pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory Queue
Authors: Xiaoyu Sun, Yang Yang, Xunde Dong
Date: 2025-05-26 00:00:00
Tasks: Contrastive Learning, Data Augmentation

In the field of automatic Electrocardiogram (ECG) diagnosis, due to the relatively limited amount of labeled data, how to build a robust ECG pretrained model based on unlabeled data is a key area of focus for researchers. Recent advancements in contrastive learning-based ECG pretrained models highlight the potential of exploiting the additional patient-level self-supervisory signals inherent in ECG. They are referred to as patient contrastive learning. Its rationale is that multiple physical recordings from the same patient may share commonalities, termed patient consistency, so redefining positive and negative pairs in contrastive learning as intrapatient and inter-patient samples provides more shared context to learn an effective representation. However, these methods still fail to efficiently exploit patient consistency due to the insufficient amount of intra-inter patient samples existing in a batch. Hence, we propose a contrastive learning-based ECG pretrained model enhanced by the Patient Memory Queue (PMQ), which incorporates a large patient memory queue to mitigate model degeneration that can arise from insufficient intra-inter patient samples. In order to further enhance the performance of the pretrained model, we introduce two extra data augmentation methods to provide more perspectives of positive and negative pairs for pretraining. Extensive experiments were conducted on three public datasets with three different data ratios. The experimental results show that the comprehensive performance of our method outperforms previous contrastive learning methods and exhibits greater robustness in scenarios with limited labeled data. The code is available at https://github.com/3hiuwoo/PMQ.
The power spectrum of SUSY-CDM on sub-galactic scales
Authors: Anne M. Green, Stefan Hofmann, Dominik J. Schwarz
Date: 2003-09-23 00:00:00

The formation of large scale structure is independent of the nature of the cold dark matter (CDM), however the fate of very small scale inhomogeneities depends on the micro-physics of the CDM particles. We investigate the matter power spectrum for scales that enter the Hubble radius well before matter-radiation equality, and follow its evolution until the time when the first inhomogeneities become non-linear. Our focus lies on weakly interacting massive particles (WIMPs), and as a concrete example we analyze the case when the lightest supersymmetric particle is a bino. We show that collisional damping and free-streaming of WIMPs lead to a matter power spectrum with a sharp cut-off at about 10^(-6) M_odot and a maximum close to that cut-off. We also calculate the transfer function for the growth of the inhomogeneities in the linear regime. These three effects (collisional damping, free-streaming and gravitational growth) are combined to provide a WMAP normalized primordial CDM power spectrum, which could serve as an input for high resolution CDM simulations. The smallest inhomogeneities typically enter the non-linear regime at a redshift of about 60.
Constraining Warm Dark Matter candidates including sterile neutrinos and light gravitinos with WMAP and the Lyman-alpha forest
Authors: M. Viel, J. Lesgourgues, M. G. Haehnelt, S. Matarrese, A. Riotto
Date: 2005-01-26 00:00:00

The matter power spectrum at comoving scales of (1-40) h^{-1} Mpc is very sensitive to the presence of Warm Dark Matter (WDM) particles with large free streaming lengths. We present constraints on the mass of WDM particles from a combined analysis of the matter power spectrum inferred from the large samples of high resolution high signal-to-noise Lyman-alpha forest data of Kim et al. (2004) and Croft et al. (2002) and the cosmic microwave background data of WMAP. We obtain a lower limit of m_wdm > 550 eV (2 sigma for early decoupled thermal relics and m_wdm > 2.0 keV (2 sigma) for sterile neutrinos. We also investigate the case where in addition to cold dark matter a light thermal gravitino with fixed effective temperature contributes significantly to the matter density. In that case the gravitino density is proportional to its mass, and we find an upper limit m_{3/2} < 16 eV (2 sigma). This translates into a bound on the scale of supersymmetry breaking, Lambda_{susy} < 260 TeV, for models of supersymmetric gauge mediation in which the gravitino is the lightest supersymmetric particle.
O-TPT: Orthogonality Constraints for Calibrating Test-time Prompt Tuning in Vision-Language Models
Authors: Ashshak Sharifdeen, Muhammad Akhtar Munir, Sanoojan Baliah, Salman Khan, Muhammad Haris Khan
Date: 2025-03-15 00:00:00
Proceeding: CVPR 2025 1

