pwc-archive/papers-with-abstracts huggingface dataset:
LGPS: A Lightweight GAN-Based Approach for Polyp Segmentation in Colonoscopy Images
Authors: Fiseha B. Tesema, Alejandro Guerra Manzanares, Tianxiang Cui, Qian Zhang, Moses Solomon, Sean He
Date: 2025-03-24 00:00:00

Colorectal cancer (CRC) is a major global cause of cancer-related deaths, with early polyp detection and removal during colonoscopy being crucial for prevention. While deep learning methods have shown promise in polyp segmentation, challenges such as high computational costs, difficulty in segmenting small or low-contrast polyps, and limited generalizability across datasets persist. To address these issues, we propose LGPS, a lightweight GAN-based framework for polyp segmentation. LGPS incorporates three key innovations: (1) a MobileNetV2 backbone enhanced with modified residual blocks and Squeeze-and-Excitation (ResE) modules for efficient feature extraction; (2) Convolutional Conditional Random Fields (ConvCRF) for precise boundary refinement; and (3) a hybrid loss function combining Binary Cross-Entropy, Weighted IoU Loss, and Dice Loss to address class imbalance and enhance segmentation accuracy. LGPS is validated on five benchmark datasets and compared with state-of-the-art(SOTA) methods. On the largest and challenging PolypGen test dataset, LGPS achieves a Dice of 0.7299 and an IoU of 0.7867, outperformed all SOTA works and demonstrating robust generalization. With only 1.07 million parameters, LGPS is 17 times smaller than the smallest existing model, making it highly suitable for real-time clinical applications. Its lightweight design and strong performance underscore its potential for improving early CRC diagnosis. Code is available at https://github.com/Falmi/LGPS/.
ILDNet: A Novel Deep Learning Framework for Interstitial Lung Disease Identification Using Respiratory Sounds
Authors: Arka Roy, Udit Satija
Date: 2024-08-22 00:00:00
Proceeding: International Conference on Signal Processing and Communications (SPCOM) 2024 8
Tasks: Audio Classification, Diagnostic, Lung Disease Classification, Lung Sound Classification, Medical waveform analysis, Specificity, Time Series Classification

Interstitial lung disease (ILD) is a collection of pulmonary adventitious conditions that induce scarring of the lung parenchyma, fibrosis, and inflammation. ILD encompasses over 200 chronic respiratory diseases that gradually damage the lung tissues and make it difficult to acquire adequate oxygen in the lungs. Therefore, it is essential to identify and diagnose diseases early to prevent their progression. ILDs are often characterized by abnormal respiratory sounds (RSs) such as crackles and squawks as a result of anatomical faults in the respiratory pathway produced by the disease. In this paper, for the first time, we propose a novel sinc convolution-based residual convolutional deep learning architecture, namely the ILDNet, for categorizing the ILD-affected RSs. The proposed framework comprises two major stages: (a) preprocessing of the input RS and (b) classification of the RSs using the proposed ILDNet. The proposed framework is extensively evaluated using the RSs from the publicly available BRACETS and KAUH datasets, and the experimental results show that our proposed ILDNet framework achieves an accuracy, sensitivity, and specificity of 81.25%, 78.85%, and 83.33%. These results also pave the way for future research on the potential use of RSs to identify reliable biomarkers for early-stage ILD identification.
LocDiffusion: Identifying Locations on Earth by Diffusing in the Hilbert Space
Authors: Zhangyu Wang, Jielu Zhang, Zhongliang Zhou, Qian Cao, Nemin Wu, Zeping Liu, Lan Mu, Yang song, Yiqun Xie, Ni Lao, Gengchen Mai
Date: 2025-03-23 00:00:00
Tasks: Image Retrieval

