International Journal of Computer Vision最新文献

{"title":"ScenarioDiff: Text-to-video Generation with Dynamic Transformations of Scene Conditions","authors":"Yipeng Zhang, Xin Wang, Hong Chen, Chenyang Qin, Yibo Hao, Hong Mei, Wenwu Zhu","doi":"10.1007/s11263-025-02413-7","DOIUrl":"https://doi.org/10.1007/s11263-025-02413-7","url":null,"abstract":"<p>With the development of diffusion models, text-to-video generation has recently received significant attention and achieved remarkable success. However, existing text-to-video approaches suffer from the following weaknesses: i) they fail to control the trajectory of the subject as well as the process of scene transformations; ii) they can only generate videos with limited frames, failing to capture the whole transformation process. To address these issues, we propose the model named ScenarioDiff, which is able to generate longer videos with scene transformations. Specifically, we employ a spatial layout fuser to control the positions of subjects and the scenes of each frame. To effectively present the process of scene transformation, we introduce mixed frequency controlnet, which utilizes several frames of the generated videos to extend them to long videos chunk by chunk in an auto-regressive manner. Additionally, to ensure consistency between different video chunks, we propose a cross-chunk scheduling mechanism during inference. Experimental results demonstrate the effectiveness of our approach in generating videos with dynamic scene transformations. Our project page is available at https://scenariodiff2024.github.io/.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"35 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143695282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LaneCorrect: Self-Supervised Lane Detection","authors":"Ming Nie, Xinyue Cai, Hang Xu, Li Zhang","doi":"10.1007/s11263-025-02417-3","DOIUrl":"https://doi.org/10.1007/s11263-025-02417-3","url":null,"abstract":"<p>Lane detection has evolved highly functional autonomous driving system to understand driving scenes even under complex environments. In this paper, we work towards developing a generalized computer vision system able to detect lanes without using <i>any</i> annotation. We make the following contributions: (i) We illustrate how to perform unsupervised 3D lane segmentation by leveraging the distinctive intensity of lanes on the LiDAR point cloud frames, and then obtain the noisy lane labels in the 2D plane by projecting the 3D points; (ii) We propose a novel self-supervised training scheme, dubbed <i>LaneCorrect</i>, that automatically corrects the lane label by learning geometric consistency and instance awareness from the adversarial augmentations; (iii) With the self-supervised pre-trained model, we distill to train a student network for arbitrary target lane (e.g., <i>TuSimple</i>) detection without any human labels; (iv) We thoroughly evaluate our self-supervised method on four major lane detection benchmarks (including <i>TuSimple, CULane, CurveLanes</i> and <i>LLAMAS</i>) and demonstrate excellent performance compared with existing supervised counterpart, whilst showing more effective results on alleviating the domain gap, i.e., training on <i>CULane</i> and test on <i>TuSimple</i>.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"183 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Camouflaged Object Detection with Adaptive Partition and Background Retrieval","authors":"Bowen Yin, Xuying Zhang, Li Liu, Ming-Ming Cheng, Yongxiang Liu, Qibin Hou","doi":"10.1007/s11263-025-02406-6","DOIUrl":"https://doi.org/10.1007/s11263-025-02406-6","url":null,"abstract":"<p>Recent works confirm the importance of local details for identifying camouflaged objects. However, how to identify the details around the target objects via background cues lacks in-depth study. In this paper, we take this into account and present a novel learning framework for camouflaged object detection, called AdaptCOD. To be specific, our method decouples the detection process into three parts, namely localization, segmentation, and retrieval. We design a context adaptive partition strategy to dynamically select a reasonable context region for local segmentation and a background retrieval module to further polish the camouflaged object boundaries. Despite the simplicity, our method enables even a simple COD model to achieve great performance. Extensive experiments show that AdaptCOD surpasses all existing state-of-the-art methods on three widely-used camouflaged object detection benchmarks. Code is publicly available at https://github.com/HVision-NKU/AdaptCOD.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"94 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms","authors":"Lea Bogensperger, Dominik Narnhofer, Alexander Falk, Konrad Schindler, Thomas Pock","doi":"10.1007/s11263-025-02373-y","DOIUrl":"https://doi.org/10.1007/s11263-025-02373-y","url":null,"abstract":"<p>Medical image segmentation plays an important role in accurately identifying and isolating regions of interest within medical images. Generative approaches are particularly effective in modeling the statistical properties of segmentation masks that are closely related to the respective structures. In this work we introduce FlowSDF, an image-guided conditional flow matching framework, designed to represent the signed distance function (SDF), and, in turn, to represent an implicit distribution of segmentation masks. The advantage of leveraging the SDF is a more natural distortion when compared to that of binary masks. Through the learning of a vector field associated with the probability path of conditional SDF distributions, our framework enables accurate sampling of segmentation masks and the computation of relevant statistical measures. This probabilistic approach also facilitates the generation of uncertainty maps represented by the variance, thereby supporting enhanced robustness in prediction and further analysis. We qualitatively and quantitatively illustrate competitive performance of the proposed method on a public nuclei and gland segmentation data set, highlighting its utility in medical image segmentation applications.