Test-time prompt tuning for vision-language models (VLMs) is getting attention because of their ability to learn with unlabeled data without fine-tuning. Although test-time prompt tuning methods for VLMs can boost accuracy, the resulting models tend to demonstrate poor calibration, which casts doubts on the reliability and trustworthiness of these models. Notably, more attention needs to be devoted to calibrating the test-time prompt tuning in vision-language models. To this end, we propose a new approach, called O-TPT that introduces orthogonality constraints on the textual features corresponding to the learnable prompts for calibrating test-time prompt tuning in VLMs. Towards introducing orthogonality constraints, we make the following contributions. First, we uncover new insights behind the suboptimal calibration performance of existing methods relying on textual feature dispersion. Second, we show that imposing a simple orthogonalization of textual features is a more effective approach towards obtaining textual dispersion. We conduct extensive experiments on various datasets with different backbones and baselines. The results indicate that our method consistently outperforms the prior state of the art in significantly reducing the overall average calibration error. Also, our method surpasses the zero-shot calibration performance on fine-grained classification tasks.
RGBAvatar: Reduced Gaussian Blendshapes for Online Modeling of Head Avatars
Authors: Linzhou Li, Yumeng Li, Yanlin Weng, Youyi Zheng, Kun Zhou
Date: 2025-03-17 00:00:00
Proceeding: CVPR 2025 1

We present Reduced Gaussian Blendshapes Avatar (RGBAvatar), a method for reconstructing photorealistic, animatable head avatars at speeds sufficient for on-the-fly reconstruction. Unlike prior approaches that utilize linear bases from 3D morphable models (3DMM) to model Gaussian blendshapes, our method maps tracked 3DMM parameters into reduced blendshape weights with an MLP, leading to a compact set of blendshape bases. The learned compact base composition effectively captures essential facial details for specific individuals, and does not rely on the fixed base composition weights of 3DMM, leading to enhanced reconstruction quality and higher efficiency. To further expedite the reconstruction process, we develop a novel color initialization estimation method and a batch-parallel Gaussian rasterization process, achieving state-of-the-art quality with training throughput of about 630 images per second. Moreover, we propose a local-global sampling strategy that enables direct on-the-fly reconstruction, immediately reconstructing the model as video streams in real time while achieving quality comparable to offline settings. Our source code is available at https://github.com/gapszju/RGBAvatar.
Learning to Normalize on the SPD Manifold under Bures-Wasserstein Geometry
Authors: Rui Wang, Shaocheng Jin, Ziheng Chen, Xiaoqing Luo, Xiao-Jun Wu
Date: 2025-04-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Representation Learning

Covariance matrices have proven highly effective across many scientific fields. Since these matrices lie within the Symmetric Positive Definite (SPD) manifold - a Riemannian space with intrinsic non-Euclidean geometry, the primary challenge in representation learning is to respect this underlying geometric structure. Drawing inspiration from the success of Euclidean deep learning, researchers have developed neural networks on the SPD manifolds for more faithful covariance embedding learning. A notable advancement in this area is the implementation of Riemannian batch normalization (RBN), which has been shown to improve the performance of SPD network models. Nonetheless, the Riemannian metric beneath the existing RBN might fail to effectively deal with the ill-conditioned SPD matrices (ICSM), undermining the effectiveness of RBN. In contrast, the Bures-Wasserstein metric (BWM) demonstrates superior performance for ill-conditioning. In addition, the recently introduced Generalized BWM (GBWM) parameterizes the vanilla BWM via an SPD matrix, allowing for a more nuanced representation of vibrant geometries of the SPD manifold. Therefore, we propose a novel RBN algorithm based on the GBW geometry, incorporating a learnable metric parameter. Moreover, the deformation of GBWM by matrix power is also introduced to further enhance the representational capacity of GBWM-based RBN. Experimental results on different datasets validate the effectiveness of our proposed method.
MESC-3D:Mining Effective Semantic Cues for 3D Reconstruction from a Single Image
Authors: Shaoming Li, Qing Cai, Songqi Kong, Runqing Tan, Heng Tong, Shiji Qiu, Yongguo Jiang, Zhi Liu
Date: 2025-02-28 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Reconstruction

