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REVIEW 2 major objections 4 minor 65 cited by

LXMERT: Learning Cross-Modality Encoder Representations from Transformers

T0 review · 2 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A three-encoder transformer pretrained on image-sentence pairs learns reusable vision-language representations and sets high scores on VQA, GQA, and NLVR2.

desk verdict LXMERT is a genuinely influential cross-modal pretraining paper whose VQA 'state-of-the-art' claim is time-stamped to May 2019 and its own footnote shows MCAN later passed it, but that nuance doesn't shake the architecture, ablations, or the striking NLVR2 result. read the letter →

arxiv 1908.07490 v3 pith:FPIGQHMJ submitted 2019-08-20 cs.CL cs.CVcs.LG

Hao Tan , Mohit Bansal This is my paper · ORCID
classification cs.CLcs.CVcs.LG
keywords visualquestionansweringcross-modalitypretrainingtransformervisionandlanguageobjectrelationshipencodingmultimodalrepresentationlearningNLVR2LXMERT
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that a single model trained jointly on language, vision, and the alignment between them can become a general-purpose engine for vision-and-language reasoning. It introduces LXMERT, a transformer with three encoders, and pretrains it on 9.18 million image-sentence pairs with five objectives: masked language modeling, masked object prediction (feature regression and label classification), cross-modality matching, and image question answering. After fine-tuning, the model reaches 72.5% on VQA test-standard, 60.3% on GQA, and 76.2% on NLVR2 Test-U, a 22-point absolute gain over the previous best result on NLVR2. The paper's ablations show that the cross-modality architecture and each pretraining task contribute to those gains.

What carries the argument

The load-bearing object is LXMERT's three-encoder transformer: a language encoder, an object-relationship encoder over detected region features, and a cross-modality encoder whose bidirectional cross-attention sub-layers align words to objects and objects to words. Five pretraining tasks push the same weights: masked cross-modality language modeling, masked object prediction through both RoI-feature regression and detected-label classification, cross-modality matching, and image question answering. Together these tasks force the model to predict a missing piece of one modality using evidence from the other, which is what makes the learned alignments transferable to new tasks.

What would settle it

Replace the bidirectional cross-attention sub-layers in the cross-modality encoder with a simple concatenation of the two single-modality outputs, keeping the same five pretraining tasks; if that model still reaches 76.2% on NLVR2, the paper's claim that cross-attention alignment is essential to the transfer would be undercut.

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Extended reading notes

Core claim

LXMERT's central claim is that cross-modality pretraining, not just single-modality pretraining, is what lets a model answer questions about images and judge whether sentences describe images. The model takes a sentence and a fixed set of 36 detected image regions, encodes each modality with its own transformer, and then lets the two streams exchange information through bidirectional cross-attention layers. Pretraining the whole stack on image-caption and image-question pairs lets masked words be filled in from visual evidence and masked objects be predicted from language, so the learned representations contain both intra-modality and cross-modality structure. The paper reports that this yields the highest published results on VQA and GQA and a 22% absolute improvement on NLVR2 over the previous best, and that removing the cross-modality encoder or any of the pretraining tasks lowers accuracy.

Load-bearing premise

The whole model sees an image only through 36 regions produced by a frozen Faster R-CNN detector trained on Visual Genome; if those regions miss fine-grained details or relations the questions need, no amount of cross-modality pretraining can recover them.