Image geolocalization is a fundamental yet challenging task, aiming at inferring the geolocation on Earth where an image is taken. Existing methods approach it either via grid-based classification or via image retrieval. Their performance significantly suffers when the spatial distribution of test images does not align with such choices. To address these limitations, we propose to leverage diffusion as a mechanism for image geolocalization. To avoid the problematic manifold reprojection step in diffusion, we developed a novel spherical positional encoding-decoding framework, which encodes points on a spherical surface (e.g., geolocations on Earth) into a Hilbert space of Spherical Harmonics coefficients and decodes points (geolocations) by mode-seeking. We call this type of position encoding Spherical Harmonics Dirac Delta (SHDD) Representation. We also propose a novel SirenNet-based architecture called CS-UNet to learn the conditional backward process in the latent SHDD space by minimizing a latent KL-divergence loss. We train a conditional latent diffusion model called LocDiffusion that generates geolocations under the guidance of images -- to the best of our knowledge, the first generative model for image geolocalization by diffusing geolocation information in a hidden location embedding space. We evaluate our method against SOTA image geolocalization baselines. LocDiffusion achieves competitive geolocalization performance and demonstrates significantly stronger generalizability to unseen geolocations.
Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting
Authors: Maochen Yang, Zekun Li, Jian Zhang, Lei Qi, Yinghuan Shi
Date: 2025-03-23 00:00:00
Proceeding: CVPR 2025 1
Tasks: Crowd Counting, Data Augmentation, regression

Semi-supervised crowd counting is crucial for addressing the high annotation costs of densely populated scenes. Although several methods based on pseudo-labeling have been proposed, it remains challenging to effectively and accurately utilize unlabeled data. In this paper, we propose a novel framework called Taste More Taste Better (TMTB), which emphasizes both data and model aspects. Firstly, we explore a data augmentation technique well-suited for the crowd counting task. By inpainting the background regions, this technique can effectively enhance data diversity while preserving the fidelity of the entire scenes. Secondly, we introduce the Visual State Space Model as backbone to capture the global context information from crowd scenes, which is crucial for extremely crowded, low-light, and adverse weather scenarios. In addition to the traditional regression head for exact prediction, we employ an Anti-Noise classification head to provide less exact but more accurate supervision, since the regression head is sensitive to noise in manual annotations. We conduct extensive experiments on four benchmark datasets and show that our method outperforms state-of-the-art methods by a large margin. Code is publicly available on https://github.com/syhien/taste_more_taste_better.
LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL
Authors: Yihan Wang, Peiyu Liu
Date: 2025-03-24 00:00:00
Tasks: Retrieval, Text to SQL, Text-To-SQL

Schema linking is a critical bottleneck in achieving human-level performance in Text-to-SQL tasks, particularly in real-world large-scale multi-database scenarios. Addressing schema linking faces two major challenges: (1) Database Retrieval: selecting the correct database from a large schema pool in multi-database settings, while filtering out irrelevant ones. (2) Schema Item Grounding: accurately identifying the relevant tables and columns from within a large and redundant schema for SQL generation. To address this, we introduce LinkAlign, a novel framework that can effectively adapt existing baselines to real-world environments by systematically addressing schema linking. Our framework comprises three key steps: multi-round semantic enhanced retrieval and irrelevant information isolation for Challenge 1, and schema extraction enhancement for Challenge 2. We evaluate our method performance of schema linking on the SPIDER and BIRD benchmarks, and the ability to adapt existing Text-to-SQL models to real-world environments on the SPIDER 2.0-lite benchmark. Experiments show that LinkAlign outperforms existing baselines in multi-database settings, demonstrating its effectiveness and robustness. On the other hand, our method ranks highest among models excluding those using long chain-of-thought reasoning LLMs. This work bridges the gap between current research and real-world scenarios, providing a practical solution for robust and scalable schema linking. The codes are available at https://github.com/Satissss/LinkAlign.
Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
Authors: Moin Uddin Maruf, Sungmin Kim, Zeeshan Ahmad
Date: 2025-03-23 00:00:00

Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties. Message-passing graph neural networks, commonly used in these MLIPs, rely on local descriptor-based symmetry functions to model atomic interactions. However, such local descriptor-based approaches struggle with systems exhibiting long-range interactions, charge transfer, and compositional heterogeneity. In this work, we develop a new equivariant MLIP incorporating long-range Coulomb interactions through explicit treatment of electronic degrees of freedom, specifically global charge distribution within the system. This is achieved using a charge equilibration scheme based on predicted atomic electronegativities. We systematically evaluate our model across a range of benchmark periodic and non-periodic datasets, demonstrating that it outperforms both short-range equivariant and long-range invariant MLIPs in energy and force predictions. Our approach enables more accurate and efficient simulations of systems with long-range interactions and charge heterogeneity, expanding the applicability of MLIPs in computational materials science.
Boosting Resolution Generalization of Diffusion Transformers with Randomized Positional Encodings
Authors: Cong Liu, Liang Hou, Mingwu Zheng, Xin Tao, Pengfei Wan, Di Zhang, Kun Gai
Date: 2025-03-24 00:00:00
Tasks: Data Augmentation, Image Cropping, Image Generation, Position

