{"list":"other","total":84,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"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":"2310.03744","title":"Improved Baselines with Visual Instruction Tuning","created":"2023-10-05","cited_by_count":4418},{"id":"2201.12086","title":"BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation","created":"2022-01-28","cited_by_count":4322},{"id":"2109.01134","title":"Learning to Prompt for Vision-Language Models","created":"2021-09-02","cited_by_count":2281},{"id":"2103.13413","title":"Vision Transformers for Dense Prediction","created":"2021-03-24","cited_by_count":1803},{"id":"1907.01341","title":"Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer","created":"2019-07-02","cited_by_count":1495},{"id":"2401.10891","title":"Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data","created":"2024-01-19","cited_by_count":1360},{"id":"1406.2283","title":"Depth Map Prediction from a Single Image using a Multi-Scale Deep Network","created":"2014-06-09","cited_by_count":1274},{"id":"2110.04544","title":"CLIP-Adapter: Better Vision-Language Models with Feature Adapters","created":"2021-10-09","cited_by_count":1026},{"id":"1609.03677","title":"Unsupervised Monocular Depth Estimation with Left-Right Consistency","created":"2016-09-13","cited_by_count":820},{"id":"1704.07813","title":"Unsupervised Learning of Depth and Ego-Motion from Video","created":"2017-04-25","cited_by_count":782},{"id":"1806.02446","title":"Deep Ordinal Regression Network for Monocular Depth Estimation","created":"2018-06-06","cited_by_count":723},{"id":"2302.12288","title":"ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth","created":"2023-02-23","cited_by_count":657},{"id":"2201.02605","title":"Detecting Twenty-thousand Classes using Image-level Supervision","created":"2022-01-07","cited_by_count":614},{"id":"2011.14141","title":"AdaBins: Depth Estimation using Adaptive Bins","created":"2020-11-28","cited_by_count":601},{"id":"2112.01518","title":"DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting","created":"2021-12-02","cited_by_count":532},{"id":"1803.08669","title":"Pyramid Stereo Matching Network","created":"2018-03-23","cited_by_count":489},{"id":"1411.4734","title":"Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture","created":"2014-11-18","cited_by_count":483},{"id":"2112.02413","title":"PointCLIP: Point Cloud Understanding by CLIP","created":"2021-12-04","cited_by_count":463},{"id":"1905.02693","title":"3D Packing for Self-Supervised Monocular Depth Estimation","created":"2019-05-06","cited_by_count":439},{"id":"2111.03930","title":"Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling","created":"2021-11-06","cited_by_count":431},{"id":"1907.10326","title":"From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation","created":"2019-07-24","cited_by_count":382},{"id":"1603.04992","title":"Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue","created":"2016-03-16","cited_by_count":368},{"id":"2206.02777","title":"Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation","created":"2022-06-06","cited_by_count":344},{"id":"2312.02145","title":"Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation","created":"2023-12-04","cited_by_count":327},{"id":"2303.02153","title":"Unleashing Text-to-Image Diffusion Models for Visual Perception","created":"2023-03-03","cited_by_count":269},{"id":"1908.10553","title":"Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video","created":"2019-08-28","cited_by_count":244},{"id":"2012.01411","title":"PatchmatchNet: Learned Multi-View Patchmatch Stereo","created":"2020-12-02","cited_by_count":207},{"id":"2004.09548","title":"AANet: Adaptive Aggregation Network for Efficient Stereo Matching","created":"2020-04-20","cited_by_count":202},{"id":"2211.11682","title":"PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning","created":"2022-11-21","cited_by_count":187},{"id":"1802.05522","title":"Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints","created":"2018-02-15","cited_by_count":179},{"id":"2203.01502","title":"NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation","created":"2022-03-03","cited_by_count":153},{"id":"2208.03543","title":"MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer","created":"2022-08-06","cited_by_count":136},{"id":"1909.05845","title":"DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch","created":"2019-09-12","cited_by_count":114},{"id":"2306.17253","title":"Towards Zero-Shot Scale-Aware Monocular Depth Estimation","created":"2023-06-29","cited_by_count":101},{"id":"2401.18084","title":"Binding Touch to Everything: Learning Unified Multimodal Tactile Representations","created":"2024-01-31","cited_by_count":95},{"id":"1905.08616","title":"Unsupervised Depth Completion from Visual Inertial Odometry","created":"2019-05-15","cited_by_count":89},{"id":"2004.01314","title":"Towards Better Generalization: Joint Depth-Pose Learning without PoseNet","created":"2020-04-03","cited_by_count":85},{"id":"2204.03636","title":"SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation","created":"2022-04-07","cited_by_count":76},{"id":"2105.11610","title":"Unsupervised Scale-consistent Depth Learning from Video","created":"2021-05-25","cited_by_count":69},{"id":"2207.01077","title":"Can Language Understand