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Among the widely used parameter-efficient fine-tuning (PEFT) methods, LoRA and its variants have gained considerable popularity because of avoiding additional inference costs.
ReferItGame: Referring to objects in photographs of natural scenes
Kazemzadeh, S., Ordonez, V., Matten, M., and Berg, T · 2014
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Microsoft coco captions: Data collection and evaluation server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollár, P., and Zitnick, C. L · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Generation and comprehension of unambiguous object descriptions
Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A. L., and Murphy, K · 2016
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Weight normalization: a simple reparameterization to accelerate training of deep neural networks
Salimans, T. and Kingma, D. P · 2016
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., and Parikh, D · 2017
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al · 2017
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Vizwiz grand challenge: Answering visual questions from blind people
Gurari, D., Li, Q., Stangl, A. J., Guo, A., Lin, C., Grauman, K., Luo, J., and Bigham, J. P · 2018
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TVQA: Localized, compositional video question answering
Lei, J., Yu, L., Bansal, M., and Berg, T · 2018
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Towards automatic learning of procedures from web instructional videos
Zhou, L., Xu, C., and Corso, J · 2018
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Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Hudson, D. A. and Manning, C. D · 2019
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Ok-vqa: A visual question answering benchmark requiring external knowledge
Marino, K., Rastegari, M., Farhadi, A., and Mottaghi, R · 2019
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Ocr-vqa: Visual question answering by reading text in images
Mishra, A., Shekhar, S., Singh, A. K., and Chakraborty, A · 2019
Earlier work this paper cites.
Towards vqa models that can read
Singh, A., Natarajan, V., Shah, M., Jiang, Y., Chen, X., Batra, D., Parikh, D., and Rohrbach, M · 2019
Earlier work this paper cites.
A corpus for reasoning about natural language grounded in photographs
Suhr, A., Zhou, S., Zhang, A., Zhang, I., Bai, H., and Artzi, Y · 2019
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Tvr: A large-scale dataset for video-subtitle moment retrieval
Lei, J., Yu, L., Berg, T. L., and Bansal, M · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L · 2020
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HERO: Hierarchical encoder for Video+Language omni-representation pre-training
Li, L., Chen, Y.-C., Cheng, Y., Gan, Z., Yu, L., and Liu, J · 2020
Earlier work this paper cites.
Textcaps: a dataset for image captioning with reading comprehension
Sidorov, O., Hu, R., Rohrbach, M., and Singh, A · 2020
Cited alongside, same era.
Towards a unified view of parameter-efficient transfer learning
He, J., Zhou, C., Ma, X., Berg-Kirkpatrick, T., and Neubig, G · 2021
Cited alongside, same era.
Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks
Karimi Mahabadi, R., Ruder, S., Dehghani, M., and Henderson, J · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
Cited alongside, same era.
Value: A multi-task benchmark for video-and-language understanding evaluation
Li, L., Lei, J., Gan, Z., Yu, L., Chen, Y.-C., Pillai, R., Cheng, Y., Zhou, L., Wang, X. E., Wang, W. Y., et al · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Welcome to the openai platform, 2023
OpenAI · 2023
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Peng, B., Li, C., He, P., Galley, M., and Gao, J · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., and Rombach, R · 2023
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Is chatgpt a general-purpose natural language processing task solver?
Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., and Yang, D · 2023
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Controlling text-to-image diffusion by orthogonal finetuning
Qiu, Z., Liu, W., Feng, H., Xue, Y., Feng, Y., Liu, Z., Zhang, D., Weller, A., and Schölkopf, B · 2023
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Li, X. L. and Liang, P · 2021
Cited alongside, same era.
Compacter: Efficient low-rank hypercomplex adapter layers
mahabadi, R. K., Henderson, J., and Ruder, S · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Cited alongside, same era.
LoRA: Low-rank adaptation of large language models
Hu, E. J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
Cited alongside, same era.
Fedpara: Low-rank hadamard product for communication-efficient federated learning
Hyeon-Woo, N., Ye-Bin, M., and Oh, T.-H · 2022
Cited alongside, same era.
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Li, J., Li, D., Xiong, C., and Hoi, S · 2022
Cited alongside, same era.
Learn to explain: Multimodal reasoning via thought chains for science question answering
Lu, P., Mishra, S., Xia, T., Qiu, L., Chang, K.-W., Zhu, S.-C., Tafjord, O., Clark, P., and Kalyan, A · 2022
Cited alongside, same era.
Residual prompt tuning: improving prompt tuning with residual reparameterization
Razdaibiedina, A., Mao, Y., Khabsa, M., Lewis, M., Hou, R., Ba, J., and Almahairi, A · 2023
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Tied-lora: Enhacing parameter efficiency of lora with weight tying
Renduchintala, A., Konuk, T., and Kuchaiev, O · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., and Aberman, K · 2023
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Stanford alpaca: An instruction-following llama model, 2023
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
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Wang, Y., Wu, J., Dabral, T., Zhang, J., Brown, G., Lu, C.-T., Liu, F., Liang, Y., Pang, B., Bendersky, M., et al · 2023
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Navigating text-to-image customization: From lycoris fine-tuning to model evaluation
Yeh, S.-Y., Hsieh, Y.-G., Gao, Z., Yang, B. B., Oh, G., and Gong, Y · 2023
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Adaptive budget allocation for parameter-efficient fine-tuning
Zhang, Q., Chen, M., Bukharin, A., He, P., Cheng, Y., Chen, W., and Zhao, T · 2023
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Pytorch fsdp: experiences on scaling fully sharded data parallel
Zhao, Y., Gu, A., Varma, R., Luo, L., Huang, C.-C., Xu, M., Wright, L., Shojanazeri, H., Ott, M., Shleifer, S., et al · 2023
Later among the works it cites.
Judging LLM-as-a-judge with MT-bench and chatbot arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., Zhang, H., Gonzalez, J. E., and Stoica, I · 2023
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Efficient finetuning of llama 3 with fsdp qdora
Kerem Turgutlu, Jonathan Whitaker, J. H · 2024
Closest in time.
Vera: Vector-based random matrix adaptation
Kopiczko, D. J., Blankevoort, T., and Asano, Y. M · 2024
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Orca-math: Unlocking the potential of slms in grade school math, 2024
Mitra, A., Khanpour, H., Rosset, C., and Awadallah, A · 2024
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