Resolution generalization in image generation tasks enables the production of higher-resolution images with lower training resolution overhead. However, a significant challenge in resolution generalization, particularly in the widely used Diffusion Transformers, lies in the mismatch between the positional encodings encountered during testing and those used during training. While existing methods have employed techniques such as interpolation, extrapolation, or their combinations, none have fully resolved this issue. In this paper, we propose a novel two-dimensional randomized positional encodings (RPE-2D) framework that focuses on learning positional order of image patches instead of the specific distances between them, enabling seamless high- and low-resolution image generation without requiring high- and low-resolution image training. Specifically, RPE-2D independently selects positions over a broader range along both the horizontal and vertical axes, ensuring that all position encodings are trained during the inference phase, thus improving resolution generalization. Additionally, we propose a random data augmentation technique to enhance the modeling of position order. To address the issue of image cropping caused by the augmentation, we introduce corresponding micro-conditioning to enable the model to perceive the specific cropping patterns. On the ImageNet dataset, our proposed RPE-2D achieves state-of-the-art resolution generalization performance, outperforming existing competitive methods when trained at a resolution of $256 \times 256$ and inferred at $384 \times 384$ and $512 \times 512$, as well as when scaling from $512 \times 512$ to $768 \times 768$ and $1024 \times 1024$. And it also exhibits outstanding capabilities in low-resolution image generation, multi-stage training acceleration and multi-resolution inheritance.
GRiNS: A Python Library for Simulating Gene Regulatory Network Dynamics
Authors: Pradyumna Harlapur, Harshavardhan B V, Mohit Kumar Jolly
Date: 2025-03-24 00:00:00
Tasks: GPU

The emergent dynamics of complex gene regulatory networks govern various cellular processes. However, understanding these dynamics is challenging due to the difficulty of parameterizing the computational models for these networks, especially as the network size increases. Here, we introduce a simulation library, Gene Regulatory Interaction Network Simulator (GRiNS), to address these challenges. GRiNS integrates popular parameter-agnostic simulation frameworks, RACIPE and Boolean Ising formalism, into a single Python library capable of leveraging GPU acceleration for efficient and scalable simulations. GRiNS extends the ordinary differential equations (ODE) based RACIPE framework with a more modular design, allowing users to choose parameters, initial conditions, and time-series outputs for greater customisability and accuracy in simulations. For large networks, where ODE-based simulation formalisms do not scale well, GRiNS implements Boolean Ising formalism, providing a simplified, coarse-grained alternative, significantly reducing the computational cost while capturing key dynamical behaviours of large regulatory networks. The documentation and installation instructions for GRiNS can be found at https://moltenecdysone09.github.io/GRiNS/.
Personalized Continual EEG Decoding: Retaining and Transferring Knowledge
Authors: Dan Li, Hye-Bin Shin, Kang Yin, Seong-Whan Lee
Date: 2024-11-04 00:00:00
Tasks: Brain Computer Interface, Domain Adaptation, EEG, Eeg Decoding, Electroencephalogram (EEG), Incremental Learning, Transfer Learning

