{"list":"other","total":111,"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":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2303.08774","title":"GPT-4 Technical Report","created":"2023-03-15","cited_by_count":18396},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"2304.02643","title":"Segment Anything","created":"2023-04-05","cited_by_count":8234},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"id":"2204.02311","title":"PaLM: Scaling Language Modeling with Pathways","created":"2022-04-05","cited_by_count":5326},{"id":"1903.11027","title":"nuScenes: A multimodal dataset for autonomous driving","created":"2019-03-26","cited_by_count":4523},{"id":"2205.01068","title":"OPT: Open Pre-trained Transformer Language Models","created":"2022-05-02","cited_by_count":3512},{"id":"2203.15556","title":"Training Compute-Optimal Large Language Models","created":"2022-03-29","cited_by_count":3019},{"id":"1912.04838","title":"Scalability in Perception for Autonomous Driving: Waymo Open Dataset","created":"2019-12-10","cited_by_count":2451},{"id":"2003.04297","title":"Improved Baselines with Momentum Contrastive Learning","created":"2020-03-09","cited_by_count":2287},{"id":"1909.08053","title":"Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism","created":"2019-09-17","cited_by_count":1868},{"id":"2104.02057","title":"An Empirical Study of Training Self-Supervised Vision Transformers","created":"2021-04-05","cited_by_count":1616},{"id":"1803.07728","title":"Unsupervised Representation Learning by Predicting Image Rotations","created":"2018-03-21","cited_by_count":1591},{"id":"1603.09246","title":"Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles","created":"2016-03-30","cited_by_count":1382},{"id":"1904.08755","title":"4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks","created":"2019-04-18","cited_by_count":1208},{"id":"1904.08889","title":"KPConv: Flexible and Deformable Convolution for Point Clouds","created":"2019-04-18","cited_by_count":1205},{"id":"2111.09886","title":"SimMIM: A Simple Framework for Masked Image Modeling","created":"2021-11-18","cited_by_count":1153},{"id":"2006.11275","title":"Center-based 3D Object Detection and Tracking","created":"2020-06-19","cited_by_count":1144},{"id":"1603.08511","title":"Colorful Image Colorization","created":"2016-03-28","cited_by_count":1114},{"id":"1505.05192","title":"Unsupervised Visual Representation Learning by Context Prediction","created":"2015-05-19","cited_by_count":1066},{"id":"1905.09272","title":"Data-Efficient Image Recognition with Contrastive Predictive Coding","created":"2019-05-22","cited_by_count":673},{"id":"1911.11236","title":"RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds","created":"2019-11-25","cited_by_count":585},{"id":"2211.15654","title":"OpenScene: 3D Scene Understanding with Open Vocabularies","created":"2022-11-28","cited_by_count":559},{"id":"2304.06718","title":"Segment Everything Everywhere All at Once","created":"2023-04-13","cited_by_count":548},{"id":"2007.10985","title":"PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding","created":"2020-07-21","cited_by_count":504},{"id":"2205.09113","title":"Masked Autoencoders As Spatiotemporal Learners","created":"2022-05-18","cited_by_count":429},{"id":"2007.16100","title":"Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution","created":"2020-07-31","cited_by_count":423},{"id":"2012.09740","title":"Understanding the Behaviour of Contrastive Loss","created":"2020-12-15","cited_by_count":398},{"id":"2011.10033","title":"Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation","created":"2020-11-19","cited_by_count":389},{"id":"2212.00794","title":"Scaling Language-Image Pre-training via Masking","created":"2022-12-01","cited_by_count":317},{"id":"2212.11270","title":"Generalized Decoding for Pixel, Image, and Language","created":"2022-12-21","cited_by_count":294},{"id":"2003.14032","title":"PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation","created":"2020-03-31","cited_by_count":275},{"id":"2101.02691","title":"Self-Supervised Pretraining of 3D Features on any Point-Cloud","created":"2021-01-07","cited_by_count":217},{"id":"2303.08131","title":"A Simple Framework for Open-Vocabulary Segmentation and Detection","created":"2023-03-14","cited_by_count":198},{"id":"2102.06191","title":"Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals","created":"2021-02-11","cited_by_count":190},{"id":"2301.04926","title":"CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP","created":"2023-01-12","cited_by_count":181},{"id":"2103.12978","title":"RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation","created":"2021-03-24","cited_by_count":173},{"id":"2004.01803","title":"SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation","created":"2020-04-03","cited_by_count":170},{"id":"2109.03805","title":"Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking","created":"2021-09-08","cited_by_count":166},{"id":"2102.04530","title":"(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network","created":"2021-02-08","cited_by_count":164},{"id":"2103.10957","title":"Efficient Visual Pretraining with Contrastive Detection","created":"2021-03-19","cited_by_count":150},{"id":"1911.12676","title":"xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation","created":"2019-11-28","cited_by_count":129},{"id":"2303.17597","title":"Robo3D: Towards Robust and Reliable 3D Perception against Corruptions","created":"2023-03-30","cited_by_count":129},{"id":"2303.05367","title":"Rethinking Range View Representation for LiDAR Segmentation","created":"2023-03-09","cited_by_count":128},{"id":"2203.16258","title":"Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data","created":"2022-03-30","cited_by_count":126},{"id":"2306.09347","title":"Segment Any Point Cloud Sequences by Distilling Vision Foundation Models","created":"2023-06-15","cited_by_count":125},{"id":"2002.09147","title":"SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances","created":"2020-02-21","cited_by_count":123},{"id":"2011.12954","title":"RELLIS-3D Dataset: Data, Benchmarks and Analysis","created":"2020-11-17","cited_by_count":119},{"id":"2103.14962","title":"Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation","created":"2021-03-27","cited_by_count":107},{"id":"2207.00026","title":"LaserMix for Semi-Supervised LiDAR Semantic Segmentation","created":"2022-06-30","cited_by_count":93},{"id":"2012.04934","title":"AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation","created":"2020-12-09","cited_by_count":87},{"id":"2011.11964","title":"LiDAR-based Panoptic Segmentation via Dynamic Shifting Network","created":"2020-11-24","cited_by_count":84},{"id":"2009.03137","title":"Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges","created":"2020-09-07","cited_by_count":80},{"id":"2107.05399","title":"Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation","created":"2021-07-12","cited_by_count":76},{"id":"2008.00305","title":"Self-supervised Learning of Point Clouds via Orientation Estimation","created":"2020-08-01","cited_by_count":72},{"id":"2203.08537","title":"Scribble-Supervised LiDAR Semantic Segmentation","created":"2022-03-16","cited_by_count":69},{"id":"2006.02598","title":"Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning","created":"2020-06-04","cited_by_count":68},{"id":"2212.05867","title":"ALSO: Automotive Lidar Self-supervision by Occupancy estimation","created":"2022-12-12","cited_by_count":68},{"id":"2104.04891","title":"SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds","created":"2021-04-11","cited_by_count":64},{"id":"2207.12691","title":"CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving","created":"2022-07-26","cited_by_count":56},{"id":"2108.06230","title":"Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds","created":"2021-08-13","cited_by_count":55},{"id":"2112.02990","title":"4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding","created":"2021-12-06","cited_by_count":54},{"id":"2202.13589","title":"Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey","created":"2022-02-28","cited_by_count":54},{"id":"2405.05258","title":"Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving","created":"2024-05-08","cited_by_count":54},{"id":"2301.10100","title":"Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation","created":"2023-01-24","cited_by_count":53},{"id":"2107.02389","title":"Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling","created":"2021-07-06","cited_by_count":51},{"id":"1811.10323","title":"Universal Semi-Supervised Semantic Segmentation","created":"2018-11-26","cited_by_count":50},{"id":"2312.04484","title":"FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation","created":"2023-12-07","cited_by_count":50},{"id":"2310.15171","title":"RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions","created":"2023-10-23","cited_by_count":49},{"id":"2106.02377","title":"A Survey on Deep Domain Adaptation for LiDAR Perception","created":"2021-06-04","cited_by_count":47},{"id":"2107.14724","title":"Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation","created":"2021-07-30","cited_by_count":46},{"id":"2109.03787","title":"FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding","created":"2021-09-08","cited_by_count":46},{"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":"2310.17504","title":"Three Pillars improving Vision Foundation Model Distillation for Lidar","created":"2023-10-26","cited_by_count":44},{"id":"2111.15242","title":"ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation","created":"2021-11-30","cited_by_count":41},{"id":"2210.08064","title":"LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds","created":"2022-10-14","cited_by_count":41},{"id":"2405.17426","title":"Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving","created":"2024-05-27","cited_by_count":41},{"id":"2101.07253","title":"Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentation","created":"2021-01-18","cited_by_count":40},{"id":"2207.02605","title":"GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation","created":"2022-07-06","cited_by_count":36},{"id":"2207.09763","title":"GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation","created":"2022-07-20","cited_by_count":35},{"id":"2301.05709","title":"Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss","created":"2023-01-12","cited_by_count":35},{"id":"2004.11803","title":"Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study","created":"2020-04-06","cited_by_count":33},{"id":"2303.11203","title":"Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation","created":"2023-03-20","cited_by_count":33},{"id":"2407.06190","title":"4D Contrastive Superflows are Dense 3D Representation Learners","created":"2024-07-08","cited_by_count":33},{"id":"2406.12214","title":"Is Your HD Map Constructor Reliable under Sensor Corruptions?","created":"2024-06-18","cited_by_count":28},{"id":"2403.17009","title":"Is Your LiDAR Placement Optimized for 3D Scene Understanding?","created":"2024-03-25","cited_by_count":26},{"id":"2405.01538","title":"Multi-Space Alignments Towards Universal LiDAR Segmentation","created":"2024-05-02","cited_by_count":25},{"id":"2207.04997","title":"A Closer Look at Invariances in Self-supervised Pre-training for 3D Vision","created":"2022-07-11","cited_by_count":24},{"id":"2310.17281","title":"BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds","created":"2023-10-26","cited_by_count":24},{"id":"2105.00373","title":"Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving","created":"2021-05-02","cited_by_count":22},{"id":"2304.03251","title":"SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation","created":"2023-04-06","cited_by_count":21},{"id":"2104.03851","title":"CoCoNets: Continuous Contrastive 3D Scene Representations","created":"2021-04-08","cited_by_count":19},{"id":"2211.05997","title":"LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation","created":"2022-11-11","cited_by_count":16},{"id":"2210.01784","title":"COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation","created":"2022-10-04","cited_by_count":15}],"next_cursor":"100"}