen Yan, Yixu Feng, Cheng Zhang, Guansong Pang, Kangbiao Shi, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang
Date: 2025-02-27 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Enhancement, Low-Light Image Enhancement

Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods are based on standard RGB (sRGB) space, which often produce color bias and brightness artifacts due to inherent high color sensitivity in sRGB. While converting the images using Hue, Saturation and Value (HSV) color space helps resolve the brightness issue, it introduces significant red and black noise artifacts. To address this issue, we propose a new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable intensity. The former enforces small distances for red coordinates to remove the red artifacts, while the latter compresses the low-light regions to remove the black artifacts. To fully leverage the chromatic and intensity information, a novel Color and Intensity Decoupling Network (CIDNet) is further introduced to learn accurate photometric mapping function under different lighting conditions in the HVI space. Comprehensive results from benchmark and ablation experiments show that the proposed HVI color space with CIDNet outperforms the state-of-the-art methods on 10 datasets. The code is available at https://github.com/Fediory/HVI-CIDNet.
PII-Bench: Evaluating Query-Aware Privacy Protection Systems
Authors: Hao Shen, Zhouhong Gu, Haokai Hong, Weili Han
Date: 2025-02-25 00:00:00
Tasks: PII Masking

The widespread adoption of Large Language Models (LLMs) has raised significant privacy concerns regarding the exposure of personally identifiable information (PII) in user prompts. To address this challenge, we propose a query-unrelated PII masking strategy and introduce PII-Bench, the first comprehensive evaluation framework for assessing privacy protection systems. PII-Bench comprises 2,842 test samples across 55 fine-grained PII categories, featuring diverse scenarios from single-subject descriptions to complex multi-party interactions. Each sample is carefully crafted with a user query, context description, and standard answer indicating query-relevant PII. Our empirical evaluation reveals that while current models perform adequately in basic PII detection, they show significant limitations in determining PII query relevance. Even state-of-the-art LLMs struggle with this task, particularly in handling complex multi-subject scenarios, indicating substantial room for improvement in achieving intelligent PII masking.
Comparative Performance Analysis of Numerical Discretization Methods for Electrochemical Models of Lithium-ion Batteries
Authors: Feng Guo, Luis D. Couto
Date: 2025-02-27 00:00:00

This study evaluates numerical discretization methods for the Single Particle Model (SPM) used in electrochemical modeling. The methods include the Finite Difference Method (FDM), spectral methods, Pad\'e approximation, and parabolic approximation. Evaluation criteria are accuracy, execution time, and memory usage, aiming to guide method selection for electrochemical models. Under constant current conditions, the FDM explicit Euler and Runge-Kutta methods show significant errors, while the FDM implicit Euler method improves accuracy with more nodes. The spectral method achieves the best accuracy and convergence with as few as five nodes. The Pad\'e approximation exhibits increasing errors with higher current, and the parabolic approximation shows higher errors than the converged spectral and FDM implicit Euler methods. Under dynamic conditions, frequency domain analysis indicates that the FDM, spectral, and Pad\'e approximation methods improve high-frequency response by increasing node count or method order. In terms of execution time, the parabolic method is fastest, followed by the Pad\'e approximation. The spectral method is faster than FDM, while the FDM implicit Euler method is the slowest. Memory usage is lowest for the parabolic and Pad\'e methods, moderate for FDM, and highest for the spectral method. These findings provide practical guidance for selecting discretization methods under different operating scenarios.
DIPSER: A Dataset for In-Person Student1 Engagement Recognition in the Wild
Authors: Luis Marquez-Carpintero, Sergio Suescun-Ferrandiz, Carolina Lorenzo Álvarez, Jorge Fernandez-Herrero, Diego Viejo, Rosabel Roig-Vila, Miguel Cazorla
Date: 2025-02-27 00:00:00

