pwc-archive/papers-with-abstracts huggingface dataset:
A Zero-Shot Learning Approach for Ephemeral Gully Detection from Remote Sensing using Vision Language Models
Authors: Seyed Mohamad Ali Tousi, Ramy Farag, Jacket Demby's, Gbenga Omotara, John A. Lory, G. N. DeSouza
Date: 2025-03-03 00:00:00
Tasks: Transfer Learning, zero-shot-classification, Zero-Shot Learning

Ephemeral gullies are a primary cause of soil erosion and their reliable, accurate, and early detection will facilitate significant improvements in the sustainability of global agricultural systems. In our view, prior research has not successfully addressed automated detection of ephemeral gullies from remotely sensed images, so for the first time, we present and evaluate three successful pipelines for ephemeral gully detection. Our pipelines utilize remotely sensed images, acquired from specific agricultural areas over a period of time. The pipelines were tested with various choices of Visual Language Models (VLMs), and they classified the images based on the presence of ephemeral gullies with accuracy higher than 70% and a F1-score close to 80% for positive gully detection. Additionally, we developed the first public dataset for ephemeral gully detection, labeled by a team of soil- and plant-science experts. To evaluate the proposed pipelines, we employed a variety of zero-shot classification methods based on State-of-the-Art (SOTA) open-source Vision-Language Models (VLMs). In addition to that, we compare the same pipelines with a transfer learning approach. Extensive experiments were conducted to validate the detection pipelines and to analyze the impact of hyperparameter changes in their performance. The experimental results demonstrate that the proposed zero-shot classification pipelines are highly effective in detecting ephemeral gullies in a scenario where classification datasets are scarce.
HOP: Heterogeneous Topology-based Multimodal Entanglement for Co-Speech Gesture Generation
Authors: Hongye Cheng, Tianyu Wang, Guangsi Shi, Zexing Zhao, Yanwei Fu
Date: 2025-03-03 00:00:00
Proceeding: CVPR 2025 1
Tasks: Gesture Generation, Rhythm

Co-speech gestures are crucial non-verbal cues that enhance speech clarity and expressiveness in human communication, which have attracted increasing attention in multimodal research. While the existing methods have made strides in gesture accuracy, challenges remain in generating diverse and coherent gestures, as most approaches assume independence among multimodal inputs and lack explicit modeling of their interactions. In this work, we propose a novel multimodal learning method named HOP for co-speech gesture generation that captures the heterogeneous entanglement between gesture motion, audio rhythm, and text semantics, enabling the generation of coordinated gestures. By leveraging spatiotemporal graph modeling, we achieve the alignment of audio and action. Moreover, to enhance modality coherence, we build the audio-text semantic representation based on a reprogramming module, which is beneficial for cross-modality adaptation. Our approach enables the trimodal system to learn each other's features and represent them in the form of topological entanglement. Extensive experiments demonstrate that HOP achieves state-of-the-art performance, offering more natural and expressive co-speech gesture generation. More information, codes, and demos are available here: https://star-uu-wang.github.io/HOP/
SAR-W-MixMAE: SAR Foundation Model Training Using Backscatter Power Weighting
Authors: Ali Caglayan, Nevrez Imamoglu, Toru Kouyama
Date: 2025-03-03 00:00:00
Tasks: image-classification, Image Classification

Foundation model approaches such as masked auto-encoders (MAE) or its variations are now being successfully applied to satellite imagery. Most of the ongoing technical validation of foundation models have been applied to optical images like RGB or multi-spectral images. Due to difficulty in semantic labeling to create datasets and higher noise content with respect to optical images, Synthetic Aperture Radar (SAR) data has not been explored a lot in the field for foundation models. Therefore, in this work as a pre-training approach, we explored masked auto-encoder, specifically MixMAE on Sentinel-1 SAR images and its impact on SAR image classification tasks. Moreover, we proposed to use the physical characteristic of SAR data for applying weighting parameter on the auto-encoder training loss (MSE) to reduce the effect of speckle noise and very high values on the SAR images. Proposed SAR intensity-based weighting of the reconstruction loss demonstrates promising results both on SAR pre-training and downstream tasks specifically on flood detection compared with the baseline model.
DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-Resolution
Authors: Xingyuan Li, ZiRui Wang, Yang Zou, Zhixin Chen, Jun Ma, Zhiying Jiang, Long Ma, JinYuan Liu
Date: 2025-03-03 00:00:00
Proceeding: CVPR 2025 1
Tasks: Autonomous Driving, Image Super-Resolution, Infrared image super-resolution, Super-Resolution

