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Motivated by the Parameter-Efficient Fine-Tuning (PEFT) in large language models, we propose LoRAT, a method that unveils the power of large ViT model for tracking within laboratory-level resources.
2013
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Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 37
2014
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Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: Common objects in context. In: ECCV (2014)
2014
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Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence 37
2015
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Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: ECCV Workshops (2016)
2016
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
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Kristan, M., Matas, J., Leonardis, A., Vojíř, T., Pflugfelder, R., Fernández, G., Nebehay, G., Porikli, F., Čehovin, L.: A novel performance evaluation methodology for single-target trackers. IEEE Transactions on Pattern Analysis and Machine Intelligence 38
2016
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Larsson, G., Maire, M., Shakhnarovich, G.: FractalNet: Ultra-deep neural networks without residuals. In: ICLR (2016)
2016
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Mueller, M., Smith, N., Ghanem, B.: A benchmark and simulator for uav tracking. In: ECCV (2016)
2016
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Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: Eco: Efficient convolution operators for tracking. In: CVPR (2017)
2017
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Kiani Galoogahi, H., Fagg, A., Huang, C., Ramanan, D., Lucey, S.: Need for speed: A benchmark for higher frame rate object tracking. In: ICCV (2017)
2017
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2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: NIPS (2017)
2017
Earlier work this paper cites.
Muller, M., Bibi, A., Giancola, S., Alsubaihi, S., Ghanem, B.: TrackingNet: A large-scale dataset and benchmark for object tracking in the wild. In: ECCV (2018)
2018
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Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. In: NAACL (2018)
2018
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Bhat, G., Danelljan, M., Gool, L.V., Timofte, R.: Learning discriminative model prediction for tracking. In: ICCV (2019)
2019
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: NAACL (2019)
2019
Earlier work this paper cites.
Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Bai, H., Xu, Y., Liao, C., Ling, H.: LaSOT: A high-quality benchmark for large-scale single object tracking. In: CVPR (2019)
2019
Earlier work this paper cites.
Fan, H., Ling, H.: Siamese cascaded region proposal networks for real-time visual tracking. In: CVPR (2019)
2019
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Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., Gelly, S.: Parameter-efficient transfer learning for nlp. In: ICML (2019)
2019
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Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: Siamrpn++: Evolution of siamese visual tracking with very deep networks. In: CVPR (2019)
2019
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Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2019)
2019
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Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: CVPR (2019)
2019
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Tian, Z., Shen, C., Chen, H., He, T.: FCOS: Fully convolutional one-stage object detection. In: ICCV (2019)
2019
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. In: NeurIPS (2020)
2020
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Danelljan, M., Gool, L.V., Timofte, R.: Probabilistic regression for visual tracking. In: CVPR (2020)
2020
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Lin, Z., Madotto, A., Fung, P.: Exploring versatile generative language model via parameter-efficient transfer learning. In: Findings of the Association for Computational Linguistics: EMNLP 2020 (2020)
2020
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Zhang, Z., Peng, H., Fu, J., Li, B., Hu, W.: Ocean: Object-aware anchor-free tracking. In: ECCV (2020)
2020
Cited alongside, same era.
Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., Lu, H.: Transformer tracking. In: CVPR (2021)
2021
Cited alongside, same era.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR (2021)
2021
Cited alongside, same era.
Fan, H., Bai, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Huang, M., Liu, J., Xu, Y., et al.: LaSOT: A high-quality large-scale single object tracking benchmark. International Journal of Computer Vision 129
2021
Cited alongside, same era.
Huang, L., Zhao, X., Huang, K.: Got-10k: A large high-diversity benchmark for generic object tracking in the wild. IEEE Transactions on Pattern Analysis and Machine Intelligence 43
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: CVPR (2022)
2022
Later among the works it cites.
Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: LoRA: Low-rank adaptation of large language models. In: ICLR (2022)
2022
Later among the works it cites.
Jia, M., Tang, L., Chen, B.C., Cardie, C., Belongie, S., Hariharan, B., Lim, S.N.: Visual prompt tuning. In: ECCV (2022)
2022
Later among the works it cites.
