pwc-archive/papers-with-abstracts huggingface dataset:
Interpretable Deep Learning Paradigm for Airborne Transient Electromagnetic Inversion
Authors: Shuang Wang, Xuben Wang, Fei Deng, Xiaodong Yu, Peifan Jiang, Lifeng Mao
Date: 2025-03-28 00:00:00
Tasks: Deep Learning, Denoising, Representation Learning

The extraction of geoelectric structural information from airborne transient electromagnetic(ATEM)data primarily involves data processing and inversion. Conventional methods rely on empirical parameter selection, making it difficult to process complex field data with high noise levels. Additionally, inversion computations are time consuming and often suffer from multiple local minima. Existing deep learning-based approaches separate the data processing steps, where independently trained denoising networks struggle to ensure the reliability of subsequent inversions. Moreover, end to end networks lack interpretability. To address these issues, we propose a unified and interpretable deep learning inversion paradigm based on disentangled representation learning. The network explicitly decomposes noisy data into noise and signal factors, completing the entire data processing workflow based on the signal factors while incorporating physical information for guidance. This approach enhances the network's reliability and interpretability. The inversion results on field data demonstrate that our method can directly use noisy data to accurately reconstruct the subsurface electrical structure. Furthermore, it effectively processes data severely affected by environmental noise, which traditional methods struggle with, yielding improved lateral structural resolution.
Camera Model Identification with SPAIR-Swin and Entropy based Non-Homogeneous Patches
Authors: Protyay Dey, Rejoy Chakraborty, Abhilasha S. Jadhav, Kapil Rana, Gaurav Sharma, Puneet Goyal
Date: 2025-03-28 00:00:00
Tasks: Image Forensics

Source camera model identification (SCMI) plays a pivotal role in image forensics with applications including authenticity verification and copyright protection. For identifying the camera model used to capture a given image, we propose SPAIR-Swin, a novel model combining a modified spatial attention mechanism and inverted residual block (SPAIR) with a Swin Transformer. SPAIR-Swin effectively captures both global and local features, enabling robust identification of artifacts such as noise patterns that are particularly effective for SCMI. Additionally, unlike conventional methods focusing on homogeneous patches, we propose a patch selection strategy for SCMI that emphasizes high-entropy regions rich in patterns and textures. Extensive evaluations on four benchmark SCMI datasets demonstrate that SPAIR-Swin outperforms existing methods, achieving patch-level accuracies of 99.45%, 98.39%, 99.45%, and 97.46% and image-level accuracies of 99.87%, 99.32%, 100%, and 98.61% on the Dresden, Vision, Forchheim, and Socrates datasets, respectively. Our findings highlight that high-entropy patches, which contain high-frequency information such as edge sharpness, noise, and compression artifacts, are more favorable in improving SCMI accuracy. Code will be made available upon request.
Parametric Point Cloud Completion for Polygonal Surface Reconstruction
Authors: Zhaiyu Chen, Yuqing Wang, Liangliang Nan, Xiao Xiang Zhu
Date: 2025-03-11 00:00:00
Proceeding: CVPR 2025 1
Tasks: Point Cloud Completion, Surface Reconstruction

Existing polygonal surface reconstruction methods heavily depend on input completeness and struggle with incomplete point clouds. We argue that while current point cloud completion techniques may recover missing points, they are not optimized for polygonal surface reconstruction, where the parametric representation of underlying surfaces remains overlooked. To address this gap, we introduce parametric completion, a novel paradigm for point cloud completion, which recovers parametric primitives instead of individual points to convey high-level geometric structures. Our presented approach, PaCo, enables high-quality polygonal surface reconstruction by leveraging plane proxies that encapsulate both plane parameters and inlier points, proving particularly effective in challenging scenarios with highly incomplete data. Comprehensive evaluations of our approach on the ABC dataset establish its effectiveness with superior performance and set a new standard for polygonal surface reconstruction from incomplete data. Project page: https://parametric-completion.github.io.
Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing
Authors: Na-Young Lim, Tae-Hyoung Park
Date: 2025-02-13 00:00:00
Proceeding: IEEE Access 2025 2
Tasks: Autonomous Driving, Autonomous Racing, Clustering, Computational Efficiency, Road Segmentation, vehicle detection

With the expansion of autonomous driving technology, autonomous racing has been actively studied in recent years. For safe autonomous racing, fast computation speed and wide detection range are essential. However, existing methods can perform detection only in short range due to the sparsity of LiDAR data. While some methods have been proposed to address this limitation, they often neglect real-time processing, making them unsuitable for racing environments. To address this, we propose a novel clustering and classification-based method specifically designed to enhance long-range vehicle detection while maintaining computational efficiency. First, our method employs a lightweight road segmentation and ground removal module to eliminate irrelevant data, significantly reducing computational overhead without down-sampling. Second, we introduce a 2D Bird’s Eye View (BEV)-based clustering approach, which is faster and more robust than traditional 3D Euclidean clustering, to generate object candidates efficiently. Additionally, a machine learning classifier trained on long-range vehicle features is incorporated to enhance detection accuracy at extended distances. Finally, range-view-based verification is performed to refine and finalize detection results, ensuring high reliability. The proposed method is evaluated on the aiMotive dataset, long-range dataset. Experimental results demonstrate that our approach achieves over 80% detection accuracy at distances exceeding 50 meters, with a computation time of only 25 ms per frame. These results underscore the novelty and effectiveness of the proposed method as an optimized solution for long-range vehicle detection in real-time autonomous racing environments.
LSNet: See Large, Focus Small
Authors: Ao Wang, Hui Chen, Zijia Lin, Jungong Han, Guiguang Ding
Date: 2025-03-29 00:00:00
Proceeding: CVPR 2025 1

Vision network designs, including Convolutional Neural Networks and Vision Transformers, have significantly advanced the field of computer vision. Yet, their complex computations pose challenges for practical deployments, particularly in real-time applications. To tackle this issue, researchers have explored various lightweight and efficient network designs. However, existing lightweight models predominantly leverage self-attention mechanisms and convolutions for token mixing. This dependence brings limitations in effectiveness and efficiency in the perception and aggregation processes of lightweight networks, hindering the balance between performance and efficiency under limited computational budgets. In this paper, we draw inspiration from the dynamic heteroscale vision ability inherent in the efficient human vision system and propose a ``See Large, Focus Small'' strategy for lightweight vision network design. We introduce LS (\textbf{L}arge-\textbf{S}mall) convolution, which combines large-kernel perception and small-kernel aggregation. It can efficiently capture a wide range of perceptual information and achieve precise feature aggregation for dynamic and complex visual representations, thus enabling proficient processing of visual information. Based on LS convolution, we present LSNet, a new family of lightweight models. Extensive experiments demonstrate that LSNet achieves superior performance and efficiency over existing lightweight networks in various vision tasks. Codes and models are available at https://github.com/jameslahm/lsnet.
Language-Inspired Modeling Reveals Redundant Encoding of N4-acetylcytidine(ac4C) Modifications in mRNA
Authors: Li Yang, Dongbo Wang
Date: 2025-03-30 00:00:00

The ac4C modification on mRNA has been demonstrated to be associated with various diseases; however, its molecular mechanism remains unclear. The wet lab experiments produced relatively rough data, which lack precise ac4C modification sites, and extracting valuable information from such data remains a challenge. In this study, we integrate linguistics, traditional machine learning, and deep learning, establishing a link between the understanding of mRNA data and natural language processing (NLP). Through our analysis, we successfully revealed key information about ac4C in mRNA and uncovered the information storage mechanism of ac4C redundancy on a single sequence. This redundant information storage method in mRNA facilitates the transmission of ac4C information and promotes the enrichment of ac4C.
Redundant feature screening method for human activity recognition based on attention purification mechanism
Authors: Hanyu Liu, Xiaoyang Li, Yixuan Jiang, Haotian Tang, Dongchen Wu, Yameng Guo
Date: 2025-03-30 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

In the field of sensor-based Human Activity Recognition (HAR), deep neural networks provide advanced technical support. Many studies have proven that recognition accuracy can be improved by increasing the depth or width of the network. However, for wearable devices, the balance between network performance and resource consumption is crucial. With minimum resource consumption as the basic principle, we propose a universal attention feature purification mechanism, called MSAP, which is suitable for multi-scale networks. The mechanism effectively solves the feature redundancy caused by the superposition of multi-scale features by means of inter-scale attention screening and connection method. In addition, we have designed a network correction module that integrates seamlessly between layers of individual network modules to mitigate inherent problems in deep networks. We also built an embedded deployment system that is in line with the current level of wearable technology to test the practical feasibility of the HAR model, and further prove the efficiency of the method. Extensive experiments on four public datasets show that the proposed method model effectively reduces redundant features in filtered data and provides excellent performance with little resource consumption.
A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial Imagery
Authors: PengYu Chen, Sicheng Wang, Cuizhen Wang, Senrong Wang, Beiao Huang, Lu Huang, Zhe Zang
Date: 2025-03-29 00:00:00
Tasks: Colorization, Image Colorization, Image Enhancement, object-detection, Object Detection, Super-Resolution

Accurate rooftop detection from historical aerial imagery is vital for examining long-term urban development and human settlement patterns. However, black-and-white analog photographs pose significant challenges for modern object detection frameworks due to their limited spatial resolution, lack of color information, and archival degradation. To address these limitations, this study introduces a two-stage image enhancement pipeline based on Generative Adversarial Networks (GANs): image colorization using DeOldify, followed by super-resolution enhancement with Real-ESRGAN. The enhanced images were then used to train and evaluate rooftop detection models, including Faster R-CNN, DETReg, and YOLOv11n. Results show that combining colorization with super-resolution substantially improves detection performance, with YOLOv11n achieving a mean Average Precision (mAP) exceeding 85%. This reflects an improvement of approximately 40% over original black-and-white images and 20% over images enhanced through colorization alone. The proposed method effectively bridges the gap between archival imagery and contemporary deep learning techniques, enabling more reliable extraction of building footprints from historical aerial photographs.
Efficient image encryption algorithm based on ECC and dynamic S-box
Authors: Ghulam Murtaza, Umar Hayat
Date: 2025-02-28 00:00:00
Proceeding: Journal of Information Security and Applications 2025 2

Image encryption is widely used to convert digital images into an unreadable form when data is transferred through open networks and the internet. However, traditional cryptosystems have small encryption key sizes and are unsuitable for real-time implementation because image pixels have high redundancy and large data capacity. We have designed an image cryptosystem to tackle the issues related to the smaller key size and plaintext attacks. There are three phases of our proposed image cryptosystem. A pseudo-random number generator is used in the first phase to diffuse the pixels of the plain image. A substitution box generator is designed in the second phase to generate substitution boxes with high nonlinearity. The last phase provides an image encryption technique to encrypt grayscale images. The proposed scheme encrypts images with an entropy value close to the optimal value, which increases security. The encryption algorithm requires 0.556 s to encrypt a 256 × 256 grayscale image. Further, extensive experiments show that the cryptosystem is highly sensitive to the input keys.
ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks
Authors: Mohamed Afane, Gabrielle Ebbrecht, Ying Wang, Juntao Chen, Junaid Farooq
Date: 2025-03-26 00:00:00
Proceeding: CVPR 2025 1
Tasks: Adversarial Robustness, Computational Efficiency

Quantum Neural Networks (QNNs) offer promising capabilities for complex data tasks, but are often constrained by limited qubit resources and high entanglement, which can hinder scalability and efficiency. In this paper, we introduce Adaptive Threshold Pruning (ATP), an encoding method that reduces entanglement and optimizes data complexity for efficient computations in QNNs. ATP dynamically prunes non-essential features in the data based on adaptive thresholds, effectively reducing quantum circuit requirements while preserving high performance. Extensive experiments across multiple datasets demonstrate that ATP reduces entanglement entropy and improves adversarial robustness when combined with adversarial training methods like FGSM. Our results highlight ATPs ability to balance computational efficiency and model resilience, achieving significant performance improvements with fewer resources, which will help make QNNs more feasible in practical, resource-constrained settings.
Multi-modal Knowledge Distillation-based Human Trajectory Forecasting
Authors: Jaewoo Jeong, Seohee Lee, Daehee Park, Giwon Lee, Kuk-Jin Yoon
Date: 2025-03-28 00:00:00
Proceeding: CVPR 2025 1
Tasks: Autonomous Driving, Knowledge Distillation, Robot Navigation, Trajectory Forecasting

Pedestrian trajectory forecasting is crucial in various applications such as autonomous driving and mobile robot navigation. In such applications, camera-based perception enables the extraction of additional modalities (human pose, text) to enhance prediction accuracy. Indeed, we find that textual descriptions play a crucial role in integrating additional modalities into a unified understanding. However, online extraction of text requires the use of VLM, which may not be feasible for resource-constrained systems. To address this challenge, we propose a multi-modal knowledge distillation framework: a student model with limited modality is distilled from a teacher model trained with full range of modalities. The comprehensive knowledge of a teacher model trained with trajectory, human pose, and text is distilled into a student model using only trajectory or human pose as a sole supplement. In doing so, we separately distill the core locomotion insights from intra-agent multi-modality and inter-agent interaction. Our generalizable framework is validated with two state-of-the-art models across three datasets on both ego-view (JRDB, SIT) and BEV-view (ETH/UCY) setups, utilizing both annotated and VLM-generated text captions. Distilled student models show consistent improvement in all prediction metrics for both full and instantaneous observations, improving up to ~13%. The code is available at https://github.com/Jaewoo97/KDTF.
Enhance Generation Quality of Flow Matching V2A Model via Multi-Step CoT-Like Guidance and Combined Preference Optimization
Authors: Haomin Zhang, Sizhe Shan, Haoyu Wang, Zihao Chen, Xiulong Liu, Chaofan Ding, Xinhan Di
Date: 2025-03-28 00:00:00
Tasks: Audio Generation, FAD

Creating high-quality sound effects from videos and text prompts requires precise alignment between visual and audio domains, both semantically and temporally, along with step-by-step guidance for professional audio generation. However, current state-of-the-art video-guided audio generation models often fall short of producing high-quality audio for both general and specialized use cases. To address this challenge, we introduce a multi-stage, multi-modal, end-to-end generative framework with Chain-of-Thought-like (CoT-like) guidance learning, termed Chain-of-Perform (CoP). First, we employ a transformer-based network architecture designed to achieve CoP guidance, enabling the generation of both general and professional audio. Second, we implement a multi-stage training framework that follows step-by-step guidance to ensure the generation of high-quality sound effects. Third, we develop a CoP multi-modal dataset, guided by video, to support step-by-step sound effects generation. Evaluation results highlight the advantages of the proposed multi-stage CoP generative framework compared to the state-of-the-art models on a variety of datasets, with FAD 0.79 to 0.74 (+6.33%), CLIP 16.12 to 17.70 (+9.80%) on VGGSound, SI-SDR 1.98dB to 3.35dB (+69.19%), MOS 2.94 to 3.49(+18.71%) on PianoYT-2h, and SI-SDR 2.22dB to 3.21dB (+44.59%), MOS 3.07 to 3.42 (+11.40%) on Piano-10h.
Hyperspectral Adapter for Object Tracking based on Hyperspectral Video
Authors: Long Gao, Yunhe Zhang, Langkun Chen, Yan Jiang, Weiying Xie, Yunsong Li
Date: 2025-03-28 00:00:00
Tasks: Object, Object Tracking

Object tracking based on hyperspectral video attracts increasing attention to the rich material and motion information in the hyperspectral videos. The prevailing hyperspectral methods adapt pretrained RGB-based object tracking networks for hyperspectral tasks by fine-tuning the entire network on hyperspectral datasets, which achieves impressive results in challenging scenarios. However, the performance of hyperspectral trackers is limited by the loss of spectral information during the transformation, and fine-tuning the entire pretrained network is inefficient for practical applications. To address the issues, a new hyperspectral object tracking method, hyperspectral adapter for tracking (HyA-T), is proposed in this work. The hyperspectral adapter for the self-attention (HAS) and the hyperspectral adapter for the multilayer perceptron (HAM) are proposed to generate the adaption information and to transfer the multi-head self-attention (MSA) module and the multilayer perceptron (MLP) in pretrained network for the hyperspectral object tracking task by augmenting the adaption information into the calculation of the MSA and MLP. Additionally, the hyperspectral enhancement of input (HEI) is proposed to augment the original spectral information into the input of the tracking network. The proposed methods extract spectral information directly from the hyperspectral images, which prevent the loss of the spectral information. Moreover, only the parameters in the proposed methods are fine-tuned, which is more efficient than the existing methods. Extensive experiments were conducted on four datasets with various spectral bands, verifing the effectiveness of the proposed methods. The HyA-T achieves state-of-the-art performance on all the datasets.
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning
Authors: Yang Liu, Xun Zhang, Jiale Du, Xinbo Gao, Jungong Han
Date: 2025-03-28 00:00:00
Tasks: Binary Classification, Generalized Zero-Shot Learning, Out-of-Distribution Detection, Out of Distribution (OOD) Detection, Transfer Learning, Zero-Shot Learning

Zero-shot Learning(ZSL) attains knowledge transfer from seen classes to unseen classes by exploring auxiliary category information, which is a promising yet difficult research topic. In this field, Audio-Visual Generalized Zero-Shot Learning~(AV-GZSL) has aroused researchers' great interest in which intricate relations within triple modalities~(audio, video, and natural language) render this task quite challenging but highly research-worthy. However, both existing embedding-based and generative-based AV-GZSL methods tend to suffer from domain shift problem a lot and we propose an extremely simple Out-of-distribution~(OOD) detection based AV-GZSL method~(EZ-AVOOD) to further mitigate bias problem by differentiating seen and unseen samples at the initial beginning. EZ-AVOOD accomplishes effective seen-unseen separation by exploiting the intrinsic discriminative information held in class-specific logits and class-agnostic feature subspace without training an extra OOD detector network. Followed by seen-unseen binary classification, we employ two expert models to classify seen samples and unseen samples separately. Compared to existing state-of-the-art methods, our model achieves superior ZSL and GZSL performances on three audio-visual datasets and becomes the new SOTA, which comprehensively demonstrates the effectiveness of the proposed EZ-AVOOD.
Unbiased Max-Min Embedding Classification for Transductive Few-Shot Learning: Clustering and Classification Are All You Need
Authors: Yang Liu, Feixiang Liu, Jiale Du, Xinbo Gao, Jungong Han
Date: 2025-03-28 00:00:00
Tasks: All, Classification, Few-Shot Learning

Convolutional neural networks and supervised learning have achieved remarkable success in various fields but are limited by the need for large annotated datasets. Few-shot learning (FSL) addresses this limitation by enabling models to generalize from only a few labeled examples. Transductive few-shot learning (TFSL) enhances FSL by leveraging both labeled and unlabeled data, though it faces challenges like the hubness problem. To overcome these limitations, we propose the Unbiased Max-Min Embedding Classification (UMMEC) Method, which addresses the key challenges in few-shot learning through three innovative contributions. First, we introduce a decentralized covariance matrix to mitigate the hubness problem, ensuring a more uniform distribution of embeddings. Second, our method combines local alignment and global uniformity through adaptive weighting and nonlinear transformation, balancing intra-class clustering with inter-class separation. Third, we employ a Variational Sinkhorn Few-Shot Classifier to optimize the distances between samples and class prototypes, enhancing classification accuracy and robustness. These combined innovations allow the UMMEC method to achieve superior performance with minimal labeled data. Our UMMEC method significantly improves classification performance with minimal labeled data, advancing the state-of-the-art in TFSL.