pwc-archive/papers-with-abstracts huggingface dataset:
Camera-Only Bird's Eye View Perception: A Neural Approach to LiDAR-Free Environmental Mapping for Autonomous Vehicles
Authors: Anupkumar Bochare
Date: 2025-05-09 00:00:00
Tasks: Autonomous Navigation, Autonomous Vehicles, Depth Estimation, Monocular Depth Estimation, object-detection, Object Detection, Road Segmentation, Scene Understanding, vehicle detection

Autonomous vehicle perception systems have traditionally relied on costly LiDAR sensors to generate precise environmental representations. In this paper, we propose a camera-only perception framework that produces Bird's Eye View (BEV) maps by extending the Lift-Splat-Shoot architecture. Our method combines YOLOv11-based object detection with DepthAnythingV2 monocular depth estimation across multi-camera inputs to achieve comprehensive 360-degree scene understanding. We evaluate our approach on the OpenLane-V2 and NuScenes datasets, achieving up to 85% road segmentation accuracy and 85-90% vehicle detection rates when compared against LiDAR ground truth, with average positional errors limited to 1.2 meters. These results highlight the potential of deep learning to extract rich spatial information using only camera inputs, enabling cost-efficient autonomous navigation without sacrificing accuracy.
S2MNet: Speckle-To-Mesh Net for Three-Dimensional Cardiac Morphology Reconstruction via Echocardiogram
Authors: Xilin Gong, Yongkai Chen, Shushan Wu, Fang Wang, Ping Ma, Wenxuan Zhong
Date: 2025-05-09 00:00:00
Tasks: Anatomy

Echocardiogram is the most commonly used imaging modality in cardiac assessment duo to its non-invasive nature, real-time capability, and cost-effectiveness. Despite its advantages, most clinical echocardiograms provide only two-dimensional views, limiting the ability to fully assess cardiac anatomy and function in three dimensions. While three-dimensional echocardiography exists, it often suffers from reduced resolution, limited availability, and higher acquisition costs. To overcome these challenges, we propose a deep learning framework S2MNet that reconstructs continuous and high-fidelity 3D heart models by integrating six slices of routinely acquired 2D echocardiogram views. Our method has three advantages. First, our method avoid the difficulties on training data acquasition by simulate six of 2D echocardiogram images from corresponding slices of a given 3D heart mesh. Second, we introduce a deformation field-based method, which avoid spatial discontinuities or structural artifacts in 3D echocardiogram reconstructions. We validate our method using clinically collected echocardiogram and demonstrate that our estimated left ventricular volume, a key clinical indicator of cardiac function, is strongly correlated with the doctor measured GLPS, a clinical measurement that should demonstrate a negative correlation with LVE in medical theory. This association confirms the reliability of our proposed 3D construction method.
TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations
Authors: Shuaiyi Huang, Mara Levy, Anubhav Gupta, Daniel Ekpo, Ruijie Zheng, Abhinav Shrivastava
Date: 2025-05-09 00:00:00