The significant inter-subject variability in electroen-cephalogram (EEG) signals often results in substantial changes to neural network weights as data distributions shift. This variability frequently causes catastrophic forgetting in continual EEG decoding tasks, where previously acquired knowledge is overwritten as new subjects are introduced. While retraining the entire dataset for each new subject can mitigate forgetting, this approach imposes significant computational costs, rendering it impractical for real-world applications. Therefore, an ideal brain-computer interface (BCI) model should incrementally learn new information without requiring complete retraining, thereby reducing computational overhead. Existing EEG decoding meth-ods typically rely on large, centralized source-domain datasets for pre-training to improve model generalization. However, in practical scenarios, data availability is often constrained by privacy concerns. Furthermore, these methods are susceptible to catastrophic forgetting in continual EEG decoding tasks, significantly limiting their utility in long-term learning scenarios. To address these issues, we propose the Personalized Continual EEG Decoding (PCED) framework for continual EEG decoding. The framework uses Euclidean Alignment for fast domain adap-tation, reducing inter-subject variability. To retain knowledge and prevent forgetting, it includes an exemplar replay mechanism that preserves key information from past tasks. A reservoir sampling-based memory management strategy optimizes exemplar storage to handle memory constraints in long-term learning. Experiments on the OpenBMI dataset with 54 subjects show that PCED balances knowledge retention and classification performance, providing an efficient solution for real-world BCI applications.
I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders
Authors: Andrey Galichin, Alexey Dontsov, Polina Druzhinina, Anton Razzhigaev, Oleg Y. Rogov, Elena Tutubalina, Ivan Oseledets
Date: 2025-03-24 00:00:00
Tasks: All

Large Language Models (LLMs) have achieved remarkable success in natural language processing. Recent advances have led to the developing of a new class of reasoning LLMs; for example, open-source DeepSeek-R1 has achieved state-of-the-art performance by integrating deep thinking and complex reasoning. Despite these impressive capabilities, the internal reasoning mechanisms of such models remain unexplored. In this work, we employ Sparse Autoencoders (SAEs), a method to learn a sparse decomposition of latent representations of a neural network into interpretable features, to identify features that drive reasoning in the DeepSeek-R1 series of models. First, we propose an approach to extract candidate ''reasoning features'' from SAE representations. We validate these features through empirical analysis and interpretability methods, demonstrating their direct correlation with the model's reasoning abilities. Crucially, we demonstrate that steering these features systematically enhances reasoning performance, offering the first mechanistic account of reasoning in LLMs. Code available at https://github.com/AIRI-Institute/SAE-Reasoning
CoMP: Continual Multimodal Pre-training for Vision Foundation Models
Authors: Yitong Chen, Lingchen Meng, Wujian Peng, Zuxuan Wu, Yu-Gang Jiang
Date: 2025-03-24 00:00:00
Tasks: cross-modal alignment

Pre-trained Vision Foundation Models (VFMs) provide strong visual representations for a wide range of applications. In this paper, we continually pre-train prevailing VFMs in a multimodal manner such that they can effortlessly process visual inputs of varying sizes and produce visual representations that are more aligned with language representations, regardless of their original pre-training process. To this end, we introduce CoMP, a carefully designed multimodal pre-training pipeline. CoMP uses a Continual Rotary Position Embedding to accommodate visual inputs with different resolutions, and an Alignment Loss between visual and textual features for better cross-modal alignment. After continual pre-training, leading VFMs like DINOv2, SigLIP and AIMv2 achieve remarkable improvements not only in multimodal understanding tasks but also in generic classification and segmentation tasks. Remarkably, CoMP-AIMv2 achieves scores of 64.9 on ChartQA with a 0.5B LLM, while maintaining an 87.3% accuracy on ImageNet-1K and a 51.8 mIoU on ADE20K under frozen chunk evaluation.
Equivariant Image Modeling
Authors: Ruixiao Dong, Mengde Xu, Zigang Geng, Li Li, Han Hu, Shuyang Gu
Date: 2025-03-24 00:00:00
Tasks: Image Generation, Zero-shot Generalization

