Fetching the paper…
Reading the bibliography…
Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of large foundation models in novel scenarios with limited training data.
Hypercomplex numbers, lie groups, and the creation of group representation theory
Thomas Hawkins · 1972
Earlier work this paper cites.
Neural networks for quaternion-valued function approximation
Paolo Arena, Luigi Fortuna, Luigi Occhipinti, and Maria Gabriella Xibilia · 1994
Earlier work this paper cites.
Adapterfusion: Non-destructive task composition for transfer learning
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych · 2005
Earlier work this paper cites.
Adapterhub: A framework for adapting transformers
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych · 2007
Earlier work this paper cites.
Multi-class geospatial object detection and geographic image classification based on collection of part detectors
Gong Cheng, Junwei Han, Peicheng Zhou, and Lei Guo · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Tensor–tensor products with invertible linear transforms
Eric Kernfeld, Misha Kilmer, and Shuchin Aeron · 2015
Earlier work this paper cites.
A survey on object detection in optical remote sensing images
Gong Cheng and Junwei Han · 2016
Earlier work this paper cites.
Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images
Gong Cheng, Peicheng Zhou, and Junwei Han · 2016
Earlier work this paper cites.
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
Earlier work this paper cites.
Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
Earlier work this paper cites.
Recognizing the style of visual arts via adaptive cross-layer correlation
Liyi Chen and Jufeng Yang · 2019
Earlier work this paper cites.
Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
Earlier work this paper cites.
Cross-x learning for fine-grained visual categorization
Wei Luo, Xitong Yang, Xianjie Mo, Yuheng Lu, Larry S Davis, Jun Li, Jian Yang, and Ser-Nam Lim · 2019
Earlier work this paper cites.
Quaternion recurrent neural networks
Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid, Georges Linarès, Chiheb Trabelsi, Renato De Mori, and Yoshua Bengio · 2019
Cited alongside, same era.
On the information bottleneck theory of deep learning
Andrew M Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan D Tracey, and David D Cox · 2019
Cited alongside, same era.
Dataset of breast ultrasound images
Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy · 2020
Cited alongside, same era.
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 · 2020
Cited alongside, same era.
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, Jakob Uszkoreit, and Neil Houlsby · 2020
Cited alongside, same era.
Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
Later among the works it cites.
Scaling & shifting your features: A new baseline for efficient model tuning
Dongze Lian, Daquan Zhou, Jiashi Feng, and Xinchao Wang · 2022
Later among the works it cites.
Abdomenct-1k: Is abdominal organ segmentation a solved problem?
Jun Ma, Yao Zhang, Song Gu, Cheng Zhu, Cheng Ge, Yichi Zhang, Xingle An, Congcong Wang, Qiyuan Wang, Xin Liu, Shucheng Cao, Qi Zhang, Shangqing Liu, Yunpeng Wang, Yuhui Li, Jian He, and Xiaoping Yang · 2022
Later among the works it cites.
Lst: Ladder side-tuning for parameter and memory efficient transfer learning
Yi-Lin Sung, Jaemin Cho, and Mohit Bansal · 2022
Later among the works it cites.
Clrnet: Cross layer refinement network for lane detection
Tu Zheng, Yifei Huang, Yang Liu, Wenjian Tang, Zheng Yang, Deng Cai, and Xiaofei He · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jungseok Hong, Michael Fulton, and Junaed Sattar · 2020
Cited alongside, same era.
Information theoretic counterfactual learning from missing-not-at-random feedback
Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan Kuruoglu, and Yefeng Zheng · 2020
Cited alongside, same era.
Reducing information bottleneck for weakly supervised semantic segmentation
Jungbeom Lee, Jooyoung Choi, Jisoo Mok, and Sungroh Yoon · 2021
Cited alongside, same era.
The marine debris dataset for forward-looking sonar semantic segmentation
Deepak Singh and Matias Valdenegro-Toro · 2021
Cited alongside, same era.
Segformer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 2021
Cited alongside, same era.
Co-scale conv-attentional image transformers
Weijian Xu, Yifan Xu, Tyler Chang, and Zhuowen Tu · 2021
Cited alongside, same era.
Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Shauli Ravfogel, and Yoav Goldberg · 2021
Cited alongside, same era.
Shibo Jie and Zhi-Hong Deng · 2023
Closest in time.
Segment anything
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
Closest in time.
Towards efficient visual adaption via structural re-parameterization
Gen Luo, Minglang Huang, Yiyi Zhou, Xiaoshuai Sun, Guannan Jiang, Zhiyu Wang, and Rongrong Ji · 2023
Closest in time.
Segment anything in medical images
Jun Ma and Bo Wang · 2023
Closest in time.
Christian Mattjie, Luis Vinicius de Moura, Rafaela Cappelari Ravazio, Lucas Silveira Kupssinskü, Otávio Parraga, Marcelo Mussi Delucis, and Rodrigo Coelho Barros · 2023
Closest in time.
Sam-parser: Fine-tuning sam efficiently by parameter space reconstruction
Zelin Peng, Zhengqin Xu, Zhilin Zeng, Xiaokang Yang, and Wei Shen · 2023
Closest in time.
Segmentation of organs-at-risk and gross tumor volume of npc for radiotherapy planning
SegRap2023 Challenge · 2023
Closest in time.
Adapting shortcut with normalizing flow: An efficient tuning framework for visual recognition
Yaoming Wang, Bowen Shi, Xiaopeng Zhang, Jin Li, Yuchen Liu, Wenrui Dai, Chenglin Li, Hongkai Xiong, and Qi Tian · 2023
Closest in time.
1% vs 100%: Parameter-efficient low rank adapter for dense predictions
Dongshuo Yin, Yiran Yang, Zhechao Wang, Hongfeng Yu, Kaiwen Wei, and Xian Sun · 2023
Closest in time.
Adaptive budget allocation for parameter-efficient fine-tuning
Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao · 2023
Closest in time.