Preference feedback collected by human or VLM annotators is often noisy, presenting a significant challenge for preference-based reinforcement learning that relies on accurate preference labels. To address this challenge, we propose TREND, a novel framework that integrates few-shot expert demonstrations with a tri-teaching strategy for effective noise mitigation. Our method trains three reward models simultaneously, where each model views its small-loss preference pairs as useful knowledge and teaches such useful pairs to its peer network for updating the parameters. Remarkably, our approach requires as few as one to three expert demonstrations to achieve high performance. We evaluate TREND on various robotic manipulation tasks, achieving up to 90% success rates even with noise levels as high as 40%, highlighting its effective robustness in handling noisy preference feedback. Project page: https://shuaiyihuang.github.io/publications/TREND.
Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation
Authors: Kunpeng Qiu, Zhiqiang Gao, Zhiying Zhou, MingJie Sun, Yongxin Guo
Date: 2025-05-09 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Generation, Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Deep learning has revolutionized medical image segmentation, yet its full potential remains constrained by the paucity of annotated datasets. While diffusion models have emerged as a promising approach for generating synthetic image-mask pairs to augment these datasets, they paradoxically suffer from the same data scarcity challenges they aim to mitigate. Traditional mask-only models frequently yield low-fidelity images due to their inability to adequately capture morphological intricacies, which can critically compromise the robustness and reliability of segmentation models. To alleviate this limitation, we introduce Siamese-Diffusion, a novel dual-component model comprising Mask-Diffusion and Image-Diffusion. During training, a Noise Consistency Loss is introduced between these components to enhance the morphological fidelity of Mask-Diffusion in the parameter space. During sampling, only Mask-Diffusion is used, ensuring diversity and scalability. Comprehensive experiments demonstrate the superiority of our method. Siamese-Diffusion boosts SANet's mDice and mIoU by 3.6% and 4.4% on the Polyps, while UNet improves by 1.52% and 1.64% on the ISIC2018. Code is available at GitHub.
Towards Better Cephalometric Landmark Detection with Diffusion Data Generation
Authors: Dongqian Guo, Wencheng Han, Pang Lyu, Yuxi Zhou, Jianbing Shen
Date: 2025-05-09 00:00:00

Cephalometric landmark detection is essential for orthodontic diagnostics and treatment planning. Nevertheless, the scarcity of samples in data collection and the extensive effort required for manual annotation have significantly impeded the availability of diverse datasets. This limitation has restricted the effectiveness of deep learning-based detection methods, particularly those based on large-scale vision models. To address these challenges, we have developed an innovative data generation method capable of producing diverse cephalometric X-ray images along with corresponding annotations without human intervention. To achieve this, our approach initiates by constructing new cephalometric landmark annotations using anatomical priors. Then, we employ a diffusion-based generator to create realistic X-ray images that correspond closely with these annotations. To achieve precise control in producing samples with different attributes, we introduce a novel prompt cephalometric X-ray image dataset. This dataset includes real cephalometric X-ray images and detailed medical text prompts describing the images. By leveraging these detailed prompts, our method improves the generation process to control different styles and attributes. Facilitated by the large, diverse generated data, we introduce large-scale vision detection models into the cephalometric landmark detection task to improve accuracy. Experimental results demonstrate that training with the generated data substantially enhances the performance. Compared to methods without using the generated data, our approach improves the Success Detection Rate (SDR) by 6.5%, attaining a notable 82.2%. All code and data are available at: https://um-lab.github.io/cepha-generation
Document Image Rectification Bases on Self-Adaptive Multitask Fusion
Authors: Heng Li, XiangPing Wu, Qingcai Chen
Date: 2025-05-09 00:00:00
Tasks: document understanding

Deformed document image rectification is essential for real-world document understanding tasks, such as layout analysis and text recognition. However, current multi-task methods -- such as background removal, 3D coordinate prediction, and text line segmentation -- often overlook the complementary features between tasks and their interactions. To address this gap, we propose a self-adaptive learnable multi-task fusion rectification network named SalmRec. This network incorporates an inter-task feature aggregation module that adaptively improves the perception of geometric distortions, enhances feature complementarity, and reduces negative interference. We also introduce a gating mechanism to balance features both within global tasks and between local tasks effectively. Experimental results on two English benchmarks (DIR300 and DocUNet) and one Chinese benchmark (DocReal) demonstrate that our method significantly improves rectification performance. Ablation studies further highlight the positive impact of different tasks on dewarping and the effectiveness of our proposed module.
From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection
Authors: Moritz Vandenhirtz, Julia E. Vogt
Date: 2025-05-09 00:00:00
Tasks: Decision Making, feature selection

Understanding the decision-making process of machine learning models provides valuable insights into the task, the data, and the reasons behind a model's failures. In this work, we propose a method that performs inherently interpretable predictions through the instance-wise sparsification of input images. To align the sparsification with human perception, we learn the masking in the space of semantically meaningful pixel regions rather than on pixel-level. Additionally, we introduce an explicit way to dynamically determine the required level of sparsity for each instance. We show empirically on semi-synthetic and natural image datasets that our inherently interpretable classifier produces more meaningful, human-understandable predictions than state-of-the-art benchmarks.
Task-Adapter++: Task-specific Adaptation with Order-aware Alignment for Few-shot Action Recognition
Authors: Congqi Cao, Peiheng Han, Yueran Zhang, Yating Yu, Qinyi Lv, Lingtong Min, Yanning Zhang
Date: 2025-05-09 00:00:00
Tasks: Action Recognition, cross-modal alignment, Few-Shot action recognition, Few Shot Action Recognition, Few-Shot Learning

Large-scale pre-trained models have achieved remarkable success in language and image tasks, leading an increasing number of studies to explore the application of pre-trained image models, such as CLIP, in the domain of few-shot action recognition (FSAR). However, current methods generally suffer from several problems: 1) Direct fine-tuning often undermines the generalization capability of the pre-trained model; 2) The exploration of task-specific information is insufficient in the visual tasks; 3) The semantic order information is typically overlooked during text modeling; 4) Existing cross-modal alignment techniques ignore the temporal coupling of multimodal information. To address these, we propose Task-Adapter++, a parameter-efficient dual adaptation method for both image and text encoders. Specifically, to make full use of the variations across different few-shot learning tasks, we design a task-specific adaptation for the image encoder so that the most discriminative information can be well noticed during feature extraction. Furthermore, we leverage large language models (LLMs) to generate detailed sequential sub-action descriptions for each action class, and introduce semantic order adapters into the text encoder to effectively model the sequential relationships between these sub-actions. Finally, we develop an innovative fine-grained cross-modal alignment strategy that actively maps visual features to reside in the same temporal stage as semantic descriptions. Extensive experiments fully demonstrate the effectiveness and superiority of the proposed method, which achieves state-of-the-art performance on 5 benchmarks consistently. The code is open-sourced at https://github.com/Jaulin-Bage/Task-Adapter-pp.
Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints
Authors: Peter Röseler, Oliver Schaudt, Helmut Berg, Christian Bauckhage, Matthias Koch
Date: 2025-05-09 00:00:00
Tasks: Dimensionality Reduction, image-classification, Image Classification

While classical convolutional neural networks (CNNs) have revolutionized image classification, the emergence of quantum computing presents new opportunities for enhancing neural network architectures. Quantum CNNs (QCNNs) leverage quantum mechanical properties and hold potential to outperform classical approaches. However, their implementation on current noisy intermediate-scale quantum (NISQ) devices remains challenging due to hardware limitations. In our research, we address this challenge by introducing an encoding scheme that significantly reduces the input dimensionality. We demonstrate that a primitive QCNN architecture with 49 qubits is sufficient to directly process $28\times 28$ pixel MNIST images, eliminating the need for classical dimensionality reduction pre-processing. Additionally, we propose an automated framework based on expressibility, entanglement, and complexity characteristics to identify the building blocks of QCNNs, parameterized quantum circuits (PQCs). Our approach demonstrates advantages in accuracy and convergence speed with a similar parameter count compared to both hybrid QCNNs and classical CNNs. We validated our experiments on IBM's Heron r2 quantum processor, achieving $96.08\%$ classification accuracy, surpassing the $71.74\%$ benchmark of traditional approaches under identical training conditions. These results represent one of the first implementations of image classifications on real quantum hardware and validate the potential of quantum computing in this area.
CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking
Authors: Weihong Li, Xiaoqiong Liu, Heng Fan, Libo Zhang
Date: 2025-05-09 00:00:00
Tasks: Object Tracking, Visual Object Tracking

