{"list":"other","total":61,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2106.09685","title":"LoRA: Low-Rank Adaptation of Large Language Models","created":"2021-06-17","cited_by_count":14380},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"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":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1604.01685","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding","created":"2016-04-06","cited_by_count":4444},{"id":"1606.00915","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","created":"2016-06-02","cited_by_count":3241},{"id":"1802.02611","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","created":"2018-02-07","cited_by_count":3223},{"id":"1505.07818","title":"Domain-Adversarial Training of Neural Networks","created":"2015-05-28","cited_by_count":3044},{"id":"2105.15203","title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","created":"2021-05-31","cited_by_count":2940},{"id":"1612.01105","title":"Pyramid Scene Parsing Network","created":"2016-12-04","cited_by_count":2479},{"id":"1711.07971","title":"Non-local Neural Networks","created":"2017-11-21","cited_by_count":2293},{"id":"1511.00561","title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","created":"2015-11-02","cited_by_count":1487},{"id":"2203.05482","title":"Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","created":"2022-03-10","cited_by_count":1396},{"id":"1805.04687","title":"BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning","created":"2018-05-12","cited_by_count":1288},{"id":"1711.03213","title":"CyCADA: Cycle-Consistent Adversarial Domain Adaptation","created":"2017-11-08","cited_by_count":1082},{"id":"1809.02983","title":"Dual Attention Network for Scene Segmentation","created":"2018-09-09","cited_by_count":1003},{"id":"1608.02192","title":"Playing for Data: Ground Truth from Computer Games","created":"2016-08-07","cited_by_count":818},{"id":"2109.01903","title":"Robust fine-tuning of zero-shot models","created":"2021-09-04","cited_by_count":797},{"id":"1808.00897","title":"BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation","created":"2018-08-02","cited_by_count":645},{"id":"1909.11065","title":"Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation","created":"2019-09-24","cited_by_count":576},{"id":"2004.05498","title":"FDA: Fourier Domain Adaptation for Semantic Segmentation","created":"2020-04-11","cited_by_count":541},{"id":"1908.09822","title":"Confidence Regularized Self-Training","created":"2019-08-26","cited_by_count":419},{"id":"2307.13269","title":"LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition","created":"2023-07-25","cited_by_count":364},{"id":"1904.10620","title":"Bidirectional Learning for Domain Adaptation of Semantic Segmentation","created":"2019-04-24","cited_by_count":308},{"id":"2111.14887","title":"DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation","created":"2021-11-29","cited_by_count":300},{"id":"2101.10979","title":"Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation","created":"2021-01-26","cited_by_count":268},{"id":"2303.11797","title":"CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation","created":"2023-03-21","cited_by_count":219},{"id":"2208.05592","title":"Patching open-vocabulary models by interpolating weights","created":"2022-08-10","cited_by_count":190},{"id":"1811.08585","title":"Progressive Feature Alignment for Unsupervised Domain Adaptation","created":"2018-11-21","cited_by_count":183},{"id":"2103.16372","title":"Source-Free Domain Adaptation for Semantic Segmentation","created":"2021-03-30","cited_by_count":168},{"id":"1907.05740","title":"Gated-SCNN: Gated Shape CNNs for Semantic Segmentation","created":"2019-07-12","cited_by_count":166},{"id":"1910.13049","title":"Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation","created":"2019-10-29","cited_by_count":151},{"id":"1907.13426","title":"Expectation-Maximization Attention Networks for Semantic Segmentation","created":"2019-07-31","cited_by_count":147},{"id":"2206.08367","title":"SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation","created":"2022-06-16","cited_by_count":146},{"id":"2007.09222","title":"Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation","created":"2020-07-17","cited_by_count":142},{"id":"1909.00179","title":"Boundary-Aware Feature Propagation for Scene Segmentation","created":"2019-08-31","cited_by_count":107},{"id":"1804.05827","title":"DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation","created":"2018-04-16","cited_by_count":101},{"id":"2311.15537","title":"SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation","created":"2023-11-27","cited_by_count":97},{"id":"2403.19522","title":"Model Stock: All we need is just a few fine-tuned models","created":"2024-03-28","cited_by_count":92},{"id":"2302.07027","title":"AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models","created":"2023-02-14","cited_by_count":84},{"id":"2402.09997","title":"LoraRetriever: Input-Aware LoRA Retrieval and Composition for Mixed Tasks in the Wild","created":"2024-02-15","cited_by_count":68},{"id":"2011.12672","title":"Batch Normalization Embeddings for Deep Domain Generalization","created":"2020-11-25","cited_by_count":57},{"id":"1712.07436","title":"Incremental Adversarial Domain Adaptation for Continually Changing Environments","created":"2017-12-20","cited_by_count":52},{"id":"1911.01664","title":"Adaptive Context Network for Scene Parsing","created":"2019-11-05","cited_by_count":51},{"id":"1511.03328","title":"Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform","created":"2015-11-10","cited_by_count":46},{"id":"1904.06268","title":"ACE: Adapting to Changing Environments for Semantic Segmentation","created":"2019-04-12","cited_by_count":44},{"id":"2009.12518","title":"Unsupervised Model Adaptation for Continual Semantic Segmentation","created":"2020-09-26","cited_by_count":42},{"id":"2007.12942","title":"Gradient Regularized Contrastive Learning for Continual Domain Adaptation","created":"2020-07-25","cited_by_count":35},{"id":"2401.12761","title":"MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty","created":"2024-01-23","cited_by_count":32},{"id":"2112.06745","title":"A Survey of Unsupervised Domain Adaptation for Visual Recognition","created":"2021-12-13","cited_by_count":31},{"id":"1810.03272","title":"Light-Weight RefineNet for Real-Time Semantic Segmentation","created":"2018-10-08","cited_by_count":27},{"id":"2404.08639","title":"COCONut: Modernizing COCO Segmentation","created":"2024-04-12","cited_by_count":24},{"id":"2203.16195","title":"On the Road to Online Adaptation for Semantic Image Segmentation","created":"2022-03-30","cited_by_count":23},{"id":"2207.10667","title":"Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions","created":"2022-07-21","cited_by_count":16},{"id":"2310.01926","title":"DARTH: Holistic Test-time Adaptation for Multiple Object Tracking","created":"2023-10-03","cited_by_count":15},{"id":"2407.13771","title":"Training-Free Model Merging for Multi-target Domain Adaptation","created":"2024-07-18","cited_by_count":15},{"id":"2307.15063","title":"To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation","created":"2023-07-27","cited_by_count":14},{"id":"2405.14873","title":"Federated Online Adaptation for Deep Stereo","created":"2024-05-23","cited_by_count":12},{"id":"2210.12878","title":"IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes","created":"2022-10-23","cited_by_count":11},{"id":"2211.04393","title":"Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts","created":"2022-11-08","cited_by_count":8}],"next_cursor":null}