擬似教師ありキャプション生成における部分的不一致の除去
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| Published in | 人工知能学会論文誌 Vol. 37; no. 2; pp. H-L82_1 - 12 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
一般社団法人 人工知能学会
01.03.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1346-0714 1346-8030 |
| DOI | 10.1527/tjsai.37-2_H-L82 |
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| Author | 松本, 裕治 渡辺, 太郎 本多, 右京 橋本, 敦史 |
|---|---|
| Author_xml | – sequence: 1 fullname: 本多, 右京 organization: 理化学研究所AIP – sequence: 1 fullname: 渡辺, 太郎 organization: 奈良先端科学技術大学院大学 – sequence: 1 fullname: 松本, 裕治 organization: 理化学研究所AIP – sequence: 1 fullname: 橋本, 敦史 organization: オムロンサイニックエックス株式会社 |
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| ContentType | Journal Article |
| Copyright | 人工知能学会2022 |
| Copyright_xml | – notice: 人工知能学会2022 |
| DOI | 10.1527/tjsai.37-2_H-L82 |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1346-8030 |
| EndPage | 12 |
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| Publisher | 一般社団法人 人工知能学会 |
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H., and Barzilay, R.: Capwap: Captioning with a purpose, in EMNLP, pp. 8755–8768 (2020) [Szegedy 17] Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A.: Inception-v4, inception-resnet and the impact of residual connections on learning, in AAAI, pp. 4278–4284 (2017) [Choi 20] Choi, B.-J., Hong, J., Park, D., and Lee, S. W.: Fˆ2-softmax: Diversifying neural text generation via frequency factorized softmax, in EMNLP, pp. 9167–9182 (2020) [Xu 15] Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y.: Show, attend and tell: Neural image caption generation with visual attention, in ICML, pp. 2048–2057 (2015) [He 16a] He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learn-ing for image recognition, in CVPR, pp. 770–778 (2016) [Russakovsky 15] Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L.: Imagenet large scale visual recognition challenge, IJCV, Vol. 115, No. 3, pp. 211–252 (2015) [Laina 19] Laina, I., Rupprecht, C., and Navab, N.: Towards unsupervised image captioning with shared multimodal embeddings, in ICCV, pp. 7414–7424 (2019) [Cho 14] Cho, K., Merrie ̈nboer, van B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y.: Learning phrase representations using RNN encoder–decoder for statistical machine translation, in EMNLP, pp. 1724–1734 (2014) [Chen 15] Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dolla ́r, P., and Zitnick, C. L.: Microsoft coco captions: Data collection and evaluation server, arXiv preprint arXiv:1504.00325 (2015) [Goodfellow14] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y.: Generative adversarial nets, in NeurIPS, Vol. 27, pp. 2672–2680 (2014) [Lin 04] Lin, C.-Y.: ROUGE: A package for automatic evaluation of summaries, in Text Summarization Branches Out, pp. 74–81 (2004) [Anderson 18a] Anderson, P., Gould, S., and Johnson, M.: Partially-supervised image captioning, in NeurIPS, Vol. 31, pp. 1875–1886 (2018) [Liu 19] Liu, F., Gao, M., Zhang, T., and Zou, Y.: Exploring semantic relationships for image captioning without parallel data, in ICDM, pp. 439–448 (2019) [Anderson16] Anderson, P., Fernando, B., Johnson, M., and Gould, S.: SPICE: Semantic propositional image caption evaluation, in ECCV, pp. 382–398 (2016) [Gu 19] Gu, J., Joty, S., Cai, J., Zhao, H., Yang, X., and Wang, G.: Unpaired image captioning via scene graph alignments, in ICCV, pp. 10323–10332 (2019) [Kingma 15] Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, in ICLR (2015) [Honda 21] Honda, U., Ushiku, Y., Hashimoto, A., Watanabe, T., and Matsumoto, Y.: Removing word-level spurious alignment between images and pseudo-captions in unsupervised image captioning, in EACL, pp. 3692–3702 (2021) [Zhang 20] Zhang, R., Chen, C., Zhang, X., Bai, K., and Carin, L.: Semantic matching for sequence-to-sequence learning, in Findings of ACL: EMNLP 2020, pp. 212–222 (2020) [Ren 15] Ren, S., He, K., Girshick, R., and Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks, in NeurIPS, Vol. 28, pp. 91–99 (2015) [Gurari 20] Gurari, D., Zhao, Y., Zhang, M., and Bhattacharya, N.: Captioning images taken by people who are blind, in ECCV, pp. 417–434 (2020) [Hendricks 16] Hendricks, L. A., Venugopalan, S., Rohrbach, M., Mooney, R., Saenko, K., and Darrell, T.: Deep compositional captioning: Describing novel object categories without paired training data, in CVPR, pp. 1–10 (2016) [Denkowski 14] Denkowski, M. and Lavie, A.: METEOR universal: Language specific translation evaluation for any target language, in Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 376–380 (2014) [Lin 14] Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ra-manan, D., Dolla ́r, P., and Zitnick, C. L.: Microsoft COCO: Common objects in context, in ECCV, pp. 740–755 (2014) [Feng 19] Feng, Y., Ma, L., Liu, W., and Luo, J.: Unsupervised image captioning, in CVPR, pp. 4125–4134 (2019) [Sharma 18] Sharma, P., Ding, N., Goodman, S., and Soricut, R.: Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning, in ACL, pp. 2556–2565 (2018) [Karpathy 15] Karpathy, A. and Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions, in CVPR, pp. 3128–3137 (2015) [Guo 20] Guo, D., Wang, Y., Song, P., and Wang, M.: Recurrent relational memory network for unsupervised image captioning, in IJCAI, pp. 920–926 (2020) [Demeter 20] Demeter, D., Kimmel, G., and Downey, D.: Stolen probability: A structural weakness of neural language models, in ACL, pp. 2191–2197 (2020) [Huang 17] Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al.: Speed/accuracy trade-offs for modern convolutional object detectors, in CVPR, pp. 7310–7311 (2017) [Vedantam 15] Vedantam, R., Lawrence Zitnick, C., and Parikh, D.: CIDEr: Consensus-based image description evaluation, in CVPR, pp. 4566–4575 (2015) [Liu 18] Liu, X., Li, H., Shao, J., Chen, D., and Wang, X.: Show, tell and discriminate: Image captioning by self-retrieval with partially labeled data, in ECCV, pp. 338–354 (2018) [Venugopalan 17] Venugopalan, S., Anne Hendricks, L., Rohrbach, M., Mooney, R., Darrell, T., and Saenko, K.: Captioning images with diverse objects, in CVPR, pp. 5753–5761 (2017) [Cao 20] Cao, S., An, G., Zheng, Z., and Ruan, Q.: Interactions guided generative adversarial network for unsupervised image captioning, Neurocomputing, Vol. 417, pp. 419–431 (2020) [Kim 20] Kim, H., Tang, Z., and Bansal, M.: Dense-caption matching and frame-selection gating for temporal localization in videoqa, in ACL, pp. 4812–4822 (2020) [Nikolaus 19] Nikolaus, M., Abdou, M., Lamm, M., Aralikatte, R., and Elliott, D.: Compositional generalization in image captioning, in CoNLL, pp. 87–98 (2019) [Wang 20] Wang, J., Xu, W., Wang, Q., and Chan, A. B.: Compare and reweight: Distinctive image captioning using similar images sets, in ECCV, pp. 370–386 (2020) [Kuznetsova 20] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., Duerig, T., and Ferrari, V.: The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale, IJCV, Vol. 128, No. 7, pp. 1956–1981 (2020) [Vinyals 15] Vinyals, O., Toshev, A., Bengio, S., and Erhan, D.: Show and tell: A neural image caption generator, in CVPR, pp. 3156–3164 (2015) [Gu 18] Gu, J., Joty, S., Cai, J., and Wang, G.: Unpaired image captioning by language pivoting, in ECCV, pp. 503–519 (2018) [Kim 19] Kim, D.-J., Choi, J., Oh, T.