Infrared imaging is essential for autonomous driving and robotic operations as a supportive modality due to its reliable performance in challenging environments. Despite its popularity, the limitations of infrared cameras, such as low spatial resolution and complex degradations, consistently challenge imaging quality and subsequent visual tasks. Hence, infrared image super-resolution (IISR) has been developed to address this challenge. While recent developments in diffusion models have greatly advanced this field, current methods to solve it either ignore the unique modal characteristics of infrared imaging or overlook the machine perception requirements. To bridge these gaps, we propose DifIISR, an infrared image super-resolution diffusion model optimized for visual quality and perceptual performance. Our approach achieves task-based guidance for diffusion by injecting gradients derived from visual and perceptual priors into the noise during the reverse process. Specifically, we introduce an infrared thermal spectrum distribution regulation to preserve visual fidelity, ensuring that the reconstructed infrared images closely align with high-resolution images by matching their frequency components. Subsequently, we incorporate various visual foundational models as the perceptual guidance for downstream visual tasks, infusing generalizable perceptual features beneficial for detection and segmentation. As a result, our approach gains superior visual results while attaining State-Of-The-Art downstream task performance. Code is available at https://github.com/zirui0625/DifIISR
Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling
Authors: Jonathan Fhima, Jan Van Eijgen, Lennert Beeckmans, Thomas Jacobs, Moti Freiman, Luis Filipe Nakayama, Ingeborg Stalmans, Chaim Baskin, Joachim A. Behar
Date: 2025-03-03 00:00:00
Tasks: Diversity, Image Generation, Retinal Vessel Segmentation

Generalization in medical segmentation models is challenging due to limited annotated datasets and imaging variability. To address this, we propose Retinal Layout-Aware Diffusion (RLAD), a novel diffusion-based framework for generating controllable layout-aware images. RLAD conditions image generation on multiple key layout components extracted from real images, ensuring high structural fidelity while enabling diversity in other components. Applied to retinal fundus imaging, we augmented the training datasets by synthesizing paired retinal images and vessel segmentations conditioned on extracted blood vessels from real images, while varying other layout components such as lesions and the optic disc. Experiments demonstrated that RLAD-generated data improved generalization in retinal vessel segmentation by up to 8.1%. Furthermore, we present REYIA, a comprehensive dataset comprising 586 manually segmented retinal images. To foster reproducibility and drive innovation, both our code and dataset will be made publicly accessible.
Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging
Authors: Chao Qu, Shuo Zhu, Yuhang Wang, Zongze Wu, Xiaoyu Chen, Edmund Y. Lam, Jing Han
Date: 2025-03-03 00:00:00
Tasks: Deblurring, Denoising, Image Deblurring

The fields of imaging in the nighttime dynamic and other extremely dark conditions have seen impressive and transformative advancements in recent years, partly driven by the rise of novel sensing approaches, e.g., near-infrared (NIR) cameras with high sensitivity and event cameras with minimal blur. However, inappropriate exposure ratios of near-infrared cameras make them susceptible to distortion and blur. Event cameras are also highly sensitive to weak signals at night yet prone to interference, often generating substantial noise and significantly degrading observations and analysis. Herein, we develop a new framework for low-light imaging combined with NIR imaging and event-based techniques, named synergistic neuromorphic imaging, which can jointly achieve NIR image deblurring and event denoising. Harnessing cross-modal features of NIR images and visible events via spectral consistency and higher-order interaction, the NIR images and events are simultaneously fused, enhanced, and bootstrapped. Experiments on real and realistically simulated sequences demonstrate the effectiveness of our method and indicate better accuracy and robustness than other methods in practical scenarios. This study gives impetus to enhance both NIR images and events, which paves the way for high-fidelity low-light imaging and neuromorphic reasoning.
LiteGS: A High-Performance Modular Framework for Gaussian Splatting Training
Authors: Kaimin Liao
Date: 2025-03-03 00:00:00
Tasks: 3DGS, GPU, NeRF

