{"list":"other","total":65,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"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":"2204.14198","title":"Flamingo: a Visual Language Model for Few-Shot Learning","created":"2022-04-29","cited_by_count":4386},{"id":"2102.05918","title":"Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision","created":"2021-02-11","cited_by_count":3864},{"id":"1512.03012","title":"ShapeNet: An Information-Rich 3D Model Repository","created":"2015-12-09","cited_by_count":2897},{"id":"2001.07685","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","created":"2020-01-21","cited_by_count":2085},{"id":"2011.00931","title":"Point Transformer","created":"2020-11-02","cited_by_count":1470},{"id":"2106.08265","title":"Towards Total Recall in Industrial Anomaly Detection","created":"2021-06-15","cited_by_count":776},{"id":"2002.07394","title":"DivideMix: Learning with Noisy Labels as Semi-supervised Learning","created":"2020-02-18","cited_by_count":692},{"id":"2112.09106","title":"RegionCLIP: Region-based Language-Image Pretraining","created":"2021-12-16","cited_by_count":616},{"id":"2011.08785","title":"PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization","created":"2020-11-17","cited_by_count":594},{"id":"2112.01071","title":"Extract Free Dense Labels from CLIP","created":"2021-12-02","cited_by_count":583},{"id":"1904.02639","title":"Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection","created":"2019-04-04","cited_by_count":564},{"id":"2112.01518","title":"DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting","created":"2021-12-02","cited_by_count":532},{"id":"2104.04015","title":"CutPaste: Self-Supervised Learning for Anomaly Detection and Localization","created":"2021-04-08","cited_by_count":531},{"id":"2108.07610","title":"DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection","created":"2021-08-17","cited_by_count":514},{"id":"2112.02413","title":"PointCLIP: Point Cloud Understanding by CLIP","created":"2021-12-04","cited_by_count":463},{"id":"2203.06604","title":"Masked Autoencoders for Point Cloud Self-supervised Learning","created":"2022-03-13","cited_by_count":459},{"id":"2007.00644","title":"Measuring Robustness to Natural Distribution Shifts in Image Classification","created":"2020-07-01","cited_by_count":454},{"id":"2201.10703","title":"Anomaly Detection via Reverse Distillation from One-Class Embedding","created":"2022-01-26","cited_by_count":421},{"id":"2107.12571","title":"CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows","created":"2021-07-27","cited_by_count":321},{"id":"2106.09018","title":"End-to-End Semi-Supervised Object Detection with Soft Teacher","created":"2021-06-16","cited_by_count":316},{"id":"2303.14814","title":"WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation","created":"2023-03-26","cited_by_count":315},{"id":"2102.09480","title":"Unbiased Teacher for Semi-Supervised Object Detection","created":"2021-02-18","cited_by_count":306},{"id":"2011.11108","title":"Multiresolution Knowledge Distillation for Anomaly Detection","created":"2020-11-22","cited_by_count":285},{"id":"2212.05171","title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","created":"2022-12-10","cited_by_count":267},{"id":"2303.15140","title":"SimpleNet: A Simple Network for Image Anomaly Detection and Localization","created":"2023-03-27","cited_by_count":256},{"id":"2206.09426","title":"ADBench: Anomaly Detection Benchmark","created":"2022-06-19","cited_by_count":232},{"id":"2111.07677","title":"FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows","created":"2021-11-15","cited_by_count":227},{"id":"1903.08550","title":"OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations","created":"2019-03-20","cited_by_count":199},{"id":"2309.00615","title":"Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following","created":"2023-09-01","cited_by_count":194},{"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":"2112.09045","title":"The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization","created":"2021-12-16","cited_by_count":178},{"id":"2301.11514","title":"Deep Industrial Image Anomaly Detection: A Survey","created":"2023-01-27","cited_by_count":148},{"id":"2103.04257","title":"Student-Teacher Feature Pyramid Matching for Anomaly Detection","created":"2021-03-07","cited_by_count":146},{"id":"2303.00601","title":"Multimodal Industrial Anomaly Detection via Hybrid Fusion","created":"2023-03-01","cited_by_count":139},{"id":"2403.12580","title":"Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection","created":"2024-03-19","cited_by_count":139},{"id":"2305.17382","title":"APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD","created":"2023-05-27","cited_by_count":130},{"id":"2210.07829","title":"Asymmetric Student-Teacher Networks for Industrial Anomaly Detection","created":"2022-10-14","cited_by_count":117},{"id":"2404.06564","title":"MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection","created":"2024-04-09","cited_by_count":113},{"id":"2203.05550","title":"Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection","created":"2022-03-10","cited_by_count":93},{"id":"2003.06780","title":"Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection","created":"2020-03-15","cited_by_count":83},{"id":"2203.00259","title":"Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection","created":"2022-03-01","cited_by_count":80},{"id":"2103.15158","title":"Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection","created":"2021-03-28","cited_by_count":77},{"id":"2303.13194","title":"Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection","created":"2023-03-23","cited_by_count":75},{"id":"2303.02595","title":"PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow","created":"2023-03-05","cited_by_count":74},{"id":"2401.16402","title":"A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect","created":"2024-01-29","cited_by_count":69},{"id":"2103.16725","title":"SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification","created":"2021-03-30","cited_by_count":68},{"id":"2205.00908","title":"MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities","created":"2022-05-02","cited_by_count":68},{"id":"2311.14897","title":"Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network","created":"2023-11-25","cited_by_count":67},{"id":"2202.11660","title":"Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors","created":"2022-02-23","cited_by_count":66},{"id":"2203.02261","title":"Class-Aware Contrastive Semi-Supervised Learning","created":"2022-03-04","cited_by_count":62},{"id":"2311.00453","title":"CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection","created":"2023-11-01","cited_by_count":58},{"id":"2210.04570","title":"The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization","created":"2022-10-10","cited_by_count":57},{"id":"2302.08769","title":"Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization","created":"2023-02-17","cited_by_count":57},{"id":"2403.14233","title":"SoftPatch: Unsupervised Anomaly Detection with Noisy Data","created":"2024-03-21","cited_by_count":55},{"id":"2312.07495","title":"Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection","created":"2023-12-12","cited_by_count":47},{"id":"2307.13925","title":"EasyNet: An Easy Network for 3D Industrial Anomaly Detection","created":"2023-07-26","cited_by_count":43},{"id":"2312.06607","title":"DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection","created":"2023-12-11","cited_by_count":33},{"id":"2311.02612","title":"GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection","created":"2023-11-05","cited_by_count":23},{"id":"2401.03145","title":"Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection","created":"2024-01-06","cited_by_count":16},{"id":"2205.14852","title":"Benchmarking Unsupervised Anomaly Detection and Localization","created":"2022-05-30","cited_by_count":11},{"id":"2403.01804","title":"PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features","created":"2024-03-04","cited_by_count":5}],"next_cursor":null}