Fetching the paper…
Reading the bibliography…
Large vision-language models (LVLMs) are markedly proficient in deriving visual representations guided by natural language.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
Why the logistic function? A tutorial discussion on probabilities and neural networks
Michael I Jordan et al · 1995
Earlier work this paper cites.
Learning to share visual appearance for multiclass object detection. In CVPR 2011 . IEEE, 1481–1488
Ruslan Salakhutdinov, Antonio Torralba, and Josh Tenenbaum. 2011 · 2011
Earlier work this paper cites.
SINGA: A Distributed Deep Learning Platform. In Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, MM . ACM, 685–688
Beng Chin Ooi, Kian-Lee Tan, Sheng Wang, Wei Wang, Qingchao Cai, Gang Chen, Jinyang Gao, Zhaojing Luo, Anthony K. H. Tung, Yuan Wang, Zhongle Xie, Meihui Zhang, and Kaiping Zheng. 2015 · 2015
Earlier work this paper cites.
Deep one-class classification. In International conference on machine learning . PMLR, 4393–4402
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft. 2018 · 2018
Earlier work this paper cites.
MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 9592–9600
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. 2019 · 2019
Earlier work this paper cites.
Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 1705–1714
Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel. 2019 · 2019
Earlier work this paper cites.
Improving Data Analytics with Fast and Adaptive Regularization
Zhaojing Luo, Shaofeng Cai, Gang Chen, Jinyang Gao, Wang-Chien Lee, Kee Yuan Ngiam, and Meihui Zhang. 2021a · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations. In International conference on machine learning . PMLR, 1597–1607
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
Earlier work this paper cites.
Any-shot sequential anomaly detection in surveillance videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . 934–935
Keval Doshi and Yasin Yilmaz. 2020 · 2020
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Earlier work this paper cites.
Deep generative model using unregularized score for anomaly detection with heterogeneous complexity
Takashi Matsubara, Kazuki Sato, Kenta Hama, Ryosuke Tachibana, and Kuniaki Uehara. 2020 · 2020
Earlier work this paper cites.
Learning memory-guided normality for anomaly detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 14372–14381
Hyunjong Park, Jongyoun Noh, and Bumsub Ham. 2020 · 2020
Earlier work this paper cites.
Patch svdd: Patch-level svdd for anomaly detection and segmentation. In Proceedings of the Asian conference on computer vision
Jihun Yi and Sungroh Yoon. 2020 · 2020
Earlier work this paper cites.
ARM-Net: Adaptive Relation Modeling Network for Structured Data. In SIGMOD ’21: International Conference on Management of Data, Virtual Event, China, June 20-25, 2021 , Guoliang Li, Zhanhuai Li, Stratos Idreos, and Divesh Srivastava (Eds.). ACM, 207–220
Shaofeng Cai, Kaiping Zheng, Gang Chen, H. V. Jagadish, Beng Chin Ooi, and Meihui Zhang. 2021 · 2021
Earlier work this paper cites.
Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 397–406
Hila Chefer, Shir Gur, and Lior Wolf. 2021 · 2021
Earlier work this paper cites.
Padim: a patch distribution modeling framework for anomaly detection and localization. In International Conference on Pattern Recognition . Springer, 475–489
Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier. 2021 · 2021
Earlier work this paper cites.
OpenCLIP
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt. 2021 · 2021
Earlier work this paper cites.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Earlier work this paper cites.
Cutpaste: Self-supervised learning for anomaly detection and localization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 9664–9674
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister. 2021 · 2021
Earlier work this paper cites.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Earlier work this paper cites.
Adaptive Knowledge Driven Regularization for Deep Neural Networks. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021 . AAAI Press, 8810–8818
Zhaojing Luo, Shaofeng Cai, Can Cui, Beng Chin Ooi, and Yang Yang. 2021b · 2021
Cited alongside, same era.
MOCCA: Multilayer one-class classification for anomaly detection
Fabio Valerio Massoli, Fabrizio Falchi, Alperen Kantarci, Şeymanur Akti, Hazim Kemal Ekenel, and Giuseppe Amato. 2021 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision. In International conference on machine learning . PMLR, 8748–8763
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Multiresolution knowledge distillation for anomaly detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 14902–14912
AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization
Hanqiu Deng, Zhaoxiang Zhang, Jinan Bao, and Xingyu Li. 2023 · 2023
Later among the works it cites.
AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models
Zhaopeng Gu, Bingke Zhu, Guibo Zhu, Yingying Chen, Ming Tang, and Jinqiao Wang. 2023 · 2023
Later among the works it cites.
Winclip: Zero-/few-shot anomaly classification and segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 19606–19616
Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, and Onkar Dabeer. 2023 · 2023
Later among the works it cites.
Robust and Transferable Log-based Anomaly Detection
Peng Jia, Shaofeng Cai, Beng Chin Ooi, Pinghui Wang, and Yiyuan Xiong. 2023 · 2023
Later among the works it cites.
Multi-Modal Classifiers for Open-Vocabulary Object Detection
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H Rohban, and Hamid R Rabiee. 2021 · 2021
Cited alongside, same era.
Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. 2021 · 2021
Cited alongside, same era.
Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 8330–8339
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. 2021 · 2021
Cited alongside, same era.
Anomaly detection for medical images using self-supervised and translation-consistent features
He Zhao, Yuexiang Li, Nanjun He, Kai Ma, Leyuan Fang, Huiqi Li, and Yefeng Zheng. 2021 · 2021
Cited alongside, same era.
Anomaly detection via reverse distillation from one-class embedding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9737–9746
Hanqiu Deng and Xingyu Li. 2022 · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
Visual classification via description from large language models
Sachit Menon and Carl Vondrick. 2022 · 2022
Cited alongside, same era.
Self-supervised predictive convolutional attentive block for anomaly detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 13576–13586
Nicolae-Cătălin Ristea, Neelu Madan, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B Moeslund, and Mubarak Shah. 2022 · 2022
Cited alongside, same era.
Towards total recall in industrial anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 14318–14328
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler. 2022 · 2022
Cited alongside, same era.
Prannay Kaul, Weidi Xie, and Andrew Zisserman. 2023 · 2023
Later among the works it cites.
Maple: Multi-modal prompt learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 19113–19122
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan. 2023 · 2023
Later among the works it cites.
Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 4015–4026
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
Later among the works it cites.
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023 · 2023
Later among the works it cites.
Regularized Pairwise Relationship based Analytics for Structured Data
Zhaojing Luo, Shaofeng Cai, Yatong Wang, and Beng Chin Ooi. 2023 · 2023
Later among the works it cites.
Synthetic prompting: Generating chain-of-thought demonstrations for large language models
Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen. 2023 · 2023
Later among the works it cites.
Pandagpt: One model to instruction-follow them all
Yixuan Su, Tian Lan, Huayang Li, Jialu Xu, Yan Wang, and Deng Cai. 2023 · 2023
Later among the works it cites.
Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection
Masato Tamura. 2023 · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Later among the works it cites.
IFSeg: Image-free Semantic Segmentation via Vision-Language Model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2967–2977
Sukmin Yun, Seong Hyeon Park, Paul Hongsuck Seo, and Jinwoo Shin. 2023 · 2023
Later among the works it cites.
Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection
Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, and Jiming Chen. 2023 · 2023
Later among the works it cites.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023b · 2023
Later among the works it cites.
METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection
Jiaqi Zhu, Shaofeng Cai, Fang Deng, Beng Chin Ooi, and Wenqiao Zhang. 2023a · 2023
Later among the works it cites.
Uaed: Unsupervised abnormal emotion detection network based on wearable mobile device
Jiaqi Zhu, Fang Deng, Jiachen Zhao, Daoming Liu, and Jie Chen. 2023c · 2023
Later among the works it cites.
Deep Learning Model for Automated Detection and Classification of Degenerative Cord Signal Abnormality, Spinal Canal and Neural Foraminal Stenosis on Cervical Spine Magnetic Resonance Imaging. In Seminars in Musculoskeletal Radiology , Vol. 28. Thieme Medical Publishers, Inc., A132
A Lee, J Wu, C Liu, YJ Lee, JH Tan, J Huang, N Kumar, BC Ooi, and J Hallinan. 2024 · 2024
Closest in time.
Xurui Li, Ziming Huang, Feng Xue, and Yu Zhou. 2024 · 2024
Closest in time.
Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts
Jiawen Zhu and Guansong Pang. 2024 · 2024
Closest in time.