Lin, L., Fan, H., Zhang, Z., Xu, Y., Ling, H.: SwinTrack: A simple and strong baseline for transformer tracking. In: NeurIPS (2022)
2022
Later among the works it cites.
Liu, H., Tam, D., Mohammed, M., Mohta, J., Huang, T., Bansal, M., Raffel, C.: Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. In: NeurIPS (2022)
2022
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2021
Cited alongside, same era.
Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
2021
Cited alongside, same era.
Li, X.L., Liang, P.: Prefix-tuning: Optimizing continuous prompts for generation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: ICCV (2021)
2021
Cited alongside, same era.
Mayer, C., Danelljan, M., Paudel, D.P., Van Gool, L.: Learning target candidate association to keep track of what not to track. In: ICCV (2021)
2021
Cited alongside, same era.
Pfeiffer, J., Kamath, A., Rücklé, A., Cho, K., Gurevych, I.: AdapterFusion: Non-destructive task composition for transfer learning. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Later among the works it cites.
Liu, X., Ji, K., Fu, Y., Tam, W., Du, Z., Yang, Z., Tang, J.: P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (2022)
2022
Later among the works it cites.
Touvron, H., Cord, M., Jégou, H.: DeiT III: Revenge of the ViT. In: ECCV. Springer (2022)
2022
Later among the works it cites.
Xie, F., Wang, C., Wang, G., Cao, Y., Yang, W., Zeng, W.: Correlation-aware deep tracking. In: CVPR (2022)
2022
Later among the works it cites.
Ye, B., Chang, H., Ma, B., Shan, S., Chen, X.: Joint feature learning and relation modeling for tracking: A one-stream framework. In: ECCV (2022)
2022
Later among the works it cites.
Zhang, R., Zhang, W., Fang, R., Gao, P., Li, K., Dai, J., Qiao, Y., Li, H.: Tip-adapter: Training-free adaption of clip for few-shot classification. In: ECCV (2022)
2022
Later among the works it cites.
Cai, Y., Liu, J., Tang, J., Wu, G.: Robust object modeling for visual tracking. In: ICCV (2023)
2023
Later among the works it cites.
Chen, X., Peng, H., Wang, D., Lu, H., Hu, H.: SeqTrack: Sequence to sequence learning for visual object tracking. In: CVPR (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Gao, S., Zhou, C., Zhang, J.: Generalized relation modeling for transformer tracking. In: CVPR (2023)
2023
Later among the works it cites.
Li, X., Huang, Y., He, Z., Wang, Y., Lu, H., Yang, M.H.: CiteTracker: Correlating image and text for visual tracking. In: ICCV (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Wei, X., Bai, Y., Zheng, Y., Shi, D., Gong, Y.: Autoregressive visual tracking. In: CVPR (2023)
2023
Later among the works it cites.
Wu, Q., Yang, T., Liu, Z., Wu, B., Shan, Y., Chan, A.B.: DropMAE: Masked autoencoders with spatial-attention dropout for tracking tasks. In: CVPR (2023)
2023
Later among the works it cites.
Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: ICCV (2023)
2023
Later among the works it cites.
Zhang, Q., Chen, M., Bukharin, A., He, P., Cheng, Y., Chen, W., Zhao, T.: Adalora: Adaptive budget allocation for parameter-efficient fine-tuning. In: ICLR (2023)
2023
Later among the works it cites.
Cui, Y., Jiang, C., Wu, G., Wang, L.: MixFormer: End-to-end tracking with iterative mixed attention. IEEE Transactions on Pattern Analysis and Machine Intelligence pp. 1–18 (2024)
2024
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2024
Closest in time.
Zhang, R., Han, J., Liu, C., Zhou, A., Lu, P., Li, H., Gao, P., Qiao, Y.: LLaMA-adapter: Efficient fine-tuning of large language models with zero-initialized attention. In: ICLR (2024)
2024
Closest in time.