Editorial extensions

If this is right

  • The same pretrained LXMERT weights can be fine-tuned to a two-image reasoning task whose images were never seen in pretraining, and still gain 22 absolute points over the previous best, so the learned cross-modality representations transfer to unseen visual domains.
  • On VQA and GQA, a single model without task-specific modules reaches the highest published scores, suggesting that cross-modality pretraining supplies much of what task-specific designs previously supplied.
  • The ablations imply each pretraining task is doing work: dropping the vision tasks or the image QA objective lowers development accuracy on all three benchmarks.
  • Loading a language-only pretrained encoder into LXMERT produces weaker final results than training the same architecture from scratch, indicating that the cross-modality objective, not just the language initialization, drives the improvement.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same recipe should apply to any task that pairs one image with one sentence, such as caption retrieval, referring expression grounding, or visual entailment, because the pretraining objectives only assume aligned image-text pairs.
  • Because the vision input is capped at 36 frozen object regions, the next measurable gains may come not from larger fusion layers but from richer or fine-tuned visual features; the paper's own detector choice is the likeliest ceiling on further improvement.
  • A testable corollary is that cross-modality pretraining should reduce the amount of task-specific supervision needed: fine-tuning LXMERT on smaller fractions of VQA or GQA should degrade more gracefully than a model trained from scratch.
  • The large NLVR2 jump may partly reflect how weak the previous baseline was, but the consistency metric improving from 12% to 42.1% suggests the gain is not only on easy examples.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The paper proposes LXMERT, a Transformer-based cross-modal encoder with three encoders (language, object-relationship, and cross-modality) and five pretraining objectives (masked language modeling, masked object feature regression, masked object label classification, cross-modality matching, and image QA). The model is pretrained on 9.18M image-sentence pairs from COCO, Visual Genome, VQA, GQA, and VG-QA, then fine-tuned on VQA v2.0, GQA, and NLVR2. The authors report 72.5% on VQA test-standard, 60.3% on GQA test-standard, and 76.2% on NLVR2 Test-U, a 22% absolute improvement over the prior best result, and they provide ablation studies (Tables 3-5) and attention visualizations.

Significance. If the results hold, LXMERT is an important early demonstration that large-scale cross-modal pretraining transfers to vision-and-language tasks; the 22% absolute gain on NLVR2 and the ablation pattern are strong evidence for the value of the proposed pretraining tasks and architecture. The paper is also commendable for releasing code and pretrained models and for carefully describing data splits in Appendix C. The main qualification is that the unqualified state-of-the-art claim on VQA is not supported by the paper's own footnote listing MCAN at 72.8%.

major comments (2)
  1. [Abstract; §1; §4.3 (footnote 5); Table 2; §7] The paper repeatedly claims that LXMERT 'achieves the state-of-the-art results' on VQA (abstract, Section 1, and conclusion), but footnote 5 in Section 4.3 states that MCAN (VQA challenge version) reaches 72.8% on VQA 2.0 test-standard, which is higher than LXMERT's 72.5%. The Table 2 caption includes the qualifier '(comparable)', but the abstract and conclusion do not, making the central empirical claim internally inconsistent. Please qualify the VQA claim as state-of-the-art among methods submitted by the EMNLP deadline, or among comparable single-feature models, and integrate the MCAN comparison into the main text rather than only in a footnote. The GQA and NLVR2 claims are not affected.
  2. [§5, Tables 3-5] All accuracy numbers in the ablation tables come from single runs; there are no error bars, confidence intervals, or multiple seeds. In several places the differences between variants are very small (e.g., Table 5 rows 2 and 3 differ by 0.2-0.3 accuracy on VQA and GQA), while the text claims that the components 'significantly contribute' to the results. Please add variance information, or soften the significance wording to reflect the evidence level. This is needed to support the paper's second central claim that the individual pretraining tasks and model components are each responsible for the gains.
minor comments (4)
  1. [§7] There is a typo in the conclusion: 'Transfermer' should be 'Transformer'.
  2. [§6] The related-work statement that LXMERT provides 'the current best performance' with a 'margin of 1.5% accuracy on VQA 2.0' needs the same timestamp/qualification as the main state-of-the-art claim, since footnote 5 already lists MCAN above LXMERT.
  3. [§3.3] The frozen Faster R-CNN detector is a design choice, but its ceiling effect is not analyzed; a sentence in the main text acknowledging this limitation would be appropriate, since the model can never recover visual information that the detector does not provide.
  4. [§5.2] The pre-training versus data augmentation comparison would be clearer if the text specified exactly which data are added in the DA condition and how the total data volume is matched, since the current wording only says the amounts are 'similar'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: LXMERT's architecture and pretraining are evaluated on held-out test splits, with ablations and external baselines supporting the claims.

