{"list":"other","total":52,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2304.02643","title":"Segment Anything","created":"2023-04-05","cited_by_count":8234},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"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":"1801.04381","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","created":"2018-01-13","cited_by_count":4795},{"id":"1604.01685","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding","created":"2016-04-06","cited_by_count":4444},{"id":"2310.03744","title":"Improved Baselines with Visual Instruction Tuning","created":"2023-10-05","cited_by_count":4418},{"id":"2109.01134","title":"Learning to Prompt for Vision-Language Models","created":"2021-09-02","cited_by_count":2281},{"id":"2203.05557","title":"Conditional Prompt Learning for Vision-Language Models","created":"2022-03-10","cited_by_count":1433},{"id":"1608.05442","title":"Semantic Understanding of Scenes through the ADE20K Dataset","created":"2016-08-18","cited_by_count":1069},{"id":"2304.00685","title":"Vision-Language Models for Vision Tasks: A Survey","created":"2023-04-03","cited_by_count":839},{"id":"2201.03546","title":"Language-driven Semantic Segmentation","created":"2022-01-10","cited_by_count":717},{"id":"2210.03117","title":"MaPLe: Multi-modal Prompt Learning","created":"2022-10-06","cited_by_count":687},{"id":"2112.01518","title":"DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting","created":"2021-12-02","cited_by_count":532},{"id":"2202.11094","title":"GroupViT: Semantic Segmentation Emerges from Text Supervision","created":"2022-02-22","cited_by_count":531},{"id":"2112.10003","title":"Image Segmentation Using Text and Image Prompts","created":"2021-12-18","cited_by_count":512},{"id":"1709.03410","title":"One-Shot Learning for Semantic Segmentation","created":"2017-09-11","cited_by_count":337},{"id":"2212.02499","title":"Images Speak in Images: A Generalist Painter for In-Context Visual Learning","created":"2022-12-05","cited_by_count":318},{"id":"2204.08583","title":"VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance","created":"2022-04-18","cited_by_count":285},{"id":"2008.01449","title":"Prior Guided Feature Enrichment Network for Few-Shot Segmentation","created":"2020-08-04","cited_by_count":271},{"id":"2110.02624","title":"CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation","created":"2021-10-06","cited_by_count":254},{"id":"2305.03048","title":"Personalize Segment Anything Model with One Shot","created":"2023-05-04","cited_by_count":250},{"id":"1906.00817","title":"Zero-Shot Semantic Segmentation","created":"2019-06-03","cited_by_count":245},{"id":"2112.07910","title":"Decoupling Zero-Shot Semantic Segmentation","created":"2021-12-15","cited_by_count":199},{"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":"2312.01597","title":"SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference","created":"2023-12-04","cited_by_count":170},{"id":"2305.13310","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","created":"2023-05-22","cited_by_count":142},{"id":"2212.00785","title":"Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs","created":"2022-12-01","cited_by_count":130},{"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":"2407.12442","title":"ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference","created":"2024-07-17","cited_by_count":113},{"id":"2207.11549","title":"Self-Support Few-Shot Semantic Segmentation","created":"2022-07-23","cited_by_count":96},{"id":"2312.12359","title":"CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation","created":"2023-12-19","cited_by_count":96},{"id":"2307.12732","title":"CLIP-KD: An Empirical Study of CLIP Model Distillation","created":"2023-07-24","cited_by_count":87},{"id":"2303.14652","title":"Hierarchical Dense Correlation Distillation for Few-Shot Segmentation","created":"2023-03-26","cited_by_count":84},{"id":"2402.17726","title":"VRP-SAM: SAM with Visual Reference Prompt","created":"2024-02-27","cited_by_count":75},{"id":"2311.16926","title":"LLaFS: When Large Language Models Meet Few-Shot Segmentation","created":"2023-11-28","cited_by_count":48},{"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":"2208.00306","title":"Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation","created":"2022-07-30","cited_by_count":23},{"id":"2409.19613","title":"Hybrid Mamba for Few-Shot Segmentation","created":"2024-09-29","cited_by_count":21},{"id":"2405.08458","title":"Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation","created":"2024-05-14","cited_by_count":20},{"id":"2308.07539","title":"Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyond","created":"2023-08-15","cited_by_count":19},{"id":"2401.05806","title":"CLIP-Driven Semantic Discovery Network for Visible-Infrared Person Re-Identification","created":"2024-01-11","cited_by_count":19},{"id":"2407.09842","title":"Eliminating Feature Ambiguity for Few-Shot Segmentation","created":"2024-07-13","cited_by_count":19},{"id":"2409.19846","title":"Towards Open-Vocabulary Semantic Segmentation Without Semantic Labels","created":"2024-09-30","cited_by_count":19},{"id":"2304.08491","title":"Delving into Shape-aware Zero-shot Semantic Segmentation","created":"2023-04-17","cited_by_count":16},{"id":"2409.10389","title":"Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentation","created":"2024-09-16","cited_by_count":10},{"id":"2501.00752","title":"Foreground-Covering Prototype Generation and Matching for SAM-Aided Few-Shot Segmentation","created":"2025-01-01","cited_by_count":9},{"id":"2503.04006","title":"DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation","created":"2025-03-06","cited_by_count":6},{"id":"2306.04715","title":"UniBoost: Unsupervised Unimodal Pre-training for Boosting Zero-shot Vision-Language Tasks","created":"2023-06-07","cited_by_count":4},{"id":"2310.06488","title":"SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural Network","created":"2023-10-10","cited_by_count":4},{"id":"2505.12656","title":"SPKLIP: Aligning Spike Video Streams with Natural Language","created":"2025-05-19","cited_by_count":1}],"next_cursor":null}