-H., and Kweon, I. S.: Image captioning with very scarce supervised data: Adversarial semi-supervised learning approach, in EMNLP-IJCNLP, pp. 2012–2023 (2019) [Song 19] Song, Y., Chen, S., Zhao, Y., and Jin, Q.: Unpaired cross-lingual image caption generation with self-supervised rewards, in ACMMM, pp. 784–792 (2019) [Hochreiter 97] Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Computation, Vol. 9, No. 8, pp. 1735–1780 (1997) [Krishna 17] Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., Bernstein, M., and Fei-Fei, L.: Visual genome: Connecting language and vision using crowdsourced dense image annotations, IJCV, Vol. 123, No. 1, pp. 32–73 (2017) [Young 14] Young, P., Lai, A., Hodosh, M., and Hockenmaier, J.: From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions, TACL, Vol. 2, pp. 67–78 (2014) [Welleck 20] Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., and Weston, J.: Neural text generation with unlikelihood training, in ICLR (2020) [Holtzman 20] Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y.: The curious case of neural text degeneration, in ICLR (2020) [Krasin 17] Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., Kuznetsova, A., Rom, H., Uijlings, J., Popov, S., Veit, A., Belongie, S., Gomes, V., Gupta, A., Sun, C., Chechik, G., Cai, D., Feng, Z., Narayanan, D., and Murphy, K.: Openimages: A public dataset for large-scale multi-label and multi-class image classification., Dataset available from https://github.com/openimages (2017) [Lu 18] Lu, J., Yang, J., Batra, D., and Parikh, D.: Neural baby talk, in CVPR, pp. 7219–7228 (2018) |
| References_xml | – reference: [Kingma 15] Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, in ICLR (2015) – reference: [Krishna 17] Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., Bernstein, M., and Fei-Fei, L.: Visual genome: Connecting language and vision using crowdsourced dense image annotations, IJCV, Vol. 123, No. 1, pp. 32–73 (2017) – reference: [Liu 19] Liu, F., Gao, M., Zhang, T., and Zou, Y.: Exploring semantic relationships for image captioning without parallel data, in ICDM, pp. 439–448 (2019) – reference: [Lu 17] Lu, J., Xiong, C., Parikh, D., and Socher, R.: Knowing when to look: Adaptive attention via a visual sentinel for image captioning, in CVPR, pp. 375–383 (2017) – reference: [Choi 20] Choi, B.-J., Hong, J., Park, D., and Lee, S. W.: Fˆ2-softmax: Diversifying neural text generation via frequency factorized softmax, in EMNLP, pp. 9167–9182 (2020) – reference: [Cao 20] Cao, S., An, G., Zheng, Z., and Ruan, Q.: Interactions guided generative adversarial network for unsupervised image captioning, Neurocomputing, Vol. 417, pp. 419–431 (2020) – reference: [Zhang 20] Zhang, R., Chen, C., Zhang, X., Bai, K., and Carin, L.: Semantic matching for sequence-to-sequence learning, in Findings of ACL: EMNLP 2020, pp. 212–222 (2020) – reference: [Fisch 20] Fisch, A., Lee, K., Chang, M.-W., Clark, J. H., and Barzilay, R.: Capwap: Captioning with a purpose, in EMNLP, pp. 8755–8768 (2020) – reference: [Karpathy 15] Karpathy, A. and Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions, in CVPR, pp. 3128–3137 (2015) – reference: [Gurari 20] Gurari, D., Zhao, Y., Zhang, M., and Bhattacharya, N.: Captioning images taken by people who are blind, in ECCV, pp. 417–434 (2020) – reference: [Lu 18] Lu, J., Yang, J., Batra, D., and Parikh, D.: Neural baby talk, in CVPR, pp. 7219–7228 (2018) – reference: [Kim 20] Kim, H., Tang, Z., and Bansal, M.: Dense-caption matching and frame-selection gating for temporal localization in videoqa, in ACL, pp. 4812–4822 (2020) – reference: [Lin 14] Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ra-manan, D., Dolla ́r, P., and Zitnick, C. L.: Microsoft COCO: Common objects in context, in ECCV, pp. 740–755 (2014) – reference: [Song 19] Song, Y., Chen, S., Zhao, Y., and Jin, Q.: Unpaired cross-lingual image caption generation with self-supervised rewards, in ACMMM, pp. 784–792 (2019) – reference: [Kuznetsova 20] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., Duerig, T., and Ferrari, V.: The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale, IJCV, Vol. 128, No. 7, pp. 1956–1981 (2020) – reference: [Huang 17] Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al.: Speed/accuracy trade-offs for modern convolutional object detectors, in CVPR, pp. 7310–7311 (2017) – reference: [Vinyals 15] Vinyals, O., Toshev, A., Bengio, S., and Erhan, D.: Show and tell: A neural image caption generator, in CVPR, pp. 3156–3164 (2015) – reference: [Young 14] Young, P., Lai, A., Hodosh, M., and Hockenmaier, J.: From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions, TACL, Vol. 2, pp. 67–78 (2014) – reference: [Sharma 18] Sharma, P., Ding, N., Goodman, S., and Soricut, R.: Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning, in ACL, pp. 2556–2565 (2018) – reference: [Agrawal 19] Agrawal, H., Desai, K., Wang, Y., Chen, X., Jain, R., Johnson, M., Batra, D., Parikh, D., Lee, S., and Anderson, P.: nocaps: novel object captioning at scale, in ICCV, pp. 8948–8957 (2019) – reference: [Goodfellow14] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y.: Generative adversarial nets, in NeurIPS, Vol. 27, pp. 2672–2680 (2014) – reference: [Russakovsky 15] Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L.: Imagenet large scale visual recognition challenge, IJCV, Vol. 115, No. 3, pp. 211–252 (2015) – reference: [Wang 20] Wang, J., Xu, W., Wang, Q., and Chan, A. B.: Compare and reweight: Distinctive image captioning using similar images sets, in ECCV, pp. 370–386 (2020) – reference: [Lin 04] Lin, C.-Y.: ROUGE: A package for automatic evaluation of summaries, in Text Summarization Branches Out, pp. 74–81 (2004) – reference: [Venugopalan 17] Venugopalan, S., Anne Hendricks, L., Rohrbach, M., Mooney, R., Darrell, T., and Saenko, K.: Captioning images with diverse objects, in CVPR, pp. 5753–5761 (2017) – reference: [Laina 19] Laina, I., Rupprecht, C., and Navab, N.: Towards unsupervised image captioning with shared multimodal embeddings, in ICCV, pp. 7414–7424 (2019) – reference: [Hochreiter 97] Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Computation, Vol. 9, No. 8, pp. 1735–1780 (1997) – reference: [Denkowski 14] Denkowski, M. and Lavie, A.: METEOR universal: Language specific translation evaluation for any target language, in Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 376–380 (2014) – reference: [Holtzman 20] Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y.: The curious case of neural text degeneration, in ICLR (2020) – reference: [Anderson16] Anderson, P., Fernando, B., Johnson, M., and Gould, S.: SPICE: Semantic propositional image caption evaluation, in ECCV, pp. 382–398 (2016) – reference: [Krasin 17] Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., Kuznetsova, A., Rom, H., Uijlings, J., Popov, S., Veit, A., Belongie, S., Gomes, V., Gupta, A., Sun, C., Chechik, G., Cai, D., Feng, Z., Narayanan, D., and Murphy, K.: Openimages: A public dataset for large-scale multi-label and multi-class image classification., Dataset available from https://github.com/openimages (2017) – reference: [Guo 20] Guo, D., Wang, Y., Song, P., and Wang, M.: Recurrent relational memory network for unsupervised image captioning, in IJCAI, pp. 920–926 (2020) – reference: [Kim 19] Kim, D.-J., Choi, J., Oh, T.