Gaussian splatting has emerged as a powerful technique for reconstruction of 3D scenes in computer graphics and vision. However, conventional implementations often suffer from inefficiencies, limited flexibility, and high computational overhead, which constrain their adaptability to diverse applications. In this paper, we present LiteGS,a high-performance and modular framework that enhances both the efficiency and usability of Gaussian splatting. LiteGS achieves a 3.4x speedup over the original 3DGS implementation while reducing GPU memory usage by approximately 30%. Its modular design decomposes the splatting process into multiple highly optimized operators, and it provides dual API support via a script-based interface and a CUDA-based interface. The script-based interface, in combination with autograd, enables rapid prototyping and straightforward customization of new ideas, while the CUDA-based interface delivers optimal training speeds for performance-critical applications. LiteGS retains the core algorithm of 3DGS, ensuring compatibility. Comprehensive experiments on the Mip-NeRF 360 dataset demonstrate that LiteGS accelerates training without compromising accuracy, making it an ideal solution for both rapid prototyping and production environments.
Parameter-free Video Segmentation for Vision and Language Understanding
Authors: Louis Mahon, Mirella Lapata
Date: 2025-03-03 00:00:00
Tasks: Question Answering, Video Question Answering, Video Segmentation, Video Semantic Segmentation, Video Summarization

The proliferation of creative video content has driven demand for adapting language models to handle video input and enable multimodal understanding. However, end-to-end models struggle to process long videos due to their size and complexity. An effective alternative is to divide them into smaller chunks to be processed separately, and this motivates a method for choosing where the chunk boundaries should be. In this paper, we propose an algorithm for segmenting videos into contiguous chunks, based on the minimum description length principle, coupled with a dynamic programming search. The algorithm is entirely parameter-free, given feature vectors, not requiring a set threshold or the number or size of chunks to be specified. We show empirically that the breakpoints it produces more accurately approximate scene boundaries in long videos, compared with existing methods for scene detection, even when such methods have access to the true number of scenes. We then showcase this algorithm in two tasks: long video summarization, and retrieval-augmented video question answering. In both cases, scene breaks produced by our algorithm lead to better downstream performance than existing methods for video segmentation.
A Multi-Sensor Fusion Approach for Rapid Orthoimage Generation in Large-Scale UAV Mapping
Authors: Jialei He, Zhihao Zhan, Zhituo Tu, Xiang Zhu, Jie Yuan
Date: 2025-03-03 00:00:00
Tasks: Management, Sensor Fusion

Rapid generation of large-scale orthoimages from Unmanned Aerial Vehicles (UAVs) has been a long-standing focus of research in the field of aerial mapping. A multi-sensor UAV system, integrating the Global Positioning System (GPS), Inertial Measurement Unit (IMU), 4D millimeter-wave radar and camera, can provide an effective solution to this problem. In this paper, we utilize multi-sensor data to overcome the limitations of conventional orthoimage generation methods in terms of temporal performance, system robustness, and geographic reference accuracy. A prior-pose-optimized feature matching method is introduced to enhance matching speed and accuracy, reducing the number of required features and providing precise references for the Structure from Motion (SfM) process. The proposed method exhibits robustness in low-texture scenes like farmlands, where feature matching is difficult. Experiments show that our approach achieves accurate feature matching orthoimage generation in a short time. The proposed drone system effectively aids in farmland detection and management.
Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond
Authors: Guanyao Wu, Haoyu Liu, Hongming Fu, Yichuan Peng, JinYuan Liu, Xin Fan, Risheng Liu
Date: 2025-03-03 00:00:00
Proceeding: CVPR 2025 1
Tasks: Infrared And Visible Image Fusion, Scene Understanding, Triplet

