International Journal of Computer Vision最新文献_第5页

Informative Scene Graph Generation via Debiasing

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2025-02-24 DOI: 10.1007/s11263-025-02365-y

Lianli Gao, Xinyu Lyu, Yuyu Guo, Yuxuan Hu, Yuan-Fang Li, Lu Xu, Heng Tao Shen, Jingkuan Song

{"title":"Informative Scene Graph Generation via Debiasing","authors":"Lianli Gao, Xinyu Lyu, Yuyu Guo, Yuxuan Hu, Yuan-Fang Li, Lu Xu, Heng Tao Shen, Jingkuan Song","doi":"10.1007/s11263-025-02365-y","DOIUrl":"https://doi.org/10.1007/s11263-025-02365-y","url":null,"abstract":"Scene graph generation aims to detect visual relationship triplets, (subject, predicate, object). Due to biases in data, current models tend to predict common predicates, e.g., “on” and “at”, instead of informative ones, e.g., “standing on” and “looking at”. This tendency results in the loss of precise information and overall performance. If a model only uses “stone on road” rather than “stone blocking road” to describe an image, it may be a grave misunderstanding. We argue that this phenomenon is caused by two imbalances: semantic space level imbalance and training sample level imbalance. For this problem, we propose DB-SGG, an effective framework based on debiasing but not the conventional distribution fitting. It integrates two components: Semantic Debiasing (SD) and Balanced Predicate Learning (BPL), for these imbalances. SD utilizes a confusion matrix and a bipartite graph to construct predicate relationships. BPL adopts a random undersampling strategy and an ambiguity removing strategy to focus on informative predicates. Benefiting from the model-agnostic process, our method can be easily applied to SGG models and outperforms Transformer by (136.3%), (119.5%), and (122.6%) on mR@20 at three SGG sub-tasks on the SGG-VG dataset. Our method is further verified on another complex SGG dataset (SGG-GQA) and two downstream tasks (sentence-to-graph retrieval and image captioning).","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"3 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485948","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}

引用次数: 0

DustNet++: Deep Learning-Based Visual Regression for Dust Density Estimation

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2025-02-24 DOI: 10.1007/s11263-025-02376-9

Andreas Michel, Martin Weinmann, Jannick Kuester, Faisal AlNasser, Tomas Gomez, Mark Falvey, Rainer Schmitz, Wolfgang Middelmann, Stefan Hinz

{"title":"DustNet++: Deep Learning-Based Visual Regression for Dust Density Estimation","authors":"Andreas Michel, Martin Weinmann, Jannick Kuester, Faisal AlNasser, Tomas Gomez, Mark Falvey, Rainer Schmitz, Wolfgang Middelmann, Stefan Hinz","doi":"10.1007/s11263-025-02376-9","DOIUrl":"https://doi.org/10.1007/s11263-025-02376-9","url":null,"abstract":"Detecting airborne dust in standard RGB images presents significant challenges. Nevertheless, the monitoring of airborne dust holds substantial potential benefits for climate protection, environmentally sustainable construction, scientific research, and various other fields. To develop an efficient and robust algorithm for airborne dust monitoring, several hurdles have to be addressed. Airborne dust can be opaque or translucent, exhibit considerable variation in density, and possess indistinct boundaries. Moreover, distinguishing dust from other atmospheric phenomena, such as fog or clouds, can be particularly challenging. To meet the demand for a high-performing and reliable method for monitoring airborne dust, we introduce DustNet++, a neural network designed for dust density estimation. DustNet++ leverages feature maps from multiple resolution scales and semantic levels through window and grid attention mechanisms to maintain a sparse, globally effective receptive field with linear complexity. To validate our approach, we benchmark the performance of DustNet++ against existing methods from the domains of crowd counting and monocular depth estimation using the Meteodata airborne dust dataset and the URDE binary dust segmentation dataset. Our findings demonstrate that DustNet++ surpasses comparative methodologies in terms of regression and localization capabilities.","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"56 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477343","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}

引用次数: 0

Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2025-02-22 DOI: 10.1007/s11263-025-02363-0

