{"list":"other","total":68,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"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":"1612.00593","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","created":"2016-12-02","cited_by_count":5248},{"id":"1706.02413","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","created":"2017-06-07","cited_by_count":4401},{"id":"1702.04405","title":"ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes","created":"2017-02-14","cited_by_count":3117},{"id":"1512.03012","title":"ShapeNet: An Information-Rich 3D Model Repository","created":"2015-12-09","cited_by_count":2897},{"id":"1801.07829","title":"Dynamic Graph CNN for Learning on Point Clouds","created":"2018-01-24","cited_by_count":2223},{"id":"2202.12837","title":"Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?","created":"2022-02-25","cited_by_count":1434},{"id":"2101.06804","title":"What Makes Good In-Context Examples for GPT-$3$?","created":"2021-01-17","cited_by_count":1195},{"id":"2012.09688","title":"PCT: Point cloud transformer","created":"2020-12-17","cited_by_count":732},{"id":"2111.14819","title":"Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling","created":"2021-11-29","cited_by_count":653},{"id":"2112.08633","title":"Learning To Retrieve Prompts for In-Context Learning","created":"2021-12-16","cited_by_count":648},{"id":"1908.04616","title":"Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data","created":"2019-08-13","cited_by_count":532},{"id":"2007.10985","title":"PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding","created":"2020-07-21","cited_by_count":504},{"id":"2203.06604","title":"Masked Autoencoders for Point Cloud Self-supervised Learning","created":"2022-03-13","cited_by_count":459},{"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":"2209.00647","title":"Visual Prompting via Image Inpainting","created":"2022-09-01","cited_by_count":250},{"id":"1809.08495","title":"SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud","created":"2018-09-22","cited_by_count":228},{"id":"2301.13670","title":"What Makes Good Examples for Visual In-Context Learning?","created":"2023-01-31","cited_by_count":150},{"id":"2007.08488","title":"Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds","created":"2020-07-16","cited_by_count":116},{"id":"1911.02744","title":"PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation","created":"2019-11-07","cited_by_count":114},{"id":"2008.06374","title":"PointMixup: Augmentation for Point Clouds","created":"2020-08-14","cited_by_count":104},{"id":"2003.12641","title":"Self-Supervised Learning for Domain Adaptation on Point-Clouds","created":"2020-03-29","cited_by_count":99},{"id":"2108.06709","title":"SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation","created":"2021-08-15","cited_by_count":94},{"id":"2108.03557","title":"Context-Aware Mixup for Domain Adaptive Semantic Segmentation","created":"2021-08-08","cited_by_count":79},{"id":"2101.01461","title":"PointCutMix: Regularization Strategy for Point Cloud Classification","created":"2021-01-05","cited_by_count":69},{"id":"2305.14800","title":"Exploring Diverse In-Context Configurations for Image Captioning","created":"2023-05-24","cited_by_count":63},{"id":"2009.03456","title":"ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation","created":"2020-09-07","cited_by_count":56},{"id":"2403.17589","title":"Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models","created":"2024-03-26","cited_by_count":52},{"id":"2304.00690","title":"3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds","created":"2023-04-03","cited_by_count":46},{"id":"2004.08878","title":"Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation","created":"2020-04-19","cited_by_count":45},{"id":"2002.08681","title":"Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice","created":"2020-02-20","cited_by_count":43},{"id":"2112.04764","title":"3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection","created":"2021-12-09","cited_by_count":43},{"id":"2304.05640","title":"Instance-Aware Domain Generalization for Face Anti-Spoofing","created":"2023-04-12","cited_by_count":42},{"id":"2306.08659","title":"Explore In-Context Learning for 3D Point Cloud Understanding","created":"2023-06-14","cited_by_count":40},{"id":"2112.02851","title":"No-Reference Point Cloud