full rationale

The paper's central claim is that a new cross-modality Transformer architecture, pretrained with five tasks on aggregated vision-and-language data, improves VQA, GQA, and NLVR2 performance after fine-tuning. This claim is not derived by construction from its inputs. The model parameters are not fitted to the test-standard or Test-U labels; evaluation is on held-out splits as described in Appendix C, and the NLVR2 evaluation explicitly uses images and statements not present in pretraining ('we do not use the natural images in their dataset for our pre-training, but fine-tune and evaluate on these challenging, real-world images'). The pretraining objectives (masked language modeling, masked object prediction, cross-modality matching, image QA) are auxiliary self-supervised or supervised tasks on train/dev corpora, and the reported gains are benchmark comparisons against external state-of-the-art methods, not algebraic consequences of the objective definitions. The ablations in Tables 3–5 compare variants and show that the pretraining tasks and architecture components contribute empirically to the results. There is no fitting of a parameter to a subset of data that is then relabeled as a prediction, no target quantity defined in terms of the model's own outputs, and no load-bearing self-citation chain. The footnote reporting MCAN's 72.8% versus LXMERT's 72.5% is an internal consistency or factual-qualification concern about the unqualified 'state-of-the-art' wording, not a circularity issue, because the empirical comparison does not reduce to the model's own construction. Overall, the derivation and evaluation are self-contained against external benchmarks, so the circularity score is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claim rests on standard deep learning assumptions plus the quality of the frozen detector and the transferability of the pretraining tasks. No ad hoc entities are introduced; LXMERT is a model architecture, not a new physical or formal object.

free parameters (5)
  • Number of object proposals per image = 36
    Fixed at 36 for all images to avoid padding and maximize compute utilization (Sec. 3.3). The model can only see these 36 regions.
  • Layer counts NL, NX, NR = 9, 5, 5
    Set so the equivalent transformer depth equals BERT-base (Sec. 3.3). Hand-picked, not tuned on target benchmarks.
  • Hidden size = 768
    Same as BERT-base; carried from prior work to allow parameter sharing with BERT (Sec. 3.3).
  • Pretraining epochs per phase = 20 total; image QA only in last 10
    Image QA converges faster and needs a smaller learning rate; the 10/10 split is a design choice (Sec. 3.3).
  • Peak learning rates = 1e-4 pretrain, 1e-5/5e-5 fine-tune
    Standard values from BERT fine-tuning; no systematic tuning is reported (Sec. 3.3, 4.2).
assumptions (5)
  • domain assumption The frozen Faster R-CNN object detector provides region features that encode sufficient visual semantics for vision-and-language tasks.
    The entire vision input is bottlenecked by these fixed features. Invoked in Sec. 2.1 and Sec. 3.3.
  • domain assumption The five pretraining tasks (masked LM, masked object regression/classification, matching, image QA) jointly learn representations that transfer to downstream tasks.
    The paper assumes equal-weighted sums of these losses produce useful cross-modal alignment, supported only by ablations (Sec. 3.1).
  • standard math The Transformer architecture and its attention layers can learn the needed intra- and cross-modality alignments from the pretraining data.
    Standard deep learning machinery taken from Vaswani et al. (2017).
  • domain assumption Evaluation benchmarks VQA v2.0, GQA, and NLVR2 reliably measure vision-and-language reasoning.
    The conclusions depend on these datasets being valid measures of the target capability (Sec. 4.1).
  • domain assumption The data split procedure prevents test images from being seen during pretraining.
    Appendix C describes the splits; the paper relies on benchmark procedures for test-standard evaluation.

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Cite this review

Pith. "Pith review of LXMERT: Learning Cross-Modality Encoder Representations from Transformers." pith.science (2026). https://pith.science/paper/FPIGQHMJ

@misc{pith2026190807490,
  author       = {Pith},
  title        = {Pith review of: LXMERT: Learning Cross-Modality Encoder Representations from Transformers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FPIGQHMJ}},
  note         = {Machine review of arXiv:1908.07490}
}
read the original abstract

Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative pre-training tasks: masked language modeling, masked object prediction (feature regression and label classification), cross-modality matching, and image question answering. These tasks help in learning both intra-modality and cross-modality relationships. After fine-tuning from our pre-trained parameters, our model achieves the state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our pre-trained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR2, and improve the previous best result by 22% absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that both our novel model components and pre-training strategies significantly contribute to our strong results; and also present several attention visualizations for the different encoders. Code and pre-trained models publicly available at: https://github.com/airsplay/lxmert

Figures

Figures reproduced from arXiv: 1908.07490 by the authors.

Figure 1
Figure 1. The LXMERT model for learning vision-and-language cross-modality representations. ‘Self’ and [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Pre-training in LXMERT. The object RoI features and word tokens are masked. Our five pre-training [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 5
Figure 5. Attention graphs in LXMERT’s cross￾modality encoder showing that the attention focus on pronouns (marked in pink), nouns (marked in blue), and articles (marked in red). d)) come from Hoover et al. (2019).13 We find that both the second LXMERT layer ( [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The attention graph (a) and its recovered [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

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Reference graph

Works this paper leans on

45 extracted references · 20 canonical work pages · cited by 65 Pith papers (see all)

  1. [1]

    URL: " 'urlintro :=

    ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2018. Bottom-up and top-down attention for image captioning and visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6077--6086

    2018

  4. [4]

    Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425--2433

    2015

  5. [5]

    Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473

    arXiv 2014

  6. [6]

    Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2015. Gated feedback recurrent neural networks. In International Conference on Machine Learning, pages 2067--2075

    work page 2015

  7. [7]

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248--255. IEEE

    work page 2009

  8. [8]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT

    2019

Show all 45 references
  1. [9]

    Peng Gao, Zhengkai Jiang, Haoxuan You, Pan Lu, Steven C. H. Hoi, Xiaogang Wang, and Hongsheng Li. 2019 a . Dynamic fusion with intra- and inter-modality attention flow for visual question answering. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    2019

  2. [10]

    Peng Gao, Haoxuan You, Zhanpeng Zhang, Xiaogang Wang, and Hongsheng Li. 2019 b . Multi-modality latent interaction network for visual question answering. arXiv preprint arXiv:1908.04289

    2019 arXiv

  3. [11]

    Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580--587

    2014

  4. [12]

    Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017. 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, pages 6904--6913

    2017

  5. [13]

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770--778

    2016

  6. [14]

    Dan Hendrycks and Kevin Gimpel. 2016. Bridging nonlinearities and stochastic regularizers with gaussian error linear units. https://openreview.net/forum?id=Bk0MRI5lg

    2016

  7. [15]

    Benjamin Hoover, Hendrik Strobelt, and Sebastian Gehrmann. 2019. exbert: A visual analysis tool to explore learned representations in transformers models. arXiv preprint arXiv:1910.05276

    2019 arXiv

  8. [16]

    Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Kate Saenko. 2017. Learning to reason: End-to-end module networks for visual question answering. In Proceedings of the IEEE International Conference on Computer Vision, pages 804--813

    2017

  9. [17]

    Drew A Hudson and Christopher D Manning. 2019. Gqa: a new dataset for compositional question answering over real-world images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

    2019

  10. [18]

    Yu Jiang, Vivek Natarajan, Xinlei Chen, Marcus Rohrbach, Dhruv Batra, and Devi Parikh. 2018. Pythia v0. 1: the winning entry to the vqa challenge 2018. arXiv preprint arXiv:1807.09956

    2018 arXiv

  11. [19]

    Jin-Hwa Kim, Jaehyun Jun, and Byoung-Tak Zhang. 2018. Bilinear attention networks. In Advances in Neural Information Processing Systems, pