{"list":"other","total":54,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"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":"2106.09685","title":"LoRA: Low-Rank Adaptation of Large Language Models","created":"2021-06-17","cited_by_count":14380},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"1506.01497","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","created":"2015-06-04","cited_by_count":9639},{"id":"2304.02643","title":"Segment Anything","created":"2023-04-05","cited_by_count":8234},{"id":"2304.08485","title":"Visual Instruction Tuning","created":"2023-04-17","cited_by_count":7869},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1506.02640","title":"You Only Look Once: Unified, Real-Time Object Detection","created":"2015-06-08","cited_by_count":4852},{"id":"2310.03744","title":"Improved Baselines with Visual Instruction Tuning","created":"2023-10-05","cited_by_count":4418},{"id":"2205.11916","title":"Large Language Models are Zero-Shot Reasoners","created":"2022-05-24","cited_by_count":4374},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"2303.18223","title":"A Survey of Large Language Models","created":"2023-03-31","cited_by_count":2576},{"id":"1606.04080","title":"Matching Networks for One Shot Learning","created":"2016-06-13","cited_by_count":2255},{"id":"1612.08242","title":"YOLO9000: Better, Faster, Stronger","created":"2016-12-25","cited_by_count":1431},{"id":"2308.00692","title":"LISA: Reasoning Segmentation via Large Language Model","created":"2023-08-01","cited_by_count":776},{"id":"2106.13884","title":"Multimodal Few-Shot Learning with Frozen Language Models","created":"2021-06-25","cited_by_count":661},{"id":"1908.06391","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","created":"2019-08-18","cited_by_count":449},{"id":"1709.03410","title":"One-Shot Learning for Semantic Segmentation","created":"2017-09-11","cited_by_count":337},{"id":"2008.01449","title":"Prior Guided Feature Enrichment Network for Few-Shot Segmentation","created":"2020-08-04","cited_by_count":271},{"id":"1903.02351","title":"CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning","created":"2019-03-06","cited_by_count":217},{"id":"2104.01893","title":"Adaptive Prototype Learning and Allocation for Few-Shot Segmentation","created":"2021-04-05","cited_by_count":207},{"id":"2104.01538","title":"Hypercorrelation Squeeze for Few-Shot Segmentation","created":"2021-04-04","cited_by_count":193},{"id":"1909.13140","title":"Feature Weighting and Boosting for Few-Shot Segmentation","created":"2019-09-28","cited_by_count":189},{"id":"1810.09091","title":"SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation","created":"2018-10-22","cited_by_count":169},{"id":"2012.06166","title":"Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?","created":"2020-12-11","cited_by_count":135},{"id":"2203.07615","title":"Learning What Not to Segment: A New Perspective on Few-Shot Segmentation","created":"2022-03-15","cited_by_count":120},{"id":"2207.10866","title":"Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation","created":"2022-07-22","cited_by_count":114},{"id":"2003.10658","title":"CRNet: Cross-Reference Networks for Few-Shot Segmentation","created":"2020-03-24","cited_by_count":101},{"id":"2207.11549","title":"Self-Support Few-Shot Semantic Segmentation","created":"2022-07-23","cited_by_count":96},{"id":"2103.16129","title":"Self-Guided and Cross-Guided Learning for Few-Shot Segmentation","created":"2021-03-30","cited_by_count":92},{"id":"2303.14652","title":"Hierarchical Dense Correlation Distillation for Few-Shot Segmentation","created":"2023-03-26","cited_by_count":84},{"id":"2103.15402","title":"Mining Latent Classes for Few-shot Segmentation","created":"2021-03-29","cited_by_count":69},{"id":"2207.08549","title":"Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation","created":"2022-07-18","cited_by_count":65},{"id":"2205.04903","title":"Learning Non-target Knowledge for Few-shot Semantic Segmentation","created":"2022-05-10","cited_by_count":57},{"id":"2108.02958","title":"Learning Meta-class Memory for Few-Shot Semantic Segmentation","created":"2021-08-06","cited_by_count":56},{"id":"2206.06122","title":"Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning","created":"2022-06-13","cited_by_count":50},{"id":"2311.16926","title":"LLaFS: When Large Language Models Meet Few-Shot Segmentation","created":"2023-11-28","cited_by_count":48},{"id":"2210.06780","title":"Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation","created":"2022-10-13","cited_by_count":37},{"id":"2108.08518","title":"Few-shot Segmentation with Optimal Transport Matching and Message Flow","created":"2021-08-19","cited_by_count":36},{"id":"2308.14575","title":"Referring Image Segmentation Using Text Supervision","created":"2023-08-28","cited_by_count":35},{"id":"2308.09294","title":"Self-Calibrated Cross Attention Network for Few-Shot Segmentation","created":"2023-08-18","cited_by_count":32},{"id":"2401.08407","title":"Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining","created":"2024-01-16","cited_by_count":30},{"id":"2309.00096","title":"AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation","created":"2023-08-31","cited_by_count":29},{"id":"2306.11087","title":"Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation","created":"2023-06-19","cited_by_count":28},{"id":"2211.08681","title":"Interclass Prototype Relation for Few-Shot Segmentation","created":"2022-11-16","cited_by_count":25},{"id":"2305.13864","title":"MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation","created":"2023-05-23","cited_by_count":25},{"id":"2303.11633","title":"Learning Context-aware Classifier for Semantic Segmentation","created":"2023-03-21","cited_by_count":23},{"id":"2301.01208","title":"Mask Matching Transformer for Few-Shot Segmentation","created":"2022-12-05","cited_by_count":22},{"id":"2212.04673","title":"MSI: Maximize Support-Set Information for Few-Shot Segmentation","created":"2022-12-09","cited_by_count":18},{"id":"2311.13865","title":"Language-guided Few-shot Semantic Segmentation","created":"2023-11-23","cited_by_count":2}],"next_cursor":null}