{"total":184,"rows":[{"id":"2204.02610","title":"Efficient Test-Time Model Adaptation without Forgetting","created":"2022-04-06","cited_by_count":469,"area":null},{"id":"2102.10274","title":"Concealed Object Detection","created":"2021-02-20","cited_by_count":212,"area":null},{"id":"2103.04011","title":"Simultaneously Localize, Segment and Rank the Camouflaged Objects","created":"2021-03-06","cited_by_count":198,"area":null},{"id":"2104.10475","title":"Camouflaged Object Segmentation with Distraction Mining","created":"2021-04-21","cited_by_count":151,"area":null},{"id":"2203.02688","title":"Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection","created":"2022-03-05","cited_by_count":145,"area":null},{"id":"2105.13381","title":"Recent advances and clinical applications of deep learning in medical image analysis","created":"2021-05-27","cited_by_count":104,"area":null},{"id":"2104.02613","title":"Mutual Graph Learning for Camouflaged Object Detection","created":"2021-04-03","cited_by_count":103,"area":null},{"id":"2304.04709","title":"Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection","created":"2023-04-10","cited_by_count":100,"area":null},{"id":"2203.11624","title":"High-resolution Iterative Feedback Network for Camouflaged Object Detection","created":"2022-03-22","cited_by_count":78,"area":null},{"id":"2304.06022","title":"SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anything","created":"2023-04-12","cited_by_count":75,"area":null},{"id":"2205.12853","title":"Deep Gradient Learning for Efficient Camouflaged Object Detection","created":"2022-05-25","cited_by_count":65,"area":null},{"id":"2310.20208","title":"ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection","created":"2023-10-31","cited_by_count":48,"area":null},{"id":"2304.11234","title":"Advances in Deep Concealed Scene Understanding","created":"2023-04-21","cited_by_count":47,"area":null},{"id":"2409.01686","title":"Frequency-Spatial Entanglement Learning for Camouflaged Object Detection","created":"2024-09-03","cited_by_count":46,"area":null},{"id":"2112.09645","title":"Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation","created":"2021-12-17","cited_by_count":43,"area":null},{"id":"2207.14083","title":"Weakly-Supervised Camouflaged Object Detection with Scribble Annotations","created":"2022-07-28","cited_by_count":43,"area":null},{"id":"2302.01735","title":"Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective","created":"2023-02-03","cited_by_count":43,"area":null},{"id":"2111.03216","title":"Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network","created":"2021-11-05","cited_by_count":42,"area":null},{"id":"2212.00990","title":"Feature Aggregation and Propagation Network for Camouflaged Object Detection","created":"2022-12-02","cited_by_count":40,"area":null},{"id":"2308.08924","title":"Frequency Perception Network for Camouflaged Object Detection","created":"2023-08-17","cited_by_count":37,"area":null},{"id":"2408.14562","title":"A Survey of Camouflaged Object Detection and Beyond","created":"2024-08-26","cited_by_count":37,"area":null},{"id":"2102.02996","title":"Deep Texture-Aware Features for Camouflaged Object Detection","created":"2021-02-05","cited_by_count":36,"area":null},{"id":"2307.03942","title":"Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images","created":"2023-07-08","cited_by_count":34,"area":null},{"id":"2207.13362","title":"Camouflaged Object Detection via Context-aware Cross-level Fusion","created":"2022-07-27","cited_by_count":32,"area":null},{"id":"2207.02255","title":"OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers","created":"2022-07-05","cited_by_count":30,"area":null},{"id":"2209.13476","title":"Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels","created":"2022-09-27","cited_by_count":30,"area":null},{"id":"2308.00303","title":"Diffusion Model for Camouflaged Object Detection","created":"2023-08-01","cited_by_count":27,"area":"diffusion"},{"id":"2405.06284","title":"Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention","created":"2024-05-10","cited_by_count":26,"area":null},{"id":"2205.10579","title":"Boosting Camouflaged Object Detection with Dual-Task Interactive Transformer","created":"2022-05-21","cited_by_count":24,"area":null},{"id":"2503.00361","title":"Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding","created":"2025-03-01","cited_by_count":24,"area":"mllm"},{"id":"2103.00429","title":"Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models","created":"2021-02-28","cited_by_count":22,"area":null},{"id":"2104.03225","title":"Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images","created":"2021-04-07","cited_by_count":22,"area":null},{"id":"2408.10760","title":"SAM-COD: SAM-guided Unified Framework for Weakly-Supervised Camouflaged Object Detection","created":"2024-08-20","cited_by_count":22,"area":null},{"id":"2305.17932","title":"CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models","created":"2023-05-29","cited_by_count":21,"area":"diffusion"},{"id":"2107.04537","title":"Modality