Recent advancements in visual object tracking have markedly improved the capabilities of unmanned aerial vehicle (UAV) tracking, which is a critical component in real-world robotics applications. While the integration of hierarchical lightweight networks has become a prevalent strategy for enhancing efficiency in UAV tracking, it often results in a significant drop in network capacity, which further exacerbates challenges in UAV scenarios, such as frequent occlusions and extreme changes in viewing angles. To address these issues, we introduce a novel family of UAV trackers, termed CGTrack, which combines explicit and implicit techniques to expand network capacity within a coarse-to-fine framework. Specifically, we first introduce a Hierarchical Feature Cascade (HFC) module that leverages the spirit of feature reuse to increase network capacity by integrating the deep semantic cues with the rich spatial information, incurring minimal computational costs while enhancing feature representation. Based on this, we design a novel Lightweight Gated Center Head (LGCH) that utilizes gating mechanisms to decouple target-oriented coordinates from previously expanded features, which contain dense local discriminative information. Extensive experiments on three challenging UAV tracking benchmarks demonstrate that CGTrack achieves state-of-the-art performance while running fast. Code will be available at https://github.com/Nightwatch-Fox11/CGTrack.
DFEN: Dual Feature Equalization Network for Medical Image Segmentation
Authors: Jianjian Yin, Yi Chen, Chengyu Li, Zhichao Zheng, Yanhui Gu, Junsheng Zhou
Date: 2025-05-09 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Current methods for medical image segmentation primarily focus on extracting contextual feature information from the perspective of the whole image. While these methods have shown effective performance, none of them take into account the fact that pixels at the boundary and regions with a low number of class pixels capture more contextual feature information from other classes, leading to misclassification of pixels by unequal contextual feature information. In this paper, we propose a dual feature equalization network based on the hybrid architecture of Swin Transformer and Convolutional Neural Network, aiming to augment the pixel feature representations by image-level equalization feature information and class-level equalization feature information. Firstly, the image-level feature equalization module is designed to equalize the contextual information of pixels within the image. Secondly, we aggregate regions of the same class to equalize the pixel feature representations of the corresponding class by class-level feature equalization module. Finally, the pixel feature representations are enhanced by learning weights for image-level equalization feature information and class-level equalization feature information. In addition, Swin Transformer is utilized as both the encoder and decoder, thereby bolstering the ability of the model to capture long-range dependencies and spatial correlations. We conducted extensive experiments on Breast Ultrasound Images (BUSI), International Skin Imaging Collaboration (ISIC2017), Automated Cardiac Diagnosis Challenge (ACDC) and PH$^2$ datasets. The experimental results demonstrate that our method have achieved state-of-the-art performance. Our code is publicly available at https://github.com/JianJianYin/DFEN.
Register and CLS tokens yield a decoupling of local and global features in large ViTs
Authors: Alexander Lappe, Martin A. Giese
Date: 2025-05-09 00:00:00

Recent work has shown that the attention maps of the widely popular DINOv2 model exhibit artifacts, which hurt both model interpretability and performance on dense image tasks. These artifacts emerge due to the model repurposing patch tokens with redundant local information for the storage of global image information. To address this problem, additional register tokens have been incorporated in which the model can store such information instead. We carefully examine the influence of these register tokens on the relationship between global and local image features, showing that while register tokens yield cleaner attention maps, these maps do not accurately reflect the integration of local image information in large models. Instead, global information is dominated by information extracted from register tokens, leading to a disconnect between local and global features. Inspired by these findings, we show that the CLS token itself, which can be interpreted as a register, leads to a very similar phenomenon in models without explicit register tokens. Our work shows that care must be taken when interpreting attention maps of large ViTs. Further, by clearly attributing the faulty behaviour to register and CLS tokens, we show a path towards more interpretable vision models.
Decoupling Multi-Contrast Super-Resolution: Pairing Unpaired Synthesis with Implicit Representations
Authors: Hongyu Rui, Yinzhe Wu, Fanwen Wang, Jiahao Huang, Liutao Yang, Zi Wang, Guang Yang
Date: 2025-05-09 00:00:00
Tasks: Super-Resolution