-H., and Kweon, I. S.: Image captioning with very scarce supervised data: Adversarial semi-supervised learning approach, in EMNLP-IJCNLP, pp. 2012–2023 (2019) – reference: [Vedantam 15] Vedantam, R., Lawrence Zitnick, C., and Parikh, D.: CIDEr: Consensus-based image description evaluation, in CVPR, pp. 4566–4575 (2015) – reference: [Papineni 02] Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J.: BLEU: A method for automatic evaluation of machine translation, in ACL, pp. 311–318 (2002) – reference: [Szegedy 17] Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A.: Inception-v4, inception-resnet and the impact of residual connections on learning, in AAAI, pp. 4278–4284 (2017) – reference: [Honda 21] Honda, U., Ushiku, Y., Hashimoto, A., Watanabe, T., and Matsumoto, Y.: Removing word-level spurious alignment between images and pseudo-captions in unsupervised image captioning, in EACL, pp. 3692–3702 (2021) – reference: [Nikolaus 19] Nikolaus, M., Abdou, M., Lamm, M., Aralikatte, R., and Elliott, D.: Compositional generalization in image captioning, in CoNLL, pp. 87–98 (2019) – reference: [Gu 19] Gu, J., Joty, S., Cai, J., Zhao, H., Yang, X., and Wang, G.: Unpaired image captioning via scene graph alignments, in ICCV, pp. 10323–10332 (2019) – reference: [He 16b] He, K., Zhang, X., Ren, S., and Sun, J.: Identity mappings in deep residual networks, in ECCV, pp. 630–645 (2016) – reference: [Demeter 20] Demeter, D., Kimmel, G., and Downey, D.: Stolen probability: A structural weakness of neural language models, in ACL, pp. 2191–2197 (2020) – reference: [Hendricks 16] Hendricks, L. A., Venugopalan, S., Rohrbach, M., Mooney, R., Saenko, K., and Darrell, T.: Deep compositional captioning: Describing novel object categories without paired training data, in CVPR, pp. 1–10 (2016) – reference: [Cho 14] Cho, K., Merrie ̈nboer, van B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y.: Learning phrase representations using RNN encoder–decoder for statistical machine translation, in EMNLP, pp. 1724–1734 (2014) – reference: [Chen 15] Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dolla ́r, P., and Zitnick, C. L.: Microsoft coco captions: Data collection and evaluation server, arXiv preprint arXiv:1504.00325 (2015) – reference: [Anderson 18b] Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., and Zhang, L.: Bottom-up and top-down attention for image captioning and visual question answering, in CVPR, pp. 6077–6086 (2018) – reference: [Feng 19] Feng, Y., Ma, L., Liu, W., and Luo, J.: Unsupervised image captioning, in CVPR, pp. 4125–4134 (2019) – reference: [Gu 18] Gu, J., Joty, S., Cai, J., and Wang, G.: Unpaired image captioning by language pivoting, in ECCV, pp. 503–519 (2018) – reference: [He 16a] He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learn-ing for image recognition, in CVPR, pp. 770–778 (2016) – reference: [Liu 18] Liu, X., Li, H., Shao, J., Chen, D., and Wang, X.: Show, tell and discriminate: Image captioning by self-retrieval with partially labeled data, in ECCV, pp. 338–354 (2018) – reference: [Ren 15] Ren, S., He, K., Girshick, R., and Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks, in NeurIPS, Vol. 28, pp. 91–99 (2015) – reference: [Xu 15] Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y.: Show, attend and tell: Neural image caption generation with visual attention, in ICML, pp. 2048–2057 (2015) – reference: [Anderson 18a] Anderson, P., Gould, S., and Johnson, M.: Partially-supervised image captioning, in NeurIPS, Vol. 31, pp. 1875–1886 (2018) – reference: [Welleck 20] Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., and Weston, J.: Neural text generation with unlikelihood training, in ICLR (2020) |
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| SourceID | jstage |
| SourceType | Publisher |
| StartPage | H-L82_1 |
| SubjectTerms | image captioning pseudo-label unsupervised learning vision and language |
| Title | 擬似教師ありキャプション生成における部分的不一致の除去 |
| URI | https://www.jstage.jst.go.jp/article/tjsai/37/2/37_37-2_H-L82/_article/-char/ja |
| Volume | 37 |
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| ispartofPNX | 人工知能学会論文誌, 2022/03/01, Vol.37(2), pp.H-L82_1-12 |
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