Shuang Cui, Yi Li, Jiangmeng Li, Xiongxin Tang, Bing Su, Fanjiang Xu, Hui Xiong

{"title":"Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks","authors":"Shuang Cui, Yi Li, Jiangmeng Li, Xiongxin Tang, Bing Su, Fanjiang Xu, Hui Xiong","doi":"10.1007/s11263-025-02363-0","DOIUrl":"https://doi.org/10.1007/s11263-025-02363-0","url":null,"abstract":"Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions. Empirical evidence supports this finding, motivating us to employ a continual test-time adaptation (CTTA) paradigm for SIDD. However, traditional CTTA methods, which primarily rely on entropy minimization, cannot sufficiently explore task-dependent information for pixel-level regression tasks like SIDD. To address this issue, we propose a novel Siamese networks-based continual test-time adaptation framework, which adapts source models to continuously changing target domains only requiring unlabeled target data in an online manner. To further mitigate semantically erroneous textures introduced by source SIDD models under severe degradation, we revisit the learning paradigm through a structural causal model and propose Causal Siamese networks (CauSiam). Our method leverages large-scale pre-trained vision-language models to derive discriminative universal semantic priors and integrates these priors into Siamese networks, ensuring causal identifiability between blurry inputs and restored images. Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains.\u0000","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"61 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473594","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}

引用次数: 0

Deep Convolutional Neural Network Enhanced Non-uniform Fast Fourier Transform for Undersampled MRI Reconstruction

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2025-02-22 DOI: 10.1007/s11263-025-02378-7

Yuze Li, Haikun Qi, Zhangxuan Hu, Haozhong Sun, Guangqi Li, Zhe Zhang, Yilong Liu, Hua Guo, Huijun Chen

{"title":"Deep Convolutional Neural Network Enhanced Non-uniform Fast Fourier Transform for Undersampled MRI Reconstruction","authors":"Yuze Li, Haikun Qi, Zhangxuan Hu, Haozhong Sun, Guangqi Li, Zhe Zhang, Yilong Liu, Hua Guo, Huijun Chen","doi":"10.1007/s11263-025-02378-7","DOIUrl":"https://doi.org/10.1007/s11263-025-02378-7","url":null,"abstract":"NUFFT is widely used in MRI reconstruction, offering a balance of efficiency and accuracy. However, it struggles with uneven or sparse sampling, leading to unacceptable under sampling errors. To address this, we introduced DCNUFFT, a novel method that enhances NUFFT with deep convolutional neural network. The interpolation kernel and density compensation in inverse NUFFT were replaced with trainable neural network layers and incorporated a new global correlation prior in the spatial-frequency domain to better recover high-frequency information, enhancing reconstruction quality. DCNUFFT outperformed inverse NUFFT, iterative methods, and other deep learning approaches in terms of normalized root mean square error (NRMSE) and structural similarity index (SSIM) across various anatomies and sampling trajectories. Importantly, DCNUFFT also excelled in reconstructing under sampled PET and CT data, showing strong generalization capabilities. In subjective evaluations by radiologists, DCNUFFT scored highest in visual quality (VQ) and lesion distinguishing ability (LD), highlighting its clinical potential.","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"23 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473595","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}

引用次数: 0

Image Matting and 3D Reconstruction in One Loop

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2025-02-21 DOI: 10.1007/s11263-024-02341-y

Xinshuang Liu, Siqi Li, Yue Gao

{"title":"Image Matting and 3D Reconstruction in One Loop","authors":"Xinshuang Liu, Siqi Li, Yue Gao","doi":"10.1007/s11263-024-02341-y","DOIUrl":"https://doi.org/10.1007/s11263-024-02341-y","url":null,"abstract":"Recent 3D object reconstruction methods rely on user-input alpha mattes to remove the background and reconstruct the object, because automatically predicted alpha mattes are not accurate enough. To realize automatic 3D object reconstruction, we propose a Joint framework for image Matting and 3D object Reconstruction (JointMR). It iteratively integrates information from all images into object hint maps to help image matting models predict better alpha mattes for each image and, in turn, improves 3D object reconstruction performance. The convergence of our framework is theoretically guaranteed. We further propose a method to convert an arbitrary image matting model into its hint-based counterpart. We conduct experiments on 3D object reconstruction from multi-view images and 3D dynamic object reconstruction from monocular videos. Different combinations of 3D object reconstruction models and image matting models are also tested. Experimental results show that our framework only slightly increases the computation cost but significantly improves the performance of all model combinations, demonstrating its compatibility and efficiency. Our code, models, and data are available at https://github.com/XinshuangL/JointMR.","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"50 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462497","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}

引用次数: 0

Bootstrapping Vision-Language Models for Frequency-Centric Self-Supervised Remote Physiological Measurement

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2025-02-21 DOI: 10.1007/s11263-025-02388-5

