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EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge

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Xin JiangAuthors Info & Claims

SenSys '23: Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems

Pages 111 - 124

https://doi.org/10.1145/3625687.3625793

Published: 26 April 2024 Publication History

Abstract

Deep Learning (DL) models have been widely deployed on IoT devices with the help of advancements in DL algorithms and chips. However, the limited resources of edge devices make these on-device DL models hard to be generalizable to diverse environments and tasks. Although the recently emerged foundation models (FMs) show impressive generalization power, how to effectively leverage the rich knowledge of FMs on resource-limited edge devices is still not explored. In this paper, we propose EdgeFM, a novel edge-cloud cooperative system with open-set recognition capability. EdgeFM selectively uploads unlabeled data to query the FM on the cloud and customizes the specific knowledge and architectures for edge models. Meanwhile, EdgeFM conducts dynamic model switching at run-time taking into account both data uncertainty and dynamic network variations, which ensures the accuracy always close to the original FM. We implement EdgeFM using two FMs on two edge platforms. We evaluate EdgeFM on three public datasets and two self-collected datasets. Results show that EdgeFM can reduce the end-to-end latency up to 3.2x and achieve 34.3% accuracy increase compared with the baseline.

References

[1]

Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, et al. 2022. Do As I Can and Not As I Say: Grounding Language in Robotic Affordances. In arXiv preprint arXiv:2204.01691.

[2]

Ali Akbari and Roozbeh Jafari. 2019. Transferring activity recognition models for new wearable sensors with deep generative domain adaptation. In Proceedings of the 18th International Conference on Information Processing in Sensor Networks. 85--96.

Digital Library

[3]

Romil Bhardwaj, Zhengxu Xia, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Nikolaos Karianakis, Kevin Hsieh, Paramvir Bahl, and Ion Stoica. 2022. Ekya: Continuous learning of video analytics models on edge compute servers. In 19th USENIX Symposium on Networked Systems Design and Implementation. 119--135.

[4]

Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021).

[5]

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems 33 (2020), 1877--1901.

[6]

Qingqing Cao, Prerna Khanna, Nicholas D Lane, and Aruna Balasubramanian. 2022. MobiVQA: Efficient On-Device Visual Question Answering. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2 (2022), 1--23.

Digital Library

[7]

Lingjiao Chen, Matei Zaharia, and James Zou. 2023. FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance. arXiv preprint arXiv:2305.05176 (2023).

[8]

Shizhe Chen and Dong Huang. 2021. Elaborative rehearsal for zero-shot action recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 13638--13647.

[9]

Tiffany Yu-Han Chen, Lenin Ravindranath, Shuo Deng, Paramvir Bahl, and Hari Balakrishnan. 2015. Glimpse: Continuous, real-time object recognition on mobile devices. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. 155--168.

[10]

Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang. 2019. A closer look at few-shot classification. arXiv preprint arXiv:1904.04232 (2019).

[11]

Google Cloud and Vertex AI. 2021. [Online]. Available: https://cloud.google.com/vertex-ai.

[12]

Wenliang Dai, Lu Hou, Lifeng Shang, Xin Jiang, Qun Liu, and Pascale Fung. 2022. Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation. In Findings of the Association for Computational Linguistics: ACL 2022. Association for Computational Linguistics, Dublin, Ireland, 2383--2395.

[13]

Deloitte. 2019. Edge AI chip shipments by device worldwide 2020 and 2024. https://www.statista.com/statistics/1084670/edge-ai-chips-shipment-worldwide/.

[14]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171--4186.

[15]

Shuya Ding, Zhe Chen, Tianyue Zheng, and Jun Luo. 2020. RF-net: A unified meta-learning framework for RF-enabled one-shot human activity recognition. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems. 517--530.

Digital Library

[16]

Utsav Drolia, Katherine Guo, Jiaqi Tan, Rajeev Gandhi, and Priya Narasimhan. 2017. Cachier: Edge-caching for recognition applications. In 2017 IEEE 37th International Conference on Distributed Computing Systems. IEEE, 276--286.

[17]

Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2019. Neural architecture search: A survey. The Journal of Machine Learning Research 20, 1 (2019), 1997--2017.

Digital Library

[18]

Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. 2023. Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 15180--15190.

[19]

Taesik Gong, Yeonsu Kim, Jinwoo Shin, and Sung-Ju Lee. 2019. Metasense: few-shot adaptation to untrained conditions in deep mobile sensing. In Proceedings of the 17th Conference on Embedded Networked Sensor Systems. 110--123.

