{"list":"other","total":56,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"id":"1807.03748","title":"Representation Learning with Contrastive Predictive Coding","created":"2018-07-10","cited_by_count":7777},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"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":"1701.06538","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","created":"2017-01-23","cited_by_count":3509},{"id":"2101.03961","title":"Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity","created":"2021-01-11","cited_by_count":3073},{"id":"2406.09246","title":"OpenVLA: An Open-Source Vision-Language-Action Model","created":"2024-06-13","cited_by_count":2887},{"id":"2401.04088","title":"Mixtral of Experts","created":"2024-01-08","cited_by_count":1823},{"id":"2203.12119","title":"Visual Prompt Tuning","created":"2022-03-23","cited_by_count":1681},{"id":"1908.07490","title":"LXMERT: Learning Cross-Modality Encoder Representations from Transformers","created":"2019-08-20","cited_by_count":1553},{"id":"2012.07421","title":"WILDS: A Benchmark of in-the-Wild Distribution Shifts","created":"2020-12-14","cited_by_count":1375},{"id":"2205.01917","title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","created":"2022-05-04","cited_by_count":1274},{"id":"2001.06782","title":"Gradient Surgery for Multi-Task Learning","created":"2020-01-19","cited_by_count":1256},{"id":"1802.04712","title":"Attention-based Deep Multiple Instance Learning","created":"2018-02-13","cited_by_count":1095},{"id":"1806.03962","title":"Rotation Equivariant CNNs for Digital Pathology","created":"2018-06-08","cited_by_count":422},{"id":"2205.14865","title":"Prompt-aligned Gradient for Prompt Tuning","created":"2022-05-30","cited_by_count":341},{"id":"2306.11207","title":"Quilt-1M: One Million Image-Text Pairs for Histopathology","created":"2023-06-20","cited_by_count":194},{"id":"2312.06709","title":"AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One","created":"2023-12-10","cited_by_count":169},{"id":"2411.19666","title":"Multimodal Whole Slide Foundation Model for Pathology","created":"2024-11-29","cited_by_count":162},{"id":"2408.00738","title":"Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology","created":"2024-08-01","cited_by_count":161},{"id":"2212.04690","title":"Benchmarking Self-Supervised Learning on Diverse Pathology Datasets","created":"2022-12-09","cited_by_count":150},{"id":"2101.04731","title":"SEED: Self-supervised Distillation For Visual Representation","created":"2021-01-12","cited_by_count":134},{"id":"2111.04740","title":"BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images","created":"2021-11-08","cited_by_count":132},{"id":"2406.16192","title":"HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis","created":"2024-06-23","cited_by_count":107},{"id":"2405.10254","title":"PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology","created":"2024-05-16","cited_by_count":85},{"id":"2409.09173","title":"Phikon-v2, A large and public feature extractor for biomarker prediction","created":"2024-09-13","cited_by_count":74},{"id":"2407.06508","title":"A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models","created":"2024-07-09","cited_by_count":66},{"id":"2502.06750","title":"Accelerating Data Processing and Benchmarking of AI Models for Pathology","created":"2025-02-10","cited_by_count":65},{"id":"2101.12355","title":"A Petri Dish for Histopathology Image Analysis","created":"2021-01-29","cited_by_count":64},{"id":"2407.20179","title":"Theia: Distilling Diverse Vision Foundation Models for Robot Learning","created":"2024-07-29","cited_by_count":61},{"id":"2406.05074","title":"Hibou: A Family of Foundational Vision Transformers for Pathology","created":"2024-06-07","cited_by_count":57},{"id":"2501.16652","title":"Molecular-driven Foundation Model for Oncologic Pathology","created":"2025-01-28","cited_by_count":55},{"id":"2408.15823","title":"Benchmarking foundation models as feature extractors for weakly-supervised computational pathology","created":"2024-08-28","cited_by_count":49},{"id":"2501.16239","title":"Distilling foundation models for robust and efficient models in digital pathology","created":"2025-01-27","cited_by_count":36},{"id":"2503.14405","title":"DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers","created":"2025-03-18","cited_by_count":32},{"id":"2407.18449","title":"Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation","created":"2024-07-26","cited_by_count":31},{"id":"2408.05088","title":"UNIC: Universal Classification Models via Multi-teacher Distillation","created":"2024-08-09","cited_by_count":27},{"id":"2505.20202","title":"PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology","created":"2025-05-26","cited_by_count":24},{"id":"2404.15217","title":"Towards Large-Scale Training of Pathology Foundation Models","created":"2024-03-24","cited_by_count":23},{"id":"2410.01680","title":"PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation","created":"2024-10-02","cited_by_count":21},{"id":"2504.05186","title":"Training state-of-the-art pathology foundation models with orders of magnitude less data","created":"2025-04-07","cited_by_count":21},{"id":"2410.16038","title":"Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios","created":"2024-10-21","cited_by_count":17},{"id":"2501.15724","title":"A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks","created":"2025-01-27","cited_by_count":16},{"id":"2405.09990","title":"A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification","created":"2024-05-16","cited_by_count":13},{"id":"2503.02876","title":"SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and Baseline Models","created":"2025-03-04","cited_by_count":13},{"id":"2301.02608","title":"An interpretable machine learning system for colorectal cancer diagnosis from pathology slides","created":"2023-01-06","cited_by_count":9},{"id":"2410.06723","title":"Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts","created":"2024-10-09","cited_by_count":9},{"id":"2507.07860","title":"THUNDER: Tile-level Histopathology image UNDERstanding benchmark","created":"2025-07-10","cited_by_count":9},{"id":"2503.12990","title":"How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmark","created":"2025-03-17","cited_by_count":8},{"id":"2511.23204","title":"Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings","created":"2025-11-28","cited_by_count":6},{"id":"2512.21331","title":"TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning","created":"2025-12-24","cited_by_count":6}],"next_cursor":null}