{"list":"other","total":56,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"2304.08485","title":"Visual Instruction Tuning","created":"2023-04-17","cited_by_count":7869},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"2301.12597","title":"BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models","created":"2023-01-30","cited_by_count":6137},{"id":"2204.14198","title":"Flamingo: a Visual Language Model for Few-Shot Learning","created":"2022-04-29","cited_by_count":4386},{"id":"2201.12086","title":"BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation","created":"2022-01-28","cited_by_count":4322},{"id":"2303.05499","title":"Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection","created":"2023-03-09","cited_by_count":3332},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"2304.10592","title":"MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models","created":"2023-04-20","cited_by_count":2711},{"id":"1607.02533","title":"Adversarial examples in the physical world","created":"2016-07-08","cited_by_count":1769},{"id":"2306.13549","title":"A Survey on Multimodal Large Language Models","created":"2023-06-23","cited_by_count":909},{"id":"2303.11381","title":"MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action","created":"2023-03-20","cited_by_count":517},{"id":"2305.16934","title":"On Evaluating Adversarial Robustness of Large Vision-Language Models","created":"2023-05-26","cited_by_count":284},{"id":"2309.16020","title":"GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization","created":"2023-09-27","cited_by_count":187},{"id":"2309.11751","title":"How Robust is Google's Bard to Adversarial Image Attacks?","created":"2023-09-21","cited_by_count":183},{"id":"2204.02287","title":"Rethinking Visual Geo-localization for Large-Scale Applications","created":"2022-04-05","cited_by_count":163},{"id":"2206.09391","title":"Towards Adversarial Attack on Vision-Language Pre-training Models","created":"2022-06-19","cited_by_count":144},{"id":"2401.06805","title":"Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning","created":"2024-01-10","cited_by_count":136},{"id":"2504.01990","title":"Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems","created":"2025-03-31","cited_by_count":126},{"id":"2303.15433","title":"Anti-DreamBooth: Protecting users from personalized text-to-image synthesis","created":"2023-03-27","cited_by_count":120},{"id":"2307.14061","title":"Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models","created":"2023-07-26","cited_by_count":108},{"id":"2303.09105","title":"Rethinking Model Ensemble in Transfer-based Adversarial Attacks","created":"2023-03-16","cited_by_count":89},{"id":"2201.08970","title":"Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection","created":"2022-01-22","cited_by_count":79},{"id":"2411.17558","title":"Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey","created":"2024-11-26","cited_by_count":76},{"id":"2310.04655","title":"VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models","created":"2023-10-07","cited_by_count":68},{"id":"2008.00217","title":"Efficient Adversarial Attacks for Visual Object Tracking","created":"2020-08-01","cited_by_count":66},{"id":"2302.09491","title":"X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection","created":"2023-02-19","cited_by_count":59},{"id":"2008.01919","title":"Adv-watermark: A Novel Watermark Perturbation for Adversarial Examples","created":"2020-08-05","cited_by_count":56},{"id":"2307.05845","title":"PIGEON: Predicting Image Geolocations","created":"2023-07-11","cited_by_count":56},{"id":"2209.07790","title":"A Large-scale Multiple-objective Method for Black-box Attack against Object Detection","created":"2022-09-16","cited_by_count":55},{"id":"2303.04249","title":"Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes","created":"2023-03-07","cited_by_count":55},{"id":"2410.05346","title":"AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models","created":"2024-10-07","cited_by_count":46},{"id":"2505.21494","title":"Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment","created":"2025-05-27","cited_by_count":43},{"id":"2404.10335","title":"Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models","created":"2024-04-16","cited_by_count":38},{"id":"2503.10635","title":"A Frustratingly Simple Yet Highly Effective Attack Baseline: Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1","created":"2025-03-13","cited_by_count":37},{"id":"2403.12445","title":"Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory","created":"2024-03-19","cited_by_count":36},{"id":"2104.14995","title":"Interpretable Semantic Photo Geolocation","created":"2021-04-30","cited_by_count":32},{"id":"2209.03716","title":"Enhancing the Self-Universality for Transferable Targeted Attacks","created":"2022-09-08","cited_by_count":30},{"id":"2501.13563","title":"Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving","created":"2025-01-23","cited_by_count":24},{"id":"2307.06166","title":"Can Vision-Language Models be a Good Guesser? Exploring VLMs for Times and Location Reasoning","created":"2023-07-12","cited_by_count":22},{"id":"2405.20363","title":"LLMGeo: Benchmarking Large Language Models on Image Geolocation In-the-wild","created":"2024-05-30","cited_by_count":21},{"id":"2407.04952","title":"Granular Privacy Control for Geolocation with Vision Language Models","created":"2024-07-06","cited_by_count":21},{"id":"2207.08178","title":"Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal","created":"2022-07-17","cited_by_count":20},{"id":"2411.02669","title":"Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack","created":"2024-11-04","cited_by_count":18},{"id":"2503.14604","title":"Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future Perspectives","created":"2025-03-18","cited_by_count":18},{"id":"2504.19373","title":"Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Models","created":"2025-04-27","cited_by_count":14},{"id":"2311.13018","title":"GeoLocator: a location-integrated large multimodal model for inferring geo-privacy","created":"2023-11-21","cited_by_count":13},{"id":"2502.14412","title":"Evaluating Precise Geolocation Inference Capabilities of Vision Language Models","created":"2025-02-20","cited_by_count":12},{"id":"2308.00958","title":"Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks","created":"2023-08-02","cited_by_count":10},{"id":"1810.03077","title":"DeepGeo: Photo Localization with Deep Neural Network","created":"2018-10-07","cited_by_count":8},{"id":"2502.14638","title":"NAVIG: Natural Language-guided Analysis with Vision Language Models for Image Geo-localization","created":"2025-02-20","cited_by_count":8},{"id":"2308.05983","title":"Face Encryption via Frequency-Restricted Identity-Agnostic Attacks","created":"2023-08-11","cited_by_count":6},{"id":"2412.11471","title":"TrapFlow: Controllable Website Fingerprinting Defense via Dynamic Backdoor Learning","created":"2024-12-16","cited_by_count":5},{"id":"2412.11487","title":"WFCAT: Augmenting Website Fingerprinting with Channel-wise Attention on Timing Features","created":"2024-12-16","cited_by_count":3},{"id":"2206.14020","title":"Rethinking Adversarial Examples for Location Privacy Protection","created":"2022-06-28","cited_by_count":2}],"next_cursor":null}