Digital Library

[20]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770--778.

[21]

Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean. 2015. Distilling the Knowledge in a Neural Network. In NIPS Deep Learning and Representation Learning Workshop.

[22]

Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al. 2019. Searching for mobilenetv3. In Proceedings of the IEEE/CVF international conference on computer vision. 1314--1324.

[23]

Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017).

[24]

Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2021. LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations.

[25]

Zhiming Hu, Ning Ye, and Iqbal Mohomed. 2022. mmSampler: Efficient Frame Sampler for Multimodal Video Retrieval. Proceedings of Machine Learning and Systems 4 (2022), 153--171.

[26]

Kai Huang and Wei Gao. 2022. Real-time neural network inference on extremely weak devices: agile offloading with explainable AI. In Proceedings of the 28th Annual International Conference on Mobile Computing and Networking. 200--213.

Digital Library

[27]

Md Tamzeed Islam and Shahriar Nirjon. 2019. Soundsemantics: exploiting semantic knowledge in text for embedded acoustic event classification. In Proceedings of the 18th International Conference on Information Processing in Sensor Networks. 217--228.

Digital Library

[28]

Dugan Jon, Elliott Seth, Bruce A. Mah, Poskanzer Jeff, and Prabhu Kaustubh. 2014. iPerf. https://software.es.net/iperf/.

[29]

Yiping Kang, Johann Hauswald, Cao Gao, Austin Rovinski, Trevor Mudge, Jason Mars, and Lingjia Tang. 2017. Neurosurgeon: Collaborative intelligence between the cloud and mobile edge. ACM SIGARCH Computer Architecture News 45, 1 (2017), 615--629.

Digital Library

[30]

Mehrdad Khani, Ganesh Ananthanarayanan, Kevin Hsieh, Junchen Jiang, Ravi Netravali, Yuanchao Shu, Mohammad Alizadeh, and Victor Bahl. 2023. RECL: Responsive Resource-Efficient Continuous Learning for Video Analytics. In 20th USENIX Symposium on Networked Systems Design and Implementation. 917--932.

[31]

Minkyong Kim and Brian Noble. 2001. Mobile network estimation. In Proceedings of the 7th Annual International Conference on Mobile Computing and Networking. 298--309.

Digital Library

[32]

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick. 2023. Segment Anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 4015--4026.

[33]

Ikki Kishida, Hong Chen, Masaki Baba, Jiren Jin, Ayako Amma, and Hideki Nakayama. 2021. Object recognition with continual open set domain adaptation for home robot. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 1517--1526.

[34]

Stefanos Laskaridis, Stylianos I Venieris, Mario Almeida, Ilias Leontiadis, and Nicholas D Lane. 2020. SPINN: synergistic progressive inference of neural networks over device and cloud. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking. 1--15.

Digital Library

[35]

Ilias Leontiadis, Stefanos Laskaridis, Stylianos I Venieris, and Nicholas D Lane. 2021. It's always personal: Using early exits for efficient on-device CNN personalisation. In Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications. 15--21.

Digital Library

[36]

Min Li, Yu Li, Ye Tian, Li Jiang, and Qiang Xu. 2021. AppealNet: An efficient and highly-accurate edge/cloud collaborative architecture for DNN inference. In 2021 58th ACM/IEEE Design Automation Conference. 409--414.

Digital Library

[37]

Xiang Li, Xin Jiang, Xuying Meng, Aixin Sun, and Yequan Wang. 2023. FreeLM: Fine-Tuning-Free Language Model. arXiv preprint arXiv:2305.01616 (2023).

[38]

Xiwen Liang, Yangxin Wu, Jianhua Han, Hang Xu, Chunjing Xu, and Xiaodan Liang. 2022. Effective adaptation in multi-task co-training for unified autonomous driving. Advances in Neural Information Processing Systems 35 (2022), 19645--19658.

[39]

Edo Liberty, Zohar Karnin, Bing Xiang, Laurence Rouesnel, Baris Coskun, Ramesh Nallapati, Julio Delgado, Amir Sadoughi, Yury Astashonok, Piali Das, et al. 2020. Elastic machine learning algorithms in amazon sagemaker. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 731--737.

Digital Library

[40]

Yu Liu, Xuhui Jia, Mingxing Tan, Raviteja Vemulapalli, Yukun Zhu, Bradley Green, and Xiaogang Wang. 2020. Search to distill: Pearls are everywhere but not the eyes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7539--7548.