Magnetic Resonance Imaging (MRI) is critical for clinical diagnostics but is often limited by long acquisition times and low signal-to-noise ratios, especially in modalities like diffusion and functional MRI. The multi-contrast nature of MRI presents a valuable opportunity for cross-modal enhancement, where high-resolution (HR) modalities can serve as references to boost the quality of their low-resolution (LR) counterparts-motivating the development of Multi-Contrast Super-Resolution (MCSR) techniques. Prior work has shown that leveraging complementary contrasts can improve SR performance; however, effective feature extraction and fusion across modalities with varying resolutions remains a major challenge. Moreover, existing MCSR methods often assume fixed resolution settings and all require large, perfectly paired training datasets-conditions rarely met in real-world clinical environments. To address these challenges, we propose a novel Modular Multi-Contrast Super-Resolution (MCSR) framework that eliminates the need for paired training data and supports arbitrary upscaling. Our method decouples the MCSR task into two stages: (1) Unpaired Cross-Modal Synthesis (U-CMS), which translates a high-resolution reference modality into a synthesized version of the target contrast, and (2) Unsupervised Super-Resolution (U-SR), which reconstructs the final output using implicit neural representations (INRs) conditioned on spatial coordinates. This design enables scale-agnostic and anatomically faithful reconstruction by bridging un-paired cross-modal synthesis with unsupervised resolution enhancement. Experiments show that our method achieves superior performance at 4x and 8x upscaling, with improved fidelity and anatomical consistency over existing baselines. Our framework demonstrates strong potential for scalable, subject-specific, and data-efficient MCSR in real-world clinical settings.
PICD: Versatile Perceptual Image Compression with Diffusion Rendering
Authors: Tongda Xu, Jiahao Li, Bin Li, Yan Wang, Ya-Qin Zhang, Yan Lu
Date: 2025-05-09 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Compression

Recently, perceptual image compression has achieved significant advancements, delivering high visual quality at low bitrates for natural images. However, for screen content, existing methods often produce noticeable artifacts when compressing text. To tackle this challenge, we propose versatile perceptual screen image compression with diffusion rendering (PICD), a codec that works well for both screen and natural images. More specifically, we propose a compression framework that encodes the text and image separately, and renders them into one image using diffusion model. For this diffusion rendering, we integrate conditional information into diffusion models at three distinct levels: 1). Domain level: We fine-tune the base diffusion model using text content prompts with screen content. 2). Adaptor level: We develop an efficient adaptor to control the diffusion model using compressed image and text as input. 3). Instance level: We apply instance-wise guidance to further enhance the decoding process. Empirically, our PICD surpasses existing perceptual codecs in terms of both text accuracy and perceptual quality. Additionally, without text conditions, our approach serves effectively as a perceptual codec for natural images.
Automated Knot Detection and Pairing for Wood Analysis in the Timber Industry
Authors: Guohao Lin, Shidong Pan, Rasul Khanbayov, Changxi Yang, Ani Khaloian-Sarnaghi, Andriy Kovryga
Date: 2025-05-09 00:00:00
Tasks: Transfer Learning, Triplet

Knots in wood are critical to both aesthetics and structural integrity, making their detection and pairing essential in timber processing. However, traditional manual annotation was labor-intensive and inefficient, necessitating automation. This paper proposes a lightweight and fully automated pipeline for knot detection and pairing based on machine learning techniques. In the detection stage, high-resolution surface images of wooden boards were collected using industrial-grade cameras, and a large-scale dataset was manually annotated and preprocessed. After the transfer learning, the YOLOv8l achieves an mAP@0.5 of 0.887. In the pairing stage, detected knots were analyzed and paired based on multidimensional feature extraction. A triplet neural network was used to map the features into a latent space, enabling clustering algorithms to identify and pair corresponding knots. The triplet network with learnable weights achieved a pairing accuracy of 0.85. Further analysis revealed that he distances from the knot's start and end points to the bottom of the wooden board, and the longitudinal coordinates play crucial roles in achieving high pairing accuracy. Our experiments validate the effectiveness of the proposed solution, demonstrating the potential of AI in advancing wood science and industry.