Reconstructing 3D shapes from a single image plays an important role in computer vision. Many methods have been proposed and achieve impressive performance. However, existing methods mainly focus on extracting semantic information from images and then simply concatenating it with 3D point clouds without further exploring the concatenated semantics. As a result, these entangled semantic features significantly hinder the reconstruction performance. In this paper, we propose a novel single-image 3D reconstruction method called Mining Effective Semantic Cues for 3D Reconstruction from a Single Image (MESC-3D), which can actively mine effective semantic cues from entangled features. Specifically, we design an Effective Semantic Mining Module to establish connections between point clouds and image semantic attributes, enabling the point clouds to autonomously select the necessary information. Furthermore, to address the potential insufficiencies in semantic information from a single image, such as occlusions, inspired by the human ability to represent 3D objects using prior knowledge drawn from daily experiences, we introduce a 3D Semantic Prior Learning Module. This module incorporates semantic understanding of spatial structures, enabling the model to interpret and reconstruct 3D objects with greater accuracy and realism, closely mirroring human perception of complex 3D environments. Extensive evaluations show that our method achieves significant improvements in reconstruction quality and robustness compared to prior works. Additionally, further experiments validate the strong generalization capabilities and excels in zero-shot preformance on unseen classes. Code is available at https://github.com/QINGQINGLE/MESC-3D.
Temporal Alignment-Free Video Matching for Few-shot Action Recognition
Authors: SuBeen Lee, WonJun Moon, Hyun Seok Seong, Jae-Pil Heo
Date: 2025-04-08 00:00:00
Proceeding: CVPR 2025 1
Tasks: Action Recognition, Few-Shot action recognition, Few Shot Action Recognition

Few-Shot Action Recognition (FSAR) aims to train a model with only a few labeled video instances. A key challenge in FSAR is handling divergent narrative trajectories for precise video matching. While the frame- and tuple-level alignment approaches have been promising, their methods heavily rely on pre-defined and length-dependent alignment units (e.g., frames or tuples), which limits flexibility for actions of varying lengths and speeds. In this work, we introduce a novel TEmporal Alignment-free Matching (TEAM) approach, which eliminates the need for temporal units in action representation and brute-force alignment during matching. Specifically, TEAM represents each video with a fixed set of pattern tokens that capture globally discriminative clues within the video instance regardless of action length or speed, ensuring its flexibility. Furthermore, TEAM is inherently efficient, using token-wise comparisons to measure similarity between videos, unlike existing methods that rely on pairwise comparisons for temporal alignment. Additionally, we propose an adaptation process that identifies and removes common information across classes, establishing clear boundaries even between novel categories. Extensive experiments demonstrate the effectiveness of TEAM. Codes are available at github.com/leesb7426/TEAM.
Crab: A Unified Audio-Visual Scene Understanding Model with Explicit Cooperation
Authors: Henghui Du, Guangyao Li, Chang Zhou, Chunjie Zhang, Alan Zhao, Di Hu
Date: 2025-03-17 00:00:00
Proceeding: CVPR 2025 1
Tasks: Data Interaction, Scene Understanding, Temporal Localization, UIE