[41]

Wenjie Luo, Zhenyu Yan, Qun Song, and Rui Tan. 2021. Phyaug: Physics-directed data augmentation for deep sensing model transfer in cyber-physical systems. In Proceedings of the 20th International Conference on Information Processing in Sensor Networks. 31--46.

Digital Library

[42]

Akhil Mathur, Anton Isopoussu, Nadia Berthouze, Nicholas D Lane, and Fahim Kawsar. 2019. Unsupervised domain adaptation for robust sensory systems. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers. 505--509.

Digital Library

[43]

Zili Meng, Tingfeng Wang, Yixin Shen, Bo Wang, Mingwei Xu, Rui Han, Honghao Liu, Venkat Arun, Hongxin Hu, and Xue Wei. 2023. Enabling High Quality Real-Time Communications with Adaptive Frame-Rate. In 20th USENIX Symposium on Networked Systems Design and Implementation. 1429--1450.

[44]

Benjamin J Meyer and Tom Drummond. 2019. The importance of metric learning for robotic vision: Open set recognition and active learning. In 2019 International Conference on Robotics and Automation. IEEE, 2924--2931.

Digital Library

[45]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems 26 (2013).

[46]

Sanath Narayan, Akshita Gupta, Fahad Shahbaz Khan, Cees GM Snoek, and Ling Shao. 2020. Latent embedding feedback and discriminative features for zero-shot classification. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XXII 16. Springer, 479--495.

[47]

Maria-Elena Nilsback and Andrew Zisserman. 2008. Automated flower classification over a large number of classes. In 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing. IEEE, 722--729.

Digital Library

[48]

Wei Niu, Xiaolong Ma, Sheng Lin, Shihao Wang, Xuehai Qian, Xue Lin, Yanzhi Wang, and Bin Ren. 2020. Patdnn: Achieving real-time dnn execution on mobile devices with pattern-based weight pruning. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems. 907--922.

Digital Library

[49]

Xiaomin Ouyang, Xian Shuai, Jiayu Zhou, Ivy Wang Shi, Zhiyuan Xie, Guoliang Xing, and Jianwei Huang. 2022. Cosmo: contrastive fusion learning with small data for multimodal human activity recognition. In Proceedings of the 28th Annual International Conference on Mobile Computing And Networking. 324--337.

Digital Library

[50]

Eunhyeok Park, Dongyoung Kim, Soobeom Kim, Yong-Deok Kim, Gunhee Kim, Sungroh Yoon, and Sungjoo Yoo. 2015. Big/little deep neural network for ultra low power inference. In 2015 International Conference on Hardware/Software Codesign and System Synthesis. IEEE, 124--132.

[51]

Karol J Piczak. 2015. ESC: Dataset for environmental sound classification. In Proceedings of the 23rd ACM international conference on Multimedia. 1015--1018.

Digital Library

[52]

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning. PMLR, 8748--8763.

[53]

Dhruv Shah, Błażej Osiński, Sergey Levine, et al. 2023. Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action. In Conference on Robot Learning. PMLR, 492--504.

[54]

Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).

[55]

Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao. 2015. Sun rgb-d: A rgb-d scene understanding benchmark suite. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 567--576.

[56]

Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. 2012. UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012).

[57]

Hongzu Su, Jingjing Li, Zhi Chen, Lei Zhu, and Ke Lu. 2022. Distinguishing unseen from seen for generalized zero-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7885--7894.

[58]

Ximeng Sun, Pengchuan Zhang, Peizhao Zhang, Hardik Shah, Kate Saenko, and Xide Xia. 2023. DIME-FM: Distilling Multimodal and Efficient Foundation Models. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 15521--15533.

[59]

Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. PMLR, 6105--6114.

[60]

MLC team. 2023. MLC-LLM. https://github.com/mlc-ai/mlc-llm

[61]

Catherine Tong, Jinchen Ge, and Nicholas D Lane. 2021. Zero-shot learning for imu-based activity recognition using video embeddings. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 4 (2021), 1--23.

Digital Library

[62]

Sathish Vallachira, Michal Orkisz, Mikael Norrlöf, and Sachit Butail. 2019. Data-driven gearbox failure detection in industrial robots. IEEE Transactions on Industrial Informatics 16, 1 (2019), 193--201.

[63]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017).

[64]

Yiding Wang, Kai Chen, Haisheng Tan, and Kun Guo. 2023. Tabi: An Efficient Multi-Level Inference System for Large Language Models. In Proceedings of the Eighteenth European Conference on Computer Systems. 233--248.