specific U-Net variants for biomedical image segmentation: A survey","created":"2021-07-09","cited_by_count":20,"area":null},{"id":"2403.11491","title":"Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting","created":"2024-03-18","cited_by_count":20,"area":null},{"id":"2205.11333","title":"Towards Deeper Understanding of Camouflaged Object Detection","created":"2022-05-23","cited_by_count":19,"area":null},{"id":"2306.07532","title":"Referring Camouflaged Object Detection","created":"2023-06-13","cited_by_count":19,"area":null},{"id":"2208.08605","title":"Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures","created":"2022-08-18","cited_by_count":18,"area":null},{"id":"2405.01002","title":"Spider: A Unified Framework for Context-dependent Concept Segmentation","created":"2024-05-02","cited_by_count":18,"area":null},{"id":"2102.07762","title":"Membership Inference Attacks are Easier on Difficult Problems","created":"2021-02-15","cited_by_count":17,"area":"safety"},{"id":"2204.11433","title":"Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning","created":"2022-04-25","cited_by_count":16,"area":null},{"id":"2207.08114","title":"BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images","created":"2022-07-17","cited_by_count":16,"area":null},{"id":"2303.12484","title":"Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions","created":"2023-03-22","cited_by_count":16,"area":null},{"id":"2406.13583","title":"Low-Rank Mixture-of-Experts for Continual Medical Image Segmentation","created":"2024-06-19","cited_by_count":16,"area":"moe"},{"id":"2407.05416","title":"Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation","created":"2024-07-07","cited_by_count":16,"area":null},{"id":"2312.04964","title":"ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting","created":"2023-12-07","cited_by_count":15,"area":null},{"id":"2109.14172","title":"Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention","created":"2021-09-29","cited_by_count":14,"area":null},{"id":"2311.11241","title":"Open-Vocabulary Camouflaged Object Segmentation","created":"2023-11-19","cited_by_count":14,"area":"mllm"},{"id":"2404.08951","title":"Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation","created":"2024-04-13","cited_by_count":14,"area":null},{"id":"2202.06372","title":"A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron","created":"2022-02-13","cited_by_count":13,"area":null},{"id":"2212.01108","title":"Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis","created":"2022-12-02","cited_by_count":13,"area":null},{"id":"2303.10894","title":"M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation","created":"2023-03-20","cited_by_count":13,"area":null},{"id":"2102.07726","title":"Detection and severity classification of COVID-19 in CT images using deep learning","created":"2021-02-15","cited_by_count":12,"area":null},{"id":"2209.10963","title":"COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs","created":"2022-09-22","cited_by_count":12,"area":null},{"id":"2111.10989","title":"Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation","created":"2021-11-22","cited_by_count":11,"area":null},{"id":"2206.01793","title":"R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation","created":"2022-06-03","cited_by_count":11,"area":null},{"id":"2209.02934","title":"Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System","created":"2022-09-07","cited_by_count":11,"area":null},{"id":"2409.09588","title":"GLCONet: Learning Multi-source Perception Representation for Camouflaged Object Detection","created":"2024-09-15","cited_by_count":10,"area":null},{"id":"2204.13004","title":"Defending Person Detection Against Adversarial Patch Attack by using Universal Defensive Frame","created":"2022-04-27","cited_by_count":9,"area":null},{"id":"2208.06643","title":"Recent Progress in Transformer-based Medical Image Analysis","created":"2022-08-13","cited_by_count":9,"area":null},{"id":"2310.15098","title":"Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data","created":"2023-10-23","cited_by_count":9,"area":null},{"id":"2312.15182","title":"Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation","created":"2023-12-23","cited_by_count":9,"area":null},{"id":"2412.01240","title":"Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes","created":"2024-12-02","cited_by_count":9,"area":null},{"id":"2106.10230","title":"CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis","created":"2021-06-15","cited_by_count":8,"area":null},{"id":"2305.18830","title":"Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions","created":"2023-05-30","cited_by_count":8,"area":null},{"id":"2111.09537","title":"The Prominence of Artificial Intelligence in