In recent years, numerous tasks have been proposed to encourage model to develop specified capability in understanding audio-visual scene, primarily categorized into temporal localization, spatial localization, spatio-temporal reasoning, and pixel-level understanding. Instead, human possesses a unified understanding ability for diversified tasks. Therefore, designing an audio-visual model with general capability to unify these tasks is of great value. However, simply joint training for all tasks can lead to interference due to the heterogeneity of audiovisual data and complex relationship among tasks. We argue that this problem can be solved through explicit cooperation among tasks. To achieve this goal, we propose a unified learning method which achieves explicit inter-task cooperation from both the perspectives of data and model thoroughly. Specifically, considering the labels of existing datasets are simple words, we carefully refine these datasets and construct an Audio-Visual Unified Instruction-tuning dataset with Explicit reasoning process (AV-UIE), which clarifies the cooperative relationship among tasks. Subsequently, to facilitate concrete cooperation in learning stage, an interaction-aware LoRA structure with multiple LoRA heads is designed to learn different aspects of audiovisual data interaction. By unifying the explicit cooperation across the data and model aspect, our method not only surpasses existing unified audio-visual model on multiple tasks, but also outperforms most specialized models for certain tasks. Furthermore, we also visualize the process of explicit cooperation and surprisingly find that each LoRA head has certain audio-visual understanding ability. Code and dataset: https://github.com/GeWu-Lab/Crab
SCAP: Transductive Test-Time Adaptation via Supportive Clique-based Attribute Prompting
Authors: Chenyu Zhang, Kunlun Xu, Zichen Liu, Yuxin Peng, Jiahuan Zhou
Date: 2025-03-17 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute, Test-time Adaptation

Vision-language models (VLMs) encounter considerable challenges when adapting to domain shifts stemming from changes in data distribution. Test-time adaptation (TTA) has emerged as a promising approach to enhance VLM performance under such conditions. In practice, test data often arrives in batches, leading to increasing interest in the transductive TTA setting. However, existing TTA methods primarily focus on individual test samples, overlooking crucial cross-sample correlations within a batch. While recent ViT-based TTA methods have introduced batch-level adaptation, they remain suboptimal for VLMs due to inadequate integration of the text modality. To address these limitations, we propose a novel transductive TTA framework, Supportive Clique-based Attribute Prompting (SCAP), which effectively combines visual and textual information to enhance adaptation by generating fine-grained attribute prompts across test batches. SCAP first forms supportive cliques of test samples in an unsupervised manner based on visual similarity and learns an attribute prompt for each clique, capturing shared attributes critical for adaptation. For each test sample, SCAP aggregates attribute prompts from its associated cliques, providing enriched contextual information. To ensure adaptability over time, we incorporate a retention module that dynamically updates attribute prompts and their associated attributes as new data arrives. Comprehensive experiments across multiple benchmarks demonstrate that SCAP outperforms existing state-of-the-art methods, significantly advancing VLM generalization under domain shifts. Our code is available at https://github.com/zhoujiahuan1991/CVPR2025-SCAP.
Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Views
Authors: Jiang Wu, Rui Li, Yu Zhu, Rong Guo, Jinqiu Sun, Yanning Zhang
Date: 2025-04-29 00:00:00
Proceeding: CVPR 2025 1
Tasks: NeRF, Surface Reconstruction

We present a Gaussian Splatting method for surface reconstruction using sparse input views. Previous methods relying on dense views struggle with extremely sparse Structure-from-Motion points for initialization. While learning-based Multi-view Stereo (MVS) provides dense 3D points, directly combining it with Gaussian Splatting leads to suboptimal results due to the ill-posed nature of sparse-view geometric optimization. We propose Sparse2DGS, an MVS-initialized Gaussian Splatting pipeline for complete and accurate reconstruction. Our key insight is to incorporate the geometric-prioritized enhancement schemes, allowing for direct and robust geometric learning under ill-posed conditions. Sparse2DGS outperforms existing methods by notable margins while being ${2}\times$ faster than the NeRF-based fine-tuning approach.
Cross-modal Causal Relation Alignment for Video Question Grounding
Authors: Weixing Chen, Yang Liu, Binglin Chen, Jiandong Su, Yongsen Zheng, Liang Lin
Date: 2025-03-05 00:00:00
Proceeding: CVPR 2025 1
Tasks: Contrastive Learning, cross-modal alignment, Question Answering, Relation