Digital Library

[65]

Yixuan Wei, Han Hu, Zhenda Xie, Zheng Zhang, Yue Cao, Jianmin Bao, Dong Chen, and Baining Guo. 2022. Contrastive learning rivals masked image modeling in fine-tuning via feature distillation. arXiv preprint arXiv:2205.14141 (2022).

[66]

Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng. 2016. Quantized convolutional neural networks for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4820--4828.

[67]

Huang Xie and Tuomas Virtanen. 2021. Zero-shot audio classification via semantic embeddings. IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021), 1233--1242.

Digital Library

[68]

Huatao Xu, Pengfei Zhou, Rui Tan, Mo Li, and Guobin Shen. 2021. Limu-bert: Unleashing the potential of unlabeled data for imu sensing applications. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems. 220--233.

Digital Library

[69]

Mengwei Xu, Mengze Zhu, Yunxin Liu, Felix Xiaozhu Lin, and Xuanzhe Liu. 2018. Deepcache: Principled cache for mobile deep vision. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. 129--144.

Digital Library

[70]

Bufang Yang, Wenxuan Wu, Yitian Liu, and Hongxing Liu. 2022. A novel sleep stage contextual refinement algorithm leveraging conditional random fields. IEEE Transactions on Instrumentation and Measurement 71 (2022), 1--13.

[71]

Bufang Yang, Xilin Zhu, Yitian Liu, and Hongxing Liu. 2021. A single-channel EEG based automatic sleep stage classification method leveraging deep one-dimensional convolutional neural network and hidden Markov model. Biomedical Signal Processing and Control 68 (2021), 102581.

[72]

Lewei Yao, Jianhua Han, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, and Hang Xu. 2023. Detclipv2: Scalable open-vocabulary object detection pre-training via word-region alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 23497--23506.

[73]

Shuochao Yao, Jinyang Li, Dongxin Liu, Tianshi Wang, Shengzhong Liu, Huajie Shao, and Tarek Abdelzaher. 2020. Deep compressive offloading: Speeding up neural network inference by trading edge computation for network latency. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems. 476--488.

Digital Library

[74]

Mu Yuan, Lan Zhang, Fengxiang He, Xueting Tong, and Xiang-Yang Li. 2022. Infi: end-to-end learnable input filter for resource-efficient mobile-centric inference. In Proceedings of the 28th Annual International Conference on Mobile Computing And Networking. 228--241.

Digital Library

[75]

Chaoning Zhang, Dongshen Han, Yu Qiao, Jung Uk Kim, Sung Ho Bae, Seungkyu Lee, and Choong Seon Hong. 2023. Faster Segment Anything: Towards Lightweight SAM for Mobile Applications. arXiv preprint arXiv:2306.14289 (2023).

[76]

Zhihe Zhao, Kai Wang, Neiwen Ling, and Guoliang Xing. 2021. Edgeml: An automl framework for real-time deep learning on the edge. In Proceedings of the International Conference on Internet-of-Things Design and Implementation. 133--144.

Digital Library

Cited By

Yang BHe LLiu KYan Z(2024)VIAssist: Adapting Multi-Modal Large Language Models for Users with Visual Impairments2024 IEEE International Workshop on Foundation Models for Cyber-Physical Systems & Internet of Things (FMSys)10.1109/FMSys62467.2024.00010(32-37)Online publication date: 13-May-2024
https://doi.org/10.1109/FMSys62467.2024.00010

Index Terms

EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image ACM Conferences

SenSys '23: Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems

November 2023

574 pages

ISBN:9798400704147

DOI:10.1145/3625687

General Chair:
Rasit Eskicioglu,
Program Chair:
Polly Huang,
Program Co-chair:
Neal Patwari

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Published: 26 April 2024

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Research Grants Council, University Grants Committee
National Natural Science Foundation of China
The Chinese University of Hong Kong

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SenSys '23

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SenSys '23: 21st ACM Conference on Embedded Networked Sensor Systems

November 12 - 17, 2023

Istanbul, Turkiye

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Overall Acceptance Rate 174 of 867 submissions, 20%

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Cited By

Yang BHe LLiu KYan Z(2024)VIAssist: Adapting Multi-Modal Large Language Models for Users with Visual Impairments2024 IEEE International Workshop on Foundation Models for Cyber-Physical Systems & Internet of Things (FMSys)10.1109/FMSys62467.2024.00010(32-37)Online publication date: 13-May-2024
https://doi.org/10.1109/FMSys62467.2024.00010

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