Current generative models, such as autoregressive and diffusion approaches, decompose high-dimensional data distribution learning into a series of simpler subtasks. However, inherent conflicts arise during the joint optimization of these subtasks, and existing solutions fail to resolve such conflicts without sacrificing efficiency or scalability. We propose a novel equivariant image modeling framework that inherently aligns optimization targets across subtasks by leveraging the translation invariance of natural visual signals. Our method introduces (1) column-wise tokenization which enhances translational symmetry along the horizontal axis, and (2) windowed causal attention which enforces consistent contextual relationships across positions. Evaluated on class-conditioned ImageNet generation at 256x256 resolution, our approach achieves performance comparable to state-of-the-art AR models while using fewer computational resources. Systematic analysis demonstrates that enhanced equivariance reduces inter-task conflicts, significantly improving zero-shot generalization and enabling ultra-long image synthesis. This work establishes the first framework for task-aligned decomposition in generative modeling, offering insights into efficient parameter sharing and conflict-free optimization. The code and models are publicly available at https://github.com/drx-code/EquivariantModeling.
mmTracking: Trajectory Tracking for Uplink mmWave Devices with Multi-Path Doppler Difference of Arrival
Authors: Cheng Lin, Chao Yu, Xiaowei Xu, Rui Wang
Date: 2025-03-21 00:00:00

This paper presents a method, namely mmTracking, for device trajectory tracking in a millimeter wave (mmWave) communication system. In mmTracking, the base station (BS) relies on one line-of-sight (LoS) path and at least two non-line-of-sight (NLoS) paths, which are reflected off two walls respectively, of the uplink channel to track the location of a mobile device versus time. There are at least three radio frequency (RF) chains at the BS. Analog phased array with narrow and adjustable receive beam is connected to each RF chain to capture one signal path, where the angle of arrival (AoA) can be roughly estimated. Due to the carrier frequency offset between the transmitter and the BS, the Doppler frequency of each path could hardly be estimated accurately. Instead, the differences of Doppler frequencies of the three paths can be estimated with much better accuracy. Therefore, a trajectory tracking method based on the Doppler difference and AoA estimations is proposed in mmTracking. Experimental results in a typical indoor environment demonstrate that the average error of transmitter localization and trajectory tracking is less than 20 cm.
ProtoGS: Efficient and High-Quality Rendering with 3D Gaussian Prototypes
Authors: Zhengqing Gao, Dongting Hu, Jia-Wang Bian, Huan Fu, Yan Li, Tongliang Liu, Mingming Gong, Kun Zhang
Date: 2025-03-21 00:00:00
Tasks: 3DGS, Novel View Synthesis

3D Gaussian Splatting (3DGS) has made significant strides in novel view synthesis but is limited by the substantial number of Gaussian primitives required, posing challenges for deployment on lightweight devices. Recent methods address this issue by compressing the storage size of densified Gaussians, yet fail to preserve rendering quality and efficiency. To overcome these limitations, we propose ProtoGS to learn Gaussian prototypes to represent Gaussian primitives, significantly reducing the total Gaussian amount without sacrificing visual quality. Our method directly uses Gaussian prototypes to enable efficient rendering and leverage the resulting reconstruction loss to guide prototype learning. To further optimize memory efficiency during training, we incorporate structure-from-motion (SfM) points as anchor points to group Gaussian primitives. Gaussian prototypes are derived within each group by clustering of K-means, and both the anchor points and the prototypes are optimized jointly. Our experiments on real-world and synthetic datasets prove that we outperform existing methods, achieving a substantial reduction in the number of Gaussians, and enabling high rendering speed while maintaining or even enhancing rendering fidelity.
LookCloser: Frequency-aware Radiance Field for Tiny-Detail Scene
Authors: XiaoYu Zhang, Weihong Pan, Chong Bao, Xiyu Zhang, Xiaojun Xiang, Hanqing Jiang, Hujun Bao
Date: 2025-03-24 00:00:00
Proceeding: CVPR 2025 1
Tasks: NeRF

Humans perceive and comprehend their surroundings through information spanning multiple frequencies. In immersive scenes, people naturally scan their environment to grasp its overall structure while examining fine details of objects that capture their attention. However, current NeRF frameworks primarily focus on modeling either high-frequency local views or the broad structure of scenes with low-frequency information, which is limited to balancing both. We introduce FA-NeRF, a novel frequency-aware framework for view synthesis that simultaneously captures the overall scene structure and high-definition details within a single NeRF model. To achieve this, we propose a 3D frequency quantification method that analyzes the scene's frequency distribution, enabling frequency-aware rendering. Our framework incorporates a frequency grid for fast convergence and querying, a frequency-aware feature re-weighting strategy to balance features across different frequency contents. Extensive experiments show that our method significantly outperforms existing approaches in modeling entire scenes while preserving fine details.