Quality Assessment via Domain Adaptation","created":"2021-12-06","cited_by_count":38},{"id":"2212.02845","title":"SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud","created":"2022-12-06","cited_by_count":37},{"id":"1905.08955","title":"Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data","created":"2019-05-22","cited_by_count":35},{"id":"2207.10015","title":"Generative Domain Adaptation for Face Anti-Spoofing","created":"2022-07-20","cited_by_count":33},{"id":"2312.01571","title":"How to Configure Good In-Context Sequence for Visual Question Answering","created":"2023-12-04","cited_by_count":33},{"id":"2108.09169","title":"Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds","created":"2021-08-20","cited_by_count":31},{"id":"2303.05955","title":"Neuron Structure Modeling for Generalizable Remote Physiological Measurement","created":"2023-03-10","cited_by_count":31},{"id":"2108.07142","title":"PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation","created":"2021-08-16","cited_by_count":30},{"id":"2207.01545","title":"Masked Autoencoders in 3D Point Cloud Representation Learning","created":"2022-07-04","cited_by_count":30},{"id":"2108.03553","title":"Self-Adversarial Disentangling for Specific Domain Adaptation","created":"2021-08-08","cited_by_count":29},{"id":"2207.09868","title":"Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing","created":"2022-07-20","cited_by_count":28},{"id":"2303.01686","title":"Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View","created":"2023-03-03","cited_by_count":27},{"id":"2402.03631","title":"CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything Model","created":"2024-02-06","cited_by_count":27},{"id":"2112.09343","title":"Domain Adaptation on Point Clouds via Geometry-Aware Implicits","created":"2021-12-17","cited_by_count":26},{"id":"2205.06427","title":"Test-time Fourier Style Calibration for Domain Generalization","created":"2022-05-13","cited_by_count":25},{"id":"2212.04245","title":"Domain generalization of 3D semantic segmentation in autonomous driving","created":"2022-12-07","cited_by_count":25},{"id":"2403.19334","title":"Test-Time Domain Generalization for Face Anti-Spoofing","created":"2024-03-28","cited_by_count":20},{"id":"2403.09192","title":"PYRA: Parallel Yielding Re-Activation for Training-Inference Efficient Task Adaptation","created":"2024-03-14","cited_by_count":17},{"id":"2309.16483","title":"Rethinking Domain Generalization: Discriminability and Generalizability","created":"2023-09-28","cited_by_count":16},{"id":"2401.02317","title":"BA-SAM: Scalable Bias-Mode Attention Mask for Segment Anything Model","created":"2024-01-04","cited_by_count":15},{"id":"2303.07123","title":"Modality-Agnostic Debiasing for Single Domain Generalization","created":"2023-03-13","cited_by_count":14},{"id":"2308.06530","title":"BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentation","created":"2023-08-12","cited_by_count":14},{"id":"2204.01599","title":"DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation","created":"2022-04-04","cited_by_count":13},{"id":"2306.04911","title":"Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization","created":"2023-06-08","cited_by_count":11},{"id":"2309.16460","title":"Diverse Target and Contribution Scheduling for Domain Generalization","created":"2023-09-28","cited_by_count":10},{"id":"2309.15575","title":"Confidence-based Visual Dispersal for Few-shot Unsupervised Domain Adaptation","created":"2023-09-27","cited_by_count":9},{"id":"2310.11346","title":"Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing","created":"2023-10-17","cited_by_count":9},{"id":"2212.04668","title":"Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds","created":"2022-12-09","cited_by_count":8},{"id":"2305.09160","title":"SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification","created":"2023-05-16","cited_by_count":6},{"id":"2401.02610","title":"DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learning","created":"2024-01-05","cited_by_count":6},{"id":"2308.14126","title":"Synergizing Contrastive Learning and Optimal Transport for 3D Point Cloud Domain Adaptation","created":"2023-08-27","cited_by_count":5},{"id":"2304.01554","title":"MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds","created":"2023-04-04","cited_by_count":2}],"next_cursor":null}