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"27 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preconditioned Score-Based Generative Models","authors":"Hengyuan Ma, Xiatian Zhu, Jianfeng Feng, Li Zhang","doi":"10.1007/s11263-025-02410-w","DOIUrl":"https://doi.org/10.1007/s11263-025-02410-w","url":null,"abstract":"<p>Score-based generative models (SGMs) have recently emerged as a promising class of generative models. However, a fundamental limitation is that their sampling process is slow due to a need for many (e.g., 2000) iterations of sequential computations. An intuitive acceleration method is to reduce the sampling iterations which however causes severe performance degradation. We assault this problem to the ill-conditioned issues of the Langevin dynamics and reverse diffusion in the sampling process. Under this insight, we propose a novel <b><i>preconditioned diffusion sampling</i></b> (PDS) method that leverages matrix preconditioning to alleviate the aforementioned problem. PDS alters the sampling process of a vanilla SGM at marginal extra computation cost and without model retraining. Theoretically, we prove that PDS preserves the output distribution of the SGM, with no risk of inducing systematical bias to the original sampling process. We further theoretically reveal a relation between the parameter of PDS and the sampling iterations, easing the parameter estimation under varying sampling iterations. Extensive experiments on various image datasets with a variety of resolutions and diversity validate that our PDS consistently accelerates off-the-shelf SGMs whilst maintaining the synthesis quality. In particular, PDS can accelerate by up to <span>(28times )</span> on more challenging high-resolution (1024<span>(times )</span>1024) image generation. Compared with the latest generative models (e.g., CLD-SGM, DDIM, and Analytic-DDIM), PDS can achieve the best sampling quality on CIFAR-10 at an FID score of 1.99. Our code is publicly available to foster any further research https://github.com/fudan-zvg/PDS.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"183 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT3D++: Improving 3D Object Detection with Keypoint-Induced Channel-wise Transformer","authors":"Hualian Sheng, Sijia Cai, Na Zhao, Bing Deng, Qiao Liang, Min-Jian Zhao, Jieping Ye","doi":"10.1007/s11263-025-02404-8","DOIUrl":"https://doi.org/10.1007/s11263-025-02404-8","url":null,"abstract":"<p>The field of 3D object detection from point clouds is rapidly advancing in computer vision, aiming to accurately and efficiently detect and localize objects in three-dimensional space. Current 3D detectors commonly fall short in terms of flexibility and scalability, with ample room for advancements in performance. In this paper, our objective is to address these limitations by introducing two frameworks for 3D object detection. Firstly, we propose CT3D, which sequentially performs raw-point-based embedding, a standard Transformer encoder, and a channel-wise decoder for point features within each proposal. Secondly, we present an enhanced network called CT3D++, which incorporates geometric and semantic fusion-based embedding to extract more valuable and comprehensive proposal-aware information. Additionally, CT3D++ utilizes a point-to-key bidirectional encoder for more efficient feature encoding with reduced computational cost. By replacing the corresponding components of CT3D with these novel modules, CT3D++ achieves state-of-the-art performance on both the KITTI dataset and the large-scale Waymo Open Dataset. The source code for our frameworks will be made accessible at https://github.com/hlsheng1/CT3Dplusplus.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"34 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143666253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LR-ASD: Lightweight and Robust Network for Active Speaker Detection","authors":"Junhua Liao, Haihan Duan, Kanghui Feng, Wanbing Zhao, Yanbing Yang, Liangyin Chen, Yanru Chen","doi":"10.1007/s11263-025-02399-2","DOIUrl":"https://doi.org/10.1007/s11263-025-02399-2","url":null,"abstract":"<p>Active speaker detection is a challenging task aimed at identifying who is speaking. Due to the critical importance of this task in numerous applications, it has received considerable attention. Existing studies endeavor to enhance performance at any cost by inputting information from multiple candidates and designing complex models. While these methods have achieved excellent performance, their substantial memory and computational demands pose challenges for their application to resource-limited scenarios. Therefore, in this study, a lightweight and robust network for active speaker detection, named LR-ASD, is constructed by reducing the number of input candidates, splitting 2D and 3D convolutions for audio-visual feature extraction, using a simple channel attention module for multi-modal feature fusion, and applying gated recurrent unit (GRU) with low computational complexity for temporal modeling. Results on the AVA-ActiveSpeaker dataset reveal that LR-ASD achieves competitive mean Average Precision (mAP) performance (94.5% vs. 95.2%), while the resource costs are significantly lower than the state-of-the-art method, particularly in terms of model parameters (0.84 M vs. 34.33 M, approximately 41 times) and floating point operations (FLOPs) (0.51 G vs. 4.86 G, approximately 10 times). Additionally, LR-ASD demonstrates excellent robustness by achieving state-of-the-art performance on the Talkies, Columbia, and RealVAD datasets in cross-dataset testing without fine-tuning. The project is available at https://github.com/Junhua-Liao/LR-ASD.