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Recently segment anything model (SAM) has shown powerful segmentation capability and has drawn great attention in computer vision fields.
Learned step size quantization
Esser, S. K.; McKinstry, J. L.; Bablani, D.; Appuswamy, R.; and Modha, D. S. 2019 · 1902
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Easyquant: Post-training quantization via scale optimization
Wu, D.; Tang, Q.; Zhao, Y.; Zhang, M.; Fu, Y.; and Zhang, D. 2020 · 2006
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2010
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Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Romero, A.; Ballas, N.; Kahou, S. E.; Chassang, A.; Gatta, C.; and Bengio, Y. 2014 · 2014
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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; Dean, J.; et al. 2015 · 2015
Earlier work this paper cites.
Fully Convolutional Networks for Semantic Segmentation
Long, J.; Shelhamer, E.; and Darrell, T. 2015 · 2015
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Milletari, F.; Navab, N.; and Ahmadi, S.-A. 2016 · 2016
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Learning efficient object detection models with knowledge distillation
Chen, G.; Choi, W.; Yu, X.; Han, T.; and Chandraker, M. 2017 · 2017
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Focal loss for dense object detection
Lin, T.-Y.; Goyal, P.; Girshick, R.; He, K.; and Dollár, P. 2017 · 2017
Earlier work this paper cites.
Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
Caicedo, J. C.; Goodman, A.; Karhohs, K. W.; Cimini, B. A.; Ackerman, J.; Haghighi, M.; Heng, C.; Becker, T.; Doan, M.; McQuin, C.; et al. 2019 · 2018
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Pact: Parameterized clipping activation for quantized neural networks
Choi, J.; Wang, Z.; Venkataramani, S.; Chuang, P. I.-J.; Srinivasan, V.; and Gopalakrishnan, K. 2018 · 2018
Earlier work this paper cites.
Path aggregation network for instance segmentation
Liu, S.; Qi, L.; Qin, H.; Shi, J.; and Jia, J. 2018 · 2018
Earlier work this paper cites.
Yolact: Real-time instance segmentation
Bolya, D.; Zhou, C.; Xiao, F.; and Lee, Y. J. 2019 · 2019
Earlier work this paper cites.
Low-bit quantization of neural networks for efficient inference
Choukroun, Y.; Kravchik, E.; Yang, F.; and Kisilev, P. 2019 · 2019
Earlier work this paper cites.
Towards Efficient Instance Segmentation with Hierarchical Distillation
Deng, Z.; Kong, Q.; and Murakami, T. 2019 · 2019
Cited alongside, same era.
Lvis: A dataset for large vocabulary instance segmentation
Gupta, A.; Dollar, P.; and Girshick, R. 2019 · 2019
Cited alongside, same era.
Panoptic segmentation
Kirillov, A.; He, K.; Girshick, R.; Rother, C.; and Dollár, P. 2019 · 2019
Cited alongside, same era.
Structured knowledge distillation for semantic segmentation
Liu, Y.; Chen, K.; Liu, C.; Qin, Z.; Luo, Z.; and Wang, J. 2019 · 2019
Cited alongside, same era.
Relational Knowledge Distillation
Park, W.; Kim, D.; Lu, Y.; and Cho, M. 2019 · 2019
Cited alongside, same era.
Correlation congruence for knowledge distillation
Peng, B.; Jin, X.; Liu, J.; Li, D.; Wu, Y.; Liu, Y.; Zhou, S.; and Zhang, Z. 2019 · 2019
Cited alongside, same era.
Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization
Yuan, Z.; Xue, C.; Chen, Y.; Wu, Q.; and Sun, G. 2022 · 2022
Later among the works it cites.
Segment anything in 3d with nerfs
Cen, J.; Zhou, Z.; Fang, J.; Shen, W.; Xie, L.; Zhang, X.; and Tian, Q. 2023 · 2023
Closest in time.
Cheng, Y.; Li, L.; Xu, Y.; Li, X.; Yang, Z.; Wang, W.; and Yang, Y. 2023 · 2023
Closest in time.
Improving Lightweight AdderNet via Distillation From ℓ 2 \ell_{2} to ℓ 1 \ell_{1} -norm
Dong, M.; Chen, X.; Wang, Y.; and Xu, C. 2023 · 2023
Closest in time.
YOLO by Ultralytics
Jocher, G.; Chaurasia, A.; and Qiu, J. 2023 · 2023
Closest in time.
Segment anything
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Optical flow distillation: Towards efficient and stable video style transfer
Chen, X.; Zhang, Y.; Wang, Y.; Shu, H.; Xu, C.; and Xu, C. 2020 · 2020
Cited alongside, same era.
Up or down? adaptive rounding for post-training quantization
Nagel, M.; Amjad, R. A.; Van Baalen, M.; Louizos, C.; and Blankevoort, T. 2020 · 2020
Cited alongside, same era.
Distilling object detectors via decoupled features
Guo, J.; Han, K.; Wang, Y.; Wu, H.; Chen, X.; Xu, C.; and Xu, C. 2021 · 2021
Cited alongside, same era.
Segmenter: Transformer for Semantic Segmentation
Strudel, R.; Garcia, R.; Laptev, I.; and Schmid, C. 2021 · 2021
Cited alongside, same era.
Masked-attention mask transformer for universal image segmentation
Cheng, B.; Misra, I.; Schwing, A. G.; Kirillov, A.; and Girdhar, R. 2022 · 2022
Cited alongside, same era.
Instance segmentation for autonomous log grasping in forestry operations
Fortin, J.-M.; Gamache, O.; Grondin, V.; Pomerleau, F.; and Giguère, P. 2022 · 2022
Cited alongside, same era.
Kirillov, A.; Mintun, E.; Ravi, N.; Mao, H.; Rolland, C.; Gustafson, L.; Xiao, T.; Whitehead, S.; Berg, A. C.; Lo, W.-Y.; et al. 2023 · 2023
Closest in time.
Pd-quant: Post-training quantization based on prediction difference metric
Liu, J.; Niu, L.; Yuan, Z.; Yang, D.; Wang, X.; and Liu, W. 2023 · 2023
Closest in time.
Segment anything in medical images
Ma, J.; and Wang, B. 2023 · 2023
Closest in time.
TSPTQ-ViT: Two-scaled post-training quantization for vision transformer
Tai, Y.-S.; Lin, M.-G.; and Wu, A.-Y. A. 2023 · 2023
Closest in time.
Inpaint anything: Segment anything meets image inpainting
Yu, T.; Feng, R.; Feng, R.; Liu, J.; Jin, X.; Zeng, W.; and Chen, Z. 2023 · 2023
Closest in time.
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
Zhang, C.; Han, D.; Qiao, Y.; Kim, J. U.; Bae, S.-H.; Lee, S.; and Hong, C. S. 2023 · 2023
Closest in time.
Zhao, X.; Ding, W.; An, Y.; Du, Y.; Yu, T.; Li, M.; Tang, M.; and Wang, J. 2023 · 2023
Closest in time.
SlimSAM: 0.1% Data Makes Segment Anything Slim
Chen, Z.; Fang, G.; Ma, X.; and Wang, X. 2024 · 2024
Closest in time.
Efficientsam: Leveraged masked image pretraining for efficient segment anything
Xiong, Y.; Varadarajan, B.; Wu, L.; Xiang, X.; Xiao, F.; Zhu, C.; Dai, X.; Wang, D.; Sun, F.; Iandola, F.; et al. 2024 · 2024
Closest in time.