Zijie Yue, Miaojing Shi, Hanli Wang, Shuai Ding, Qijun Chen, Shanlin Yang

{"title":"Bootstrapping Vision-Language Models for Frequency-Centric Self-Supervised Remote Physiological Measurement","authors":"Zijie Yue, Miaojing Shi, Hanli Wang, Shuai Ding, Qijun Chen, Shanlin Yang","doi":"10.1007/s11263-025-02388-5","DOIUrl":"https://doi.org/10.1007/s11263-025-02388-5","url":null,"abstract":"Facial video-based remote physiological measurement is a promising research area for detecting human vital signs (e.g., heart rate, respiration frequency) in a non-contact way. Conventional approaches are mostly supervised learning, requiring extensive collections of facial videos and synchronously recorded photoplethysmography (PPG) signals. To tackle it, self-supervised learning has recently gained attentions; due to the lack of ground truth PPG signals, its performance is however limited. In this paper, we propose a novel frequency-centric self-supervised framework that successfully integrates the popular vision-language models (VLMs) into the remote physiological measurement task. Given a facial video, we first augment its positive and negative video samples with varying rPPG signal frequencies. Next, we introduce a frequency-oriented vision-text pair generation method by carefully creating contrastive spatio-temporal maps from positive and negative samples and designing proper text prompts to describe their relative ratios of signal frequencies. A pre-trained VLM is employed to extract features for these formed vision-text pairs and estimate rPPG signals thereafter. We develop a series of frequency-related generative and contrastive learning mechanisms to optimize the VLM, including the text-guided visual reconstruction task, the vision-text contrastive learning task, and the frequency contrastive and ranking task. Overall, our method for the first time adapts VLMs to digest and align the frequency-related knowledge in vision and text modalities. Extensive experiments on four benchmark datasets demonstrate that it significantly outperforms state of the art self-supervised methods. Our codes will be available at https://github.com/yuezijie/Bootstrapping-VLM-for-Frequency-centric-Self-supervised-Remote-Physiological-Measurement.","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"22 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462498","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}

引用次数: 0

Imbuing, Enrichment and Calibration: Leveraging Language for Unseen Domain Extension

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2025-02-20 DOI: 10.1007/s11263-025-02382-x

Chenyi Jiang, Jianqin Zhao, Jingjing Deng, Zechao Li, Haofeng Zhang

{"title":"Imbuing, Enrichment and Calibration: Leveraging Language for Unseen Domain Extension","authors":"Chenyi Jiang, Jianqin Zhao, Jingjing Deng, Zechao Li, Haofeng Zhang","doi":"10.1007/s11263-025-02382-x","DOIUrl":"https://doi.org/10.1007/s11263-025-02382-x","url":null,"abstract":"The incorporation of language to enable model extension into unseen domains has gained significant interest in recent years. Previous methods commonly utilize semantically guided distributional shifts in training features to achieve this. Nevertheless, the intrinsic modal disparities between language and pixel-level images frequently result in a divergence within the feature manifold when employing semantic guidelines to augment features. This paper presents the IMbuing, Enrichment, and Calibration (IMEC) strategy as a concise solution for these issues. Unlike previous approaches, IMEC reverses the target domain style mining process to ensure the retention of semantic content within a more structured framework. Guided by global semantics, we conditionally generate style vectors for imbuing into visual features. After which IMEC introduces minor perturbations to disperse these vectors using local semantics and selectively calibrates semantic content in features through a dimensional activation strategy. IMEC integrates semantic abstract knowledge with detail image content, bridging the gap between synthetic and real samples in the target domain and mitigating content collapse resulting from semantic-visual disparities. Our model is evaluated on semantic segmentation, object detection, and image classification tasks across challenging datasets, demonstrating superior performance over existing methods in both the target and source domains. The code for IMEC is available at https://github.com/LanchJL/IMEC-ZSDE.","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"65 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462495","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}

引用次数: 0

Consistent Prompt Tuning for Generalized Category Discovery

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2025-02-20 DOI: 10.1007/s11263-024-02343-w

Muli Yang, Jie Yin, Yanan Gu, Cheng Deng, Hanwang Zhang, Hongyuan Zhu

{"title":"Consistent Prompt Tuning for Generalized Category Discovery","authors":"Muli Yang, Jie Yin, Yanan Gu, Cheng Deng, Hanwang Zhang, Hongyuan Zhu","doi":"10.1007/s11263-024-02343-w","DOIUrl":"https://doi.org/10.1007/s11263-024-02343-w","url":null,"abstract":"Generalized Category Discovery (GCD) aims at discovering both known and unknown classes in unlabeled data, using the knowledge learned from a limited set of labeled data. Despite today’s foundation models being trained with Internet-scale multi-modal corpus, we find that they still struggle in GCD due to the ambiguity in class definitions. In this paper, we present Consistent Prompt Tuning (CPT) to disambiguate the classes for large vision-language models (e.g., CLIP). To this end, CPT learns a set of “task + class” prompts for labeled and unlabeled data of both known and unknown classes, with the “task” tokens globally shared across classes, which contain a unified class definition pattern, e.g., “the foreground is an animal named” or “the background scene is”. These prompts are optimized with two efficient regularization techniques that encourage consistent global and local relationships between any two matched inputs. CPT is evaluated on various existing GCD benchmarks, as well as in new practical scenarios with fewer annotations and customized class definitions, demonstrating clear superiority and broad versatility over existing state-of-the-art methods.","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"22 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462496","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}

引用次数: 0

Instance-Level Moving Object Segmentation from a Single Image with Events

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2025-02-20 DOI: 10.1007/s11263-025-02380-z

Zhexiong Wan, Bin Fan, Le Hui, Yuchao Dai, Gim Hee Lee

{"title":"Instance-Level Moving Object Segmentation from a Single Image with Events","authors":"Zhexiong Wan, Bin Fan, Le Hui, Yuchao Dai, Gim Hee Lee","doi":"10.1007/s11263-025-02380-z","DOIUrl":"https://doi.org/10.1007/s11263-025-02380-z","url":null,"abstract":"Moving object segmentation plays a crucial role in understanding dynamic scenes involving multiple moving objects, while the difficulties lie in taking into account both spatial texture structures and temporal motion cues. Existing methods based on video frames encounter difficulties in distinguishing whether pixel displacements of an object are caused by camera motion or object motion due to the complexities of accurate image-based motion modeling. Recent advances exploit the motion sensitivity of novel event cameras to counter conventional images’ inadequate motion modeling capabilities, but instead lead to challenges in segmenting pixel-level object masks due to the lack of dense texture structures in events. To address these two limitations imposed by unimodal settings, we propose the first instance-level moving object segmentation framework that integrates complementary texture and motion cues. Our model incorporates implicit cross-modal masked attention augmentation, explicit contrastive feature learning, and flow-guided motion enhancement to exploit dense texture information from a single image and rich motion information from events, respectively. By leveraging the augmented texture and motion features, we separate mask segmentation from motion classification to handle varying numbers of independently moving objects. Through extensive evaluations on multiple datasets, as well as ablation experiments with different input settings and real-time efficiency analysis of the proposed framework, we believe that our first attempt to incorporate image and event data for practical deployment can provide new insights for future work in event-based motion related works. The source code with model training and pre-trained weights is released at https://npucvr.github.io/EvInsMOS.","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"2 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451612","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}

引用次数: 0

VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2025-02-19 DOI: 10.1007/s11263-025-02368-9

Jiawei Liang, Siyuan Liang, Aishan Liu, Xiaochun Cao

{"title":"VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models","authors":"Jiawei Liang, Siyuan Liang, Aishan Liu, Xiaochun Cao","doi":"10.1007/s11263-025-02368-9","DOIUrl":"https://doi.org/10.1007/s11263-025-02368-9","url":null,"abstract":"Autoregressive Visual Language Models (VLMs) demonstrate remarkable few-shot learning capabilities within a multimodal context. Recently, multimodal instruction tuning has emerged as a technique to further refine instruction-following abilities. However, we uncover the potential threat posed by backdoor attacks on autoregressive VLMs during instruction tuning. Adversaries can implant a backdoor by inserting poisoned samples with triggers embedded in instructions or images to datasets, enabling malicious manipulation of the victim model’s predictions with predefined triggers. However, the frozen visual encoder in autoregressive VLMs imposes constraints on learning conventional image triggers. Additionally, adversaries may lack access to the parameters and architectures of the victim model. To overcome these challenges, we introduce a multimodal instruction backdoor attack, namely VL-Trojan. Our approach facilitates image trigger learning through active reshaping of poisoned features and enhances black-box attack efficacy through an iterative character-level text trigger generation method. Our attack successfully induces target output during inference, significantly outperforming baselines (+15.68%) in ASR. Furthermore, our attack demonstrates robustness across various model scales, architectures and few-shot in-context reasoning scenarios. Our codes are available at https://github.com/JWLiang007/VL-Trojan.","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"49 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443340","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}

引用次数: 0