{"list":"other","total":53,"rows":[{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2402.03300","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","created":"2024-02-05","cited_by_count":9379},{"id":"2304.02643","title":"Segment Anything","created":"2023-04-05","cited_by_count":8234},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"id":"2304.08485","title":"Visual Instruction Tuning","created":"2023-04-17","cited_by_count":7869},{"id":"2309.16609","title":"Qwen Technical Report","created":"2023-09-28","cited_by_count":3344},{"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":"2408.00714","title":"SAM 2: Segment Anything in Images and Videos","created":"2024-08-01","cited_by_count":2863},{"id":"1505.00468","title":"VQA: Visual Question Answering","created":"2015-05-03","cited_by_count":2506},{"id":"1306.0895","title":"Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances","created":"2013-06-04","cited_by_count":2316},{"id":"2203.03605","title":"DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection","created":"2022-03-07","cited_by_count":1319},{"id":"2112.03857","title":"Grounded Language-Image Pre-training","created":"2021-12-07","cited_by_count":1156},{"id":"2308.00692","title":"LISA: Reasoning Segmentation via Large Language Model","created":"2023-08-01","cited_by_count":776},{"id":"2401.12168","title":"SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities","created":"2024-01-22","cited_by_count":706},{"id":"1809.05231","title":"VoxelMorph: A Learning Framework for Deformable Medical Image Registration","created":"2018-09-14","cited_by_count":662},{"id":"1707.03237","title":"Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations","created":"2017-07-11","cited_by_count":620},{"id":"2208.05868","title":"TotalSegmentator: robust segmentation of 104 anatomical structures in CT images","created":"2022-08-11","cited_by_count":613},{"id":"2210.10163","title":"MedCLIP: Contrastive Learning from Unpaired Medical Images and Text","created":"2022-10-18","cited_by_count":570},{"id":"2304.06718","title":"Segment Everything Everywhere All at Once","created":"2023-04-13","cited_by_count":548},{"id":"2204.03162","title":"Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality","created":"2022-04-07","cited_by_count":529},{"id":"2307.15189","title":"Med-Flamingo: a Multimodal Medical Few-shot Learner","created":"2023-07-27","cited_by_count":392},{"id":"2402.00253","title":"A Survey on Hallucination in Large Vision-Language Models","created":"2024-02-01","cited_by_count":357},{"id":"1908.06948","title":"Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography","created":"2019-08-16","cited_by_count":244},{"id":"2306.14610","title":"SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality","created":"2023-06-26","cited_by_count":243},{"id":"2503.06520","title":"Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement","created":"2025-03-09","cited_by_count":239},{"id":"2310.05492","title":"How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition","created":"2023-10-09","cited_by_count":223},{"id":"2006.14822","title":"A survey of loss functions for semantic segmentation","created":"2020-06-26","cited_by_count":217},{"id":"2411.16537","title":"RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics","created":"2024-11-25","cited_by_count":136},{"id":"2403.06801","title":"CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging","created":"2024-03-11","cited_by_count":96},{"id":"2504.03600","title":"MedSAM2: Segment Anything in 3D Medical Images and Videos","created":"2025-04-04","cited_by_count":96},{"id":"2406.20076","title":"EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model","created":"2024-06-28","cited_by_count":65},{"id":"2305.08252","title":"Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity","created":"2023-05-14","cited_by_count":56},{"id":"2306.08666","title":"Radiology-GPT: A Large Language Model for Radiology","created":"2023-06-14","cited_by_count":44},{"id":"2310.11475","title":"Tracking and Mapping in Medical Computer Vision: A Review","created":"2023-10-17","cited_by_count":36},{"id":"2310.14230","title":"A comprehensive survey on deep active learning in medical image analysis","created":"2023-10-22","cited_by_count":35},{"id":"2304.09138","title":"Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task","created":"2023-04-18","cited_by_count":32},{"id":"2304.04920","title":"Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT","created":"2023-04-11","cited_by_count":28},{"id":"2301.01208","title":"Mask Matching Transformer for Few-Shot Segmentation","created":"2022-12-05","cited_by_count":22},{"id":"2410.20327","title":"R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interest","created":"2024-10-27","cited_by_count":21},{"id":"2504.20648","title":"SpaRE: Enhancing Spatial Reasoning in Vision-Language Models with Synthetic Data","created":"2025-04-29","cited_by_count":21},{"id":"2408.08813","title":"Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models","created":"2024-08-16","cited_by_count":20},{"id":"2406.13674","title":"Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases","created":"2024-06-19","cited_by_count":19},{"id":"2401.12215","title":"Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models","created":"2024-01-22","cited_by_count":18},{"id":"2303.12484","title":"Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions","created":"2023-03-22","cited_by_count":16},{"id":"2506.01371","title":"SVQA-R1: Reinforcing Spatial Reasoning in MLLMs via View-Consistent Reward Optimization","created":"2025-06-02","cited_by_count":16},{"id":"2311.18260","title":"Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation","created":"2023-11-30","cited_by_count":12},{"id":"2410.03143","title":"ECHOPulse: ECG controlled echocardio-grams video generation","created":"2024-10-04","cited_by_count":9},{"id":"2506.00721","title":"Common Inpainted Objects In-N-Out of Context","created":"2025-05-31","cited_by_count":8},{"id":"2506.09095","title":"Foundation Models in Medical Imaging: A Review and Outlook","created":"2025-06-10","cited_by_count":8},{"id":"2507.03698","title":"SAMed-2: Selective Memory Enhanced Medical Segment Anything Model","created":"2025-07-04","cited_by_count":8},{"id":"2406.10712","title":"Object Detection using Oriented Window Learning Vi-sion Transformer: Roadway Assets Recognition","created":"2024-06-15","cited_by_count":6},{"id":"2506.00698","title":"Concept-Centric Token Interpretation for Vector-Quantized Generative Models","created":"2025-05-31","cited_by_count":4},{"id":"2402.15894","title":"Multi-graph Graph Matching for Coronary Artery Semantic Labeling","created":"2024-02-24","cited_by_count":2}],"next_cursor":null}