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The emergence of Vision Language Models (VLMs) is a significant advancement in integrating computer vision with Large Language Models (LLMs) to produce detailed text descriptions based on visual inputs, yet it introduces new security vulnerabilities.
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics. pp. 311–318 (2002)
2002
Earlier work this paper cites.
Chin-Yew, L.: Rouge: A package for automatic evaluation of summaries. In: Proceedings of the Workshop on Text Summarization Branches Out, 2004 (2004)
2004
Earlier work this paper cites.
Banerjee, S., Lavie, A.: Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In: Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization. pp. 65–72 (2005)
2005
Earlier work this paper cites.
Hodosh, M., Young, P., Hockenmaier, J.: Framing image description as a ranking task: Data, models and evaluation metrics. Journal of Artificial Intelligence Research 47
2013
Earlier work this paper cites.
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: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Young, P., Lai, A., Hodosh, M., Hockenmaier, J.: From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. Transactions of the Association for Computational Linguistics 2
2014
Earlier work this paper cites.
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., Parikh, D.: Vqa: Visual question answering. In: Proceedings of the IEEE international conference on computer vision. pp. 2425–2433 (2015)
2015
Earlier work this paper cites.
Vedantam, R., Lawrence Zitnick, C., Parikh, D.: Cider: Consensus-based image description evaluation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4566–4575 (2015)
2015
Earlier work this paper cites.
2017
Earlier work this paper cites.
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., Parikh, D.: Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 6904–6913 (2017)
2017
Earlier work this paper cites.
Gu, T., Dolan-Gavitt, B., BadNets, S.: Identifying vulnerabilities in the machine learning model supply chain. In: Proceedings of the Neural Information Processing Symposium Workshop Mach. Learning Security (MLSec). pp. 1–5 (2017)
2017
Earlier work this paper cites.
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. pp. 618–626 (2017)
2017
Earlier work this paper cites.
Marino, K., Rastegari, M., Farhadi, A., Mottaghi, R.: Ok-vqa: A visual question answering benchmark requiring external knowledge. In: Proceedings of the IEEE/cvf conference on computer vision and pattern recognition. pp. 3195–3204 (2019)
2019
Earlier work this paper cites.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1
2019
Earlier work this paper cites.
2021
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Cited alongside, same era.
Alayrac, J.B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al.: Flamingo: a visual language model for few-shot learning. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
Cui, G., Yuan, L., He, B., Chen, Y., Liu, Z., Sun, M.: A unified evaluation of textual backdoor learning: Frameworks and benchmarks. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
Jia, J., Liu, Y., Gong, N.Z.: Badencoder: Backdoor attacks to pre-trained encoders in self-supervised learning. In: 2022 IEEE Symposium on Security and Privacy (SP). pp. 2043–2059. IEEE (2022)
2022
2023
Later among the works it cites.
Lyu, W., Zheng, S., Ling, H., Chen, C.: Backdoor attacks against transformers with attention enhancement. In: ICLR 2023 Workshop on Backdoor Attacks and Defenses in Machine Learning (2023)
2023
Later among the works it cites.
Lyu, W., Zheng, S., Pang, L., Ling, H., Chen, C.: Attention-enhancing backdoor attacks against bert-based models. In: Findings of the Association for Computational Linguistics: EMNLP 2023. pp. 10672–10690 (2023)
2023
Later among the works it cites.
Miao, Q., Hoai, M., Samaras, D.: Patch-level gaze distribution prediction for gaze following. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 880–889 (2023)
2023
Later among the works it cites.
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Cited alongside, same era.
Kwon, H., Lee, S.: Toward backdoor attacks for image captioning model in deep neural networks. Security and Communication Networks 2022
2022
Cited alongside, same era.
Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning. pp. 12888–12900. PMLR (2022)
2022
Cited alongside, same era.
Li, M., Zhong, N., Zhang, X., Qian, Z., Li, S.: Object-oriented backdoor attack against image captioning. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 2864–2868. IEEE (2022)
2022
Cited alongside, same era.
Li, Y., Jiang, Y., Li, Z., Xia, S.T.: Backdoor learning: A survey. IEEE Transactions on Neural Networks and Learning Systems (2022)
2022
Cited alongside, same era.
Lyu, W., Dong, X., Wong, R., Zheng, S., Abell-Hart, K., Wang, F., Chen, C.: A multimodal transformer: Fusing clinical notes with structured ehr data for interpretable in-hospital mortality prediction. In: AMIA Annual Symposium Proceedings. vol. 2022, p. 719. American Medical Informatics Association (2022)
2022
Cited alongside, same era.
Lyu, W., Zheng, S., Ma, T., Chen, C.: A study of the attention abnormality in trojaned berts. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 4727–4741 (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Walmer, M., Sikka, K., Sur, I., Shrivastava, A., Jha, S.: Dual-key multimodal backdoors for visual question answering. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition. pp. 15375–15385 (2022)
2022
Cited alongside, same era.
OpenAI: Gpt-4v(ision) system card. https://cdn.openai.com/papers/GPTV_System_Card.pdf (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Yang, Z., He, X., Li, Z., Backes, M., Humbert, M., Berrang, P., Zhang, Y.: Data poisoning attacks against multimodal encoders. In: International Conference on Machine Learning. pp. 39299–39313. PMLR (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhu, D., Li, Y., Yuan, J., Li, Z., Kuang, K., Wu, C.: Universal domain adaptation via compressive attention matching. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6974–6985 (2023)
2023
Later among the works it cites.
Lai, Z., Bai, H., Zhang, H., Du, X., Shan, J., Yang, Y., Chuah, C.N., Cao, M.: Empowering unsupervised domain adaptation with large-scale pre-trained vision-language models. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 2691–2701 (2024)
2024
Closest in time.
Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Advances in neural information processing systems 36
2024
Closest in time.
2024
Closest in time.
Lyu, W., Lin, X., Zheng, S., Pang, L., Ling, H., Jha, S., Chen, C.: Task-agnostic detector for insertion-based backdoor attacks. In: Findings of the Association for Computational Linguistics: NAACL 2024. pp. 2808–2822 (2024)
2024
Closest in time.
Sun, S., Ren, W., Li, J., Wang, R., Cao, X.: Logit standardization in knowledge distillation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15731–15740 (2024)
2024
Closest in time.