\u0000</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"124 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PointSea: Point Cloud Completion via Self-structure Augmentation","authors":"Zhe Zhu, Honghua Chen, Xing He, Mingqiang Wei","doi":"10.1007/s11263-025-02400-y","DOIUrl":"https://doi.org/10.1007/s11263-025-02400-y","url":null,"abstract":"<p>Point cloud completion is a fundamental yet not well-solved problem in 3D vision. Current approaches often rely on 3D coordinate information and/or additional data (e.g., images and scanning viewpoints) to fill in missing parts. Unlike these methods, we explore self-structure augmentation and propose <b>PointSea</b> for global-to-local point cloud completion. In the global stage, consider how we inspect a defective region of a physical object, we may observe it from various perspectives for a better understanding. Inspired by this, PointSea augments data representation by leveraging self-projected depth images from multiple views. To reconstruct a compact global shape from the cross-modal input, we incorporate a feature fusion module to fuse features at both intra-view and inter-view levels. In the local stage, to reveal highly detailed structures, we introduce a point generator called the self-structure dual-generator. This generator integrates both learned shape priors and geometric self-similarities for shape refinement. Unlike existing efforts that apply a unified strategy for all points, our dual-path design adapts refinement strategies conditioned on the structural type of each point, addressing the specific incompleteness of each point. Comprehensive experiments on widely-used benchmarks demonstrate that PointSea effectively understands global shapes and generates local details from incomplete input, showing clear improvements over existing methods. Our code is available at https://github.com/czvvd/SVDFormer_PointSea.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"20 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143653346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning to Generalize Heterogeneous Representation for Cross-Modality Image Synthesis via Multiple Domain Interventions","authors":"Yawen Huang, Huimin Huang, Hao Zheng, Yuexiang Li, Feng Zheng, Xiantong Zhen, Yefeng Zheng","doi":"10.1007/s11263-025-02381-y","DOIUrl":"https://doi.org/10.1007/s11263-025-02381-y","url":null,"abstract":"<p>Magnetic resonance imaging with modality diversity substantially increases productivity in routine diagnosis and advanced research. However, high inter-equipment variability and expensive examination cost remain as key challenges in acquiring and utilizing multi-modal images. Missing modalities often can be synthesized from existing ones. While the rapid growth in image style transfer with deep models overwhelms the above endeavor, such image synthesis may not always be achievable and even impractical when applied to medical data. The proposed method addresses this issue by a convolutional sparse coding (CSC) adaptation network to handle the lacking of generalizing medical image representation learning. We reduce both inter-domain and intra-domain divergences by the domain-adaptation and domain-standardization modules, respectively. On the basis of CSC features, we penalize their subspace mismatching to reduce the generalization error. The overall framework is cast in a minimax setting, and the extensive experiments show that the proposed method yields state-of-the-art results on multiple datasets.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"25 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143653351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fully Decoupled End-to-End Person Search: An Approach without Conflicting Objectives","authors":"Pengcheng Zhang, Xiaohan Yu, Xiao Bai, Jin Zheng, Xin Ning, Edwin R. Hancock","doi":"10.1007/s11263-025-02407-5","DOIUrl":"https://doi.org/10.1007/s11263-025-02407-5","url":null,"abstract":"<p>End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection sub-task learns to identify all persons as one category while the re-identification (re-id) sub-task aims to discriminate persons of different identities, resulting in conflicting optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on the sub-tasks due to their partially decoupled models, which limits the overall person search performance. To further eliminate the last coupled part in decoupled models without sacrificing the efficiency of end-to-end person search, we propose a fully decoupled person search framework in this work. Specifically, we design a task-incremental network to construct an end-to-end model in a task-incremental learning procedure. Given that the detection subtask is easier, we start by training a lightweight detection sub-network and expand it with a re-id sub-network trained in another stage. On top of the fully decoupled design, we also enable one-stage training for the task-incremental network. The fully decoupled framework further allows an Online Representation Distillation to mitigate the representation gap between the end-to-end model and two-step models for learning robust representations. Without requiring an offline teacher re-id model, this transfers structured representational knowledge learned from cropped images to the person search model. The learned person representations thus focus more on discriminative clues of foreground persons and suppress the distractive background information. To understand the effectiveness and efficiency of the proposed method, we conduct comprehensive experimental evaluations on two popular person search datasets PRW and CUHK-SYSU. The experimental results demonstrate that the fully decoupled model achieves superior performance than previous decoupled methods. The inference of the model is also shown to be efficient among recent end-to-end methods. The source code is available at https://github.com/PatrickZad/fdps.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"33 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143653347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}