In this paper, a novel dataset is introduced, designed to assess student attention within in-person classroom settings. This dataset encompasses RGB camera data, featuring multiple cameras per student to capture both posture and facial expressions, in addition to smartwatch sensor data for each individual. This dataset allows machine learning algorithms to be trained to predict attention and correlate it with emotion. A comprehensive suite of attention and emotion labels for each student is provided, generated through self-reporting as well as evaluations by four different experts. Our dataset uniquely combines facial and environmental camera data, smartwatch metrics, and includes underrepresented ethnicities in similar datasets, all within in-the-wild, in-person settings, making it the most comprehensive dataset of its kind currently available. The dataset presented offers an extensive and diverse collection of data pertaining to student interactions across different educational contexts, augmented with additional metadata from other tools. This initiative addresses existing deficiencies by offering a valuable resource for the analysis of student attention and emotion in face-to-face lessons.
Scalable Graph Attention-based Instance Selection via Mini-Batch Sampling and Hierarchical Hashing
Authors: Zahiriddin Rustamov, Ayham Zaitouny, Nazar Zaki
Date: 2025-02-27 00:00:00
Tasks: Graph Attention, graph construction

Instance selection (IS) is important in machine learning for reducing dataset size while keeping key characteristics. Current IS methods often struggle with capturing complex relationships in high-dimensional spaces and scale with large datasets. This paper introduces a graph attention-based instance selection (GAIS) method that uses attention mechanisms to identify informative instances through their structural relationships in graph representations. We present two approaches for scalable graph construction: a distance-based mini-batch sampling technique that reduces computation through strategic batch processing, and a hierarchical hashing approach that allows for efficient similarity computation through random projections. The mini-batch approach keeps class distributions through stratified sampling, while the hierarchical hashing method captures relationships at multiple granularities through single-level, multi-level, and multi-view variants. Experiments across 39 datasets show that GAIS achieves reduction rates above 96\% while maintaining or improving model performance relative to state-of-the-art IS methods. The findings shows that the distance-based mini-batch approach offers an optimal balance of efficiency and effectiveness for large-scale datasets, while multi-view variants provide superior performance for complex, high-dimensional data, demonstrating that attention-based importance scoring can effectively identify instances crucial for maintaining decision boundaries without requiring exhaustive pairwise comparisons.
HaLoRA: Hardware-aware Low-Rank Adaptation for Large Language Models Based on Hybrid Compute-in-Memory Architecture
Authors: Taiqiang Wu, Chenchen Ding, Wenyong Zhou, Yuxin Cheng, Xincheng Feng, Shuqi Wang, Chufan Shi, Zhengwu Liu, Ngai Wong
Date: 2025-02-27 00:00:00

Low-rank adaptation (LoRA) is a predominant parameter-efficient finetuning method to adapt large language models (LLMs) for downstream tasks. In this paper, we first propose to deploy the LoRA-finetuned LLMs on the hybrid compute-in-memory (CIM) architecture (i.e., pretrained weights onto RRAM and LoRA onto SRAM). To address performance degradation from RRAM's inherent noise, we design a novel Hardware-aware Low-rank Adaption (HaLoRA) method, aiming to train a LoRA branch that is both robust and accurate by aligning the training objectives under both ideal and noisy conditions. Experiments finetuning LLaMA 3.2 1B and 3B demonstrate HaLoRA's effectiveness across multiple reasoning tasks, achieving up to 22.7 improvement in average score while maintaining robustness at various noise levels.
HALO: Hardware-aware quantization with low critical-path-delay weights for LLM acceleration
Authors: Rohan Juneja, Shivam Aggarwal, Safeen Huda, Tulika Mitra, Li-Shiuan Peh
Date: 2025-02-27 00:00:00
Tasks: Quantization

Quantization is critical for efficiently deploying large language models (LLMs). Yet conventional methods remain hardware-agnostic, limited to bit-width constraints, and do not account for intrinsic circuit characteristics such as the timing behaviors and energy profiles of Multiply-Accumulate (MAC) units. This disconnect from circuit-level behavior limits the ability to exploit available timing margins and energy-saving opportunities, reducing the overall efficiency of deployment on modern accelerators. To address these limitations, we propose HALO, a versatile framework for Hardware-Aware Post-Training Quantization (PTQ). Unlike traditional methods, HALO explicitly incorporates detailed hardware characteristics, including critical-path timing and power consumption, into its quantization approach. HALO strategically selects weights with low critical-path-delays enabling higher operational frequencies and dynamic frequency scaling without disrupting the architecture's dataflow. Remarkably, HALO achieves these improvements with only a few dynamic voltage and frequency scaling (DVFS) adjustments, ensuring simplicity and practicality in deployment. Additionally, by reducing switching activity within the MAC units, HALO effectively lowers energy consumption. Evaluations on accelerators such as Tensor Processing Units (TPUs) and Graphics Processing Units (GPUs) demonstrate that HALO significantly enhances inference efficiency, achieving average performance improvements of 270% and energy savings of 51% over baseline quantization methods, all with minimal impact on accuracy.
How Much is Enough? The Diminishing Returns of Tokenization Training Data
Authors: Varshini Reddy, Craig W. Schmidt, Yuval Pinter, Chris Tanner
Date: 2025-02-27 00:00:00
Tasks: Attribute