COVID-19","created":"2021-11-18","cited_by_count":7,"area":null},{"id":"2111.13330","title":"ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks","created":"2021-11-26","cited_by_count":7,"area":null},{"id":"2211.14559","title":"Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification","created":"2022-11-26","cited_by_count":7,"area":null},{"id":"2503.16997","title":"Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation","created":"2025-03-21","cited_by_count":7,"area":null},{"id":"2604.00684","title":"TP-Seg: Task-Prototype Framework for Unified Medical Lesion Segmentation","created":"2026-04-01","cited_by_count":7,"area":null},{"id":"2102.11099","title":"RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detection","created":"2021-02-22","cited_by_count":6,"area":null},{"id":"2210.15156","title":"Towards Complex Backgrounds: A Unified Difference-Aware Decoder for Binary Segmentation","created":"2022-10-27","cited_by_count":6,"area":null},{"id":"2212.08296","title":"DQnet: Cross-Model Detail Querying for Camouflaged Object Detection","created":"2022-12-16","cited_by_count":6,"area":null},{"id":"2310.06557","title":"Data efficient deep learning for medical image analysis: A survey","created":"2023-10-10","cited_by_count":6,"area":null},{"id":"2312.16039","title":"Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation","created":"2023-12-26","cited_by_count":6,"area":null},{"id":"2504.01561","title":"STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation","created":"2025-04-02","cited_by_count":6,"area":null},{"id":"2512.01922","title":"Med-VCD: Mitigating Hallucination for Medical Large Vision Language Models through Visual Contrastive Decoding","created":"2025-12-01","cited_by_count":6,"area":"mllm"},{"id":"2606.08906","title":"DifferSeg: Towards Diverse Multimodal Binary Segmentation via Differential Perception and Frequency Guidance","created":"2026-06-08","cited_by_count":6,"area":null},{"id":"2103.04235","title":"Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation","created":"2021-03-07","cited_by_count":5,"area":null},{"id":"2110.14910","title":"Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review","created":"2021-10-28","cited_by_count":5,"area":null},{"id":"2202.07422","title":"Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels","created":"2022-02-11","cited_by_count":5,"area":null},{"id":"2204.02839","title":"CCAT-NET: A Novel Transformer Based Semi-supervised Framework for Covid-19 Lung Lesion Segmentation","created":"2022-04-06","cited_by_count":5,"area":null},{"id":"2207.00973","title":"Trichomonas Vaginalis Segmentation in Microscope Images","created":"2022-07-03","cited_by_count":5,"area":null},{"id":"2101.00691","title":"CovTANet: A Hybrid Tri-level Attention Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans","created":"2021-01-03","cited_by_count":4,"area":null},{"id":"2102.00596","title":"Few-shot Learning for CT Scan based COVID-19 Diagnosis","created":"2021-02-01","cited_by_count":4,"area":null},{"id":"2102.05210","title":"D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism","created":"2021-02-10","cited_by_count":4,"area":null},{"id":"2103.07240","title":"Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs","created":"2021-03-12","cited_by_count":4,"area":null},{"id":"2104.09699","title":"Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images","created":"2021-04-20","cited_by_count":4,"area":null},{"id":"2105.11748","title":"Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients","created":"2021-05-25","cited_by_count":4,"area":null},{"id":"2211.05548","title":"Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19","created":"2022-11-10","cited_by_count":4,"area":null},{"id":"2302.02619","title":"COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images","created":"2023-02-06","cited_by_count":4,"area":null},{"id":"2302.14277","title":"DECOR-NET: A COVID-19 Lung Infection Segmentation Network Improved by Emphasizing Low-level Features and Decorrelating Features","created":"2023-02-28","cited_by_count":4,"area":null},{"id":"2309.10523","title":"Edge-aware Feature Aggregation Network for Polyp Segmentation","created":"2023-09-19","cited_by_count":4,"area":null},{"id":"2405.02824","title":"Adaptive Guidance Learning for Camouflaged Object Detection","created":"2024-05-05","cited_by_count":4,"area":null},{"id":"2104.02042","title":"Automated lung segmentation from CT images of normal and COVID-19 pneumonia patients","created":"2021-04-05","cited_by_count":3,"area":null},{"id":"2201.00317","title":"Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation","created":"2022-01-02","cited_by_count":3,"area":null},{"id":"2201.03050","title":"Lung infection and normal region segmentation from CT volumes of COVID-19 cases","created":"2022-01-09","cited_by_count":3,"area":null},{"id":"2401.10129","title":"Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study","created":"2024-01-18","cited_by_count":3,"area":null},{"id":"2402.03456","title":"Constrained Multiview Representation for Self-supervised Contrastive Learning","created":"2024-02-05","cited_by_count":3,"area":null}],"next_cursor":"100"}