Video question grounding (VideoQG) requires models to answer the questions and simultaneously infer the relevant video segments to support the answers. However, existing VideoQG methods usually suffer from spurious cross-modal correlations, leading to a failure to identify the dominant visual scenes that align with the intended question. Moreover, vision-language models exhibit unfaithful generalization performance and lack robustness on challenging downstream tasks such as VideoQG. In this work, we propose a novel VideoQG framework named Cross-modal Causal Relation Alignment (CRA), to eliminate spurious correlations and improve the causal consistency between question-answering and video temporal grounding. Our CRA involves three essential components: i) Gaussian Smoothing Grounding (GSG) module for estimating the time interval via cross-modal attention, which is de-noised by an adaptive Gaussian filter, ii) Cross-Modal Alignment (CMA) enhances the performance of weakly supervised VideoQG by leveraging bidirectional contrastive learning between estimated video segments and QA features, iii) Explicit Causal Intervention (ECI) module for multimodal deconfounding, which involves front-door intervention for vision and back-door intervention for language. Extensive experiments on two VideoQG datasets demonstrate the superiority of our CRA in discovering visually grounded content and achieving robust question reasoning. Codes are available at https://github.com/WissingChen/CRA-GQA.
Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazing
Authors: Ruiyi Wang, Yushuo Zheng, ZiCheng Zhang, Chunyi Li, Shuaicheng Liu, Guangtao Zhai, Xiaohong Liu
Date: 2025-03-25 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Dehazing, Image Generation

Existing real-world image dehazing methods primarily attempt to fine-tune pre-trained models or adapt their inference procedures, thus heavily relying on the pre-trained models and associated training data. Moreover, restoring heavily distorted information under dense haze requires generative diffusion models, whose potential in dehazing remains underutilized partly due to their lengthy sampling processes. To address these limitations, we introduce a novel hazing-dehazing pipeline consisting of a Realistic Hazy Image Generation framework (HazeGen) and a Diffusion-based Dehazing framework (DiffDehaze). Specifically, HazeGen harnesses robust generative diffusion priors of real-world hazy images embedded in a pre-trained text-to-image diffusion model. By employing specialized hybrid training and blended sampling strategies, HazeGen produces realistic and diverse hazy images as high-quality training data for DiffDehaze. To alleviate the inefficiency and fidelity concerns associated with diffusion-based methods, DiffDehaze adopts an Accelerated Fidelity-Preserving Sampling process (AccSamp). The core of AccSamp is the Tiled Statistical Alignment Operation (AlignOp), which can provide a clean and faithful dehazing estimate within a small fraction of sampling steps to reduce complexity and enable effective fidelity guidance. Extensive experiments demonstrate the superior dehazing performance and visual quality of our approach over existing methods. The code is available at https://github.com/ruiyi-w/Learning-Hazing-to-Dehazing.
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation
Authors: Shahad Albastaki, Anabia Sohail, Iyyakutti Iyappan Ganapathi, Basit Alawode, Asim Khan, Sajid Javed, Naoufel Werghi, Mohammed Bennamoun, Arif Mahmood
Date: 2025-04-26 00:00:00
Proceeding: CVPR 2025 1
Tasks: Survival Analysis, whole slide images

In Computational Pathology (CPath), the introduction of Vision-Language Models (VLMs) has opened new avenues for research, focusing primarily on aligning image-text pairs at a single magnification level. However, this approach might not be sufficient for tasks like cancer subtype classification, tissue phenotyping, and survival analysis due to the limited level of detail that a single-resolution image can provide. Addressing this, we propose a novel multi-resolution paradigm leveraging Whole Slide Images (WSIs) to extract histology patches at multiple resolutions and generate corresponding textual descriptions through advanced CPath VLM. We introduce visual-textual alignment at multiple resolutions as well as cross-resolution alignment to establish more effective text-guided visual representations. Cross-resolution alignment using a multimodal encoder enhances the model's ability to capture context from multiple resolutions in histology images. Our model aims to capture a broader range of information, supported by novel loss functions, enriches feature representation, improves discriminative ability, and enhances generalization across different resolutions. Pre-trained on a comprehensive TCGA dataset with 34 million image-language pairs at various resolutions, our fine-tuned model outperforms state-of-the-art (SOTA) counterparts across multiple datasets and tasks, demonstrating its effectiveness in CPath. The code is available on GitHub at: https://github.com/BasitAlawode/MR-PLIP