Tokenization, a crucial initial step in natural language processing, is governed by several key parameters, such as the tokenization algorithm, vocabulary size, pre-tokenization strategy, inference strategy, and training data corpus. This paper investigates the impact of an often-overlooked hyperparameter, tokenizer training data size. We train BPE, UnigramLM, and WordPiece tokenizers across various vocabulary sizes using English training data ranging from 1GB to 900GB. Our findings reveal diminishing returns as training data size increases beyond roughly 150GB, suggesting a practical limit to the improvements in tokenization quality achievable through additional data. We analyze this phenomenon and attribute the saturation effect to constraints introduced by the pre-tokenization stage. We then demonstrate the extent to which these findings can generalize by experimenting on data in Russian, a language typologically distant from English. For Russian text, we observe diminishing returns after training a tokenizer from 200GB of data, which is approximately 33% more than when training on English. These results provide valuable insights for optimizing the tokenization process by reducing the compute required for training on large corpora and suggest promising directions for future research in tokenization algorithms.
ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model
Authors: Chuanliu Fan, Ziqiang Cao, Zicheng Ma, Nan Yu, Yimin Peng, Jun Zhang, Yiqin Gao, Guohong Fu
Date: 2025-02-27 00:00:00
Tasks: Bayesian Optimization, Drug Discovery, Language Modeling, Language Modelling, Large Language Model, Property Prediction, Text Generation

Goal-oriented de novo molecule design, namely generating molecules with specific property or substructure constraints, is a crucial yet challenging task in drug discovery. Existing methods, such as Bayesian optimization and reinforcement learning, often require training multiple property predictors and struggle to incorporate substructure constraints. Inspired by the success of Large Language Models (LLMs) in text generation, we propose ChatMol, a novel approach that leverages LLMs for molecule design across diverse constraint settings. Initially, we crafted a molecule representation compatible with LLMs and validated its efficacy across multiple online LLMs. Afterwards, we developed specific prompts geared towards diverse constrained molecule generation tasks to further fine-tune current LLMs while integrating feedback learning derived from property prediction. Finally, to address the limitations of LLMs in numerical recognition, we referred to the position encoding method and incorporated additional encoding for numerical values within the prompt. Experimental results across single-property, substructure-property, and multi-property constrained tasks demonstrate that ChatMol consistently outperforms state-of-the-art baselines, including VAE and RL-based methods. Notably, in multi-objective binding affinity maximization task, ChatMol achieves a significantly lower KD value of 0.25 for the protein target ESR1, while maintaining the highest overall performance, surpassing previous methods by 4.76%. Meanwhile, with numerical enhancement, the Pearson correlation coefficient between the instructed property values and those of the generated molecules increased by up to 0.49. These findings highlight the potential of LLMs as a versatile framework for molecule generation, offering a promising alternative to traditional latent space and RL-based approaches.
Validating the predictions of mathematical models describing tumor growth and treatment response
Authors: Guillermo Lorenzo, David A. Hormuth II, Chengyue Wu, Graham Pash, Anirban Chaudhuri, Ernesto A. B. F. Lima, Lois C. Okereke, Reshmi Patel, Karen Willcox, Thomas E. Yankeelov
Date: 2025-02-26 00:00:00
Tasks: Management, Model Selection

Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticipation of tumor outcomes to hasten the discovery of cancer mechanisms and personalize disease management. To address these limitations, individualized cancer forecasts have been shown to predict tumor growth and therapeutic response, inform treatment optimization, and guide experimental efforts. These predictions are obtained via computer simulations of mathematical models that are constrained with data from a patient's cancer and experiments. This book chapter addresses the validation of these mathematical models to forecast tumor growth and treatment response. We start with an overview of mathematical modeling frameworks, model selection techniques, and fundamental metrics. We then describe the usual strategies employed to validate cancer forecasts in preclinical and clinical scenarios. Finally, we discuss existing barriers in validating these predictions along with potential strategies to address them.