Depth?","created":"2022-07-03","cited_by_count":69},{"id":"2108.10531","title":"Unsupervised Depth Completion with Calibrated Backprojection Layers","created":"2021-08-24","cited_by_count":61},{"id":"1901.10034","title":"Dense Depth Posterior (DDP) from Single Image and Sparse Range","created":"2019-01-28","cited_by_count":57},{"id":"2302.06556","title":"VA-DepthNet: A Variational Approach to Single Image Depth Prediction","created":"2023-02-13","cited_by_count":54},{"id":"2210.09670","title":"Hierarchical Normalization for Robust Monocular Depth Estimation","created":"2022-10-18","cited_by_count":52},{"id":"2109.12484","title":"Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation","created":"2021-09-26","cited_by_count":48},{"id":"2404.13565","title":"Exploring Diverse Methods in Visual Question Answering","created":"2024-04-21","cited_by_count":45},{"id":"1903.07309","title":"Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction","created":"2019-03-18","cited_by_count":44},{"id":"2010.00058","title":"Depth Estimation from Monocular Images and Sparse Radar Data","created":"2020-09-30","cited_by_count":44},{"id":"2112.02306","title":"Toward Practical Monocular Indoor Depth Estimation","created":"2021-12-04","cited_by_count":42},{"id":"2112.02399","title":"VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts","created":"2021-12-04","cited_by_count":42},{"id":"2007.07696","title":"P$^{2}$Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation","created":"2020-07-15","cited_by_count":41},{"id":"2104.00152","title":"Full Surround Monodepth from Multiple Cameras","created":"2021-03-31","cited_by_count":41},{"id":"2004.05560","title":"Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications","created":"2020-04-12","cited_by_count":40},{"id":"2006.08602","title":"Targeted Adversarial Perturbations for Monocular Depth Prediction","created":"2020-06-12","cited_by_count":40},{"id":"2106.02994","title":"Learning Topology from Synthetic Data for Unsupervised Depth Completion","created":"2021-06-06","cited_by_count":38},{"id":"2107.12429","title":"MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments","created":"2021-07-26","cited_by_count":38},{"id":"2108.04814","title":"R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes","created":"2021-08-10","cited_by_count":36},{"id":"2108.08574","title":"StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation","created":"2021-08-19","cited_by_count":36},{"id":"2404.03635","title":"WorDepth: Variational Language Prior for Monocular Depth Estimation","created":"2024-04-04","cited_by_count":34},{"id":"1807.11130","title":"Geo-Supervised Visual Depth Prediction","created":"2018-07-30","cited_by_count":33},{"id":"2107.07596","title":"Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression Network","created":"2021-07-15","cited_by_count":33},{"id":"2210.01612","title":"PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes","created":"2022-10-04","cited_by_count":32},{"id":"1910.08898","title":"Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments","created":"2019-10-20","cited_by_count":31},{"id":"2106.03010","title":"An Adaptive Framework for Learning Unsupervised Depth Completion","created":"2021-06-06","cited_by_count":30},{"id":"2403.07241","title":"Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations","created":"2024-03-12","cited_by_count":30},{"id":"2402.03312","title":"Test-Time Adaptation for Depth Completion","created":"2024-02-05","cited_by_count":26},{"id":"2203.16034","title":"Monitored Distillation for Positive Congruent Depth Completion","created":"2022-03-30","cited_by_count":24},{"id":"2309.00526","title":"SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation","created":"2023-09-01","cited_by_count":21},{"id":"2311.01034","title":"Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation","created":"2023-11-02","cited_by_count":20},{"id":"2009.10142","title":"Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations","created":"2020-09-21","cited_by_count":19},{"id":"2111.10332","title":"DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion","created":"2021-11-19","cited_by_count":17},{"id":"2112.06116","title":"Stereoscopic Universal Perturbations across Different Architectures and Datasets","created":"2021-12-12","cited_by_count":17},{"id":"2108.10637","title":"Full-Velocity Radar Returns by Radar-Camera Fusion","created":"2021-08-24","cited_by_count":16},{"id":"2312.00944","title":"Enhancing Diffusion Models with 3D Perspective Geometry Constraints","created":"2023-12-01","cited_by_count":16},{"id":"2310.09739","title":"AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimation","created":"2023-10-15","cited_by_count":15},{"id":"2409.09582","title":"NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training","created":"2024-09-15","cited_by_count":15},{"id":"2407.14020","title":"NeuroBind: Towards Unified Multimodal Representations for Neural Signals","created":"2024-07-19","cited_by_count":13},{"id":"2409.05427","title":"TextToucher: Fine-Grained Text-to-Touch Generation","created":"2024-09-09","cited_by_count":11},{"id":"2310.03967","title":"Sub-token ViT Embedding via Stochastic Resonance Transformers","created":"2023-10-06","cited_by_count":9},{"id":"2405.17315","title":"All-day Depth Completion","created":"2024-05-27","cited_by_count":8}],"next_cursor":null}