Multi-modality image fusion, particularly infrared and visible image fusion, plays a crucial role in integrating diverse modalities to enhance scene understanding. Early research primarily focused on visual quality, yet challenges remain in preserving fine details, making it difficult to adapt to subsequent tasks. Recent approaches have shifted towards task-specific design, but struggle to achieve the ``The Best of Both Worlds'' due to inconsistent optimization goals. To address these issues, we propose a novel method that leverages the semantic knowledge from the Segment Anything Model (SAM) to Grow the quality of fusion results and Establish downstream task adaptability, namely SAGE. Specifically, we design a Semantic Persistent Attention (SPA) Module that efficiently maintains source information via the persistent repository while extracting high-level semantic priors from SAM. More importantly, to eliminate the impractical dependence on SAM during inference, we introduce a bi-level optimization-driven distillation mechanism with triplet losses, which allow the student network to effectively extract knowledge at the feature, pixel, and contrastive semantic levels, thereby removing reliance on the cumbersome SAM model. Extensive experiments show that our method achieves a balance between high-quality visual results and downstream task adaptability while maintaining practical deployment efficiency.
Understanding Dataset Distillation via Spectral Filtering
Authors: Deyu Bo, Songhua Liu, Xinchao Wang
Date: 2025-03-03 00:00:00
Tasks: Dataset Distillation, Feature Correlation

Dataset distillation (DD) has emerged as a promising approach to compress datasets and speed up model training. However, the underlying connections among various DD methods remain largely unexplored. In this paper, we introduce UniDD, a spectral filtering framework that unifies diverse DD objectives. UniDD interprets each DD objective as a specific filter function that affects the eigenvalues of the feature-feature correlation (FFC) matrix and modulates the frequency components of the feature-label correlation (FLC) matrix. In this way, UniDD reveals that the essence of DD fundamentally lies in matching frequency-specific features. Moreover, according to the filter behaviors, we classify existing methods into low-frequency matching and high-frequency matching, encoding global texture and local details, respectively. However, existing methods rely on fixed filter functions throughout distillation, which cannot capture the low- and high-frequency information simultaneously. To address this limitation, we further propose Curriculum Frequency Matching (CFM), which gradually adjusts the filter parameter to cover both low- and high-frequency information of the FFC and FLC matrices. Extensive experiments on small-scale datasets, such as CIFAR-10/100, and large-scale datasets, including ImageNet-1K, demonstrate the superior performance of CFM over existing baselines and validate the practicality of UniDD.
One-Step Event-Driven High-Speed Autofocus
Authors: Yuhan Bao, Shaohua Gao, Wenyong Li, Kaiwei Wang
Date: 2025-03-03 00:00:00
Proceeding: CVPR 2025 1

High-speed autofocus in extreme scenes remains a significant challenge. Traditional methods rely on repeated sampling around the focus position, resulting in ``focus hunting''. Event-driven methods have advanced focusing speed and improved performance in low-light conditions; however, current approaches still require at least one lengthy round of ``focus hunting'', involving the collection of a complete focus stack. We introduce the Event Laplacian Product (ELP) focus detection function, which combines event data with grayscale Laplacian information, redefining focus search as a detection task. This innovation enables the first one-step event-driven autofocus, cutting focusing time by up to two-thirds and reducing focusing error by 24 times on the DAVIS346 dataset and 22 times on the EVK4 dataset. Additionally, we present an autofocus pipeline tailored for event-only cameras, achieving accurate results across a range of challenging motion and lighting conditions. All datasets and code will be made publicly available.
Self-Adaptive Gamma Context-Aware SSM-based Model for Metal Defect Detection
Authors: Sijin Sun, Ming Deng, Xingrui Yu, Xingyu Xi, Liangbin Zhao
Date: 2025-03-03 00:00:00
Tasks: Defect Detection, Management

Metal defect detection is critical in industrial quality assurance, yet existing methods struggle with grayscale variations and complex defect states, limiting its robustness. To address these challenges, this paper proposes a Self-Adaptive Gamma Context-Aware SSM-based model(GCM-DET). This advanced detection framework integrating a Dynamic Gamma Correction (GC) module to enhance grayscale representation and optimize feature extraction for precise defect reconstruction. A State-Space Search Management (SSM) architecture captures robust multi-scale features, effectively handling defects of varying shapes and scales. Focal Loss is employed to mitigate class imbalance and refine detection accuracy. Additionally, the CD5-DET dataset is introduced, specifically designed for port container maintenance, featuring significant grayscale variations and intricate defect patterns. Experimental results demonstrate that the proposed model achieves substantial improvements, with mAP@0.5 gains of 27.6\%, 6.6\%, and 2.6\% on the CD5-DET, NEU-DET, and GC10-DET datasets.
Convex Hull-based Algebraic Constraint for Visual Quadric SLAM
Authors: Xiaolong Yu, Junqiao Zhao, Shuangfu Song, Zhongyang Zhu, Zihan Yuan, Chen Ye, Tiantian Feng
Date: 2025-03-03 00:00:00
Tasks: Object, Object Reconstruction, Pose Estimation, Semantic Segmentation

Using Quadrics as the object representation has the benefits of both generality and closed-form projection derivation between image and world spaces. Although numerous constraints have been proposed for dual quadric reconstruction, we found that many of them are imprecise and provide minimal improvements to localization.After scrutinizing the existing constraints, we introduce a concise yet more precise convex hull-based algebraic constraint for object landmarks, which is applied to object reconstruction, frontend pose estimation, and backend bundle adjustment.This constraint is designed to fully leverage precise semantic segmentation, effectively mitigating mismatches between complex-shaped object contours and dual quadrics.Experiments on public datasets demonstrate that our approach is applicable to both monocular and RGB-D SLAM and achieves improved object mapping and localization than existing quadric SLAM methods. The implementation of our method is available at https://github.com/tiev-tongji/convexhull-based-algebraic-constraint.
SVDC: Consistent Direct Time-of-Flight Video Depth Completion with Frequency Selective Fusion
Authors: Xuan Zhu, Jijun Xiang, Xianqi Wang, Longliang Liu, Yu Wang, Hong Zhang, Fei Guo, Xin Yang
Date: 2025-03-03 00:00:00
Proceeding: CVPR 2025 1
Tasks: Depth Completion

Lightweight direct Time-of-Flight (dToF) sensors are ideal for 3D sensing on mobile devices. However, due to the manufacturing constraints of compact devices and the inherent physical principles of imaging, dToF depth maps are sparse and noisy. In this paper, we propose a novel video depth completion method, called SVDC, by fusing the sparse dToF data with the corresponding RGB guidance. Our method employs a multi-frame fusion scheme to mitigate the spatial ambiguity resulting from the sparse dToF imaging. Misalignment between consecutive frames during multi-frame fusion could cause blending between object edges and the background, which results in a loss of detail. To address this, we introduce an adaptive frequency selective fusion (AFSF) module, which automatically selects convolution kernel sizes to fuse multi-frame features. Our AFSF utilizes a channel-spatial enhancement attention (CSEA) module to enhance features and generates an attention map as fusion weights. The AFSF ensures edge detail recovery while suppressing high-frequency noise in smooth regions. To further enhance temporal consistency, We propose a cross-window consistency loss to ensure consistent predictions across different windows, effectively reducing flickering. Our proposed SVDC achieves optimal accuracy and consistency on the TartanAir and Dynamic Replica datasets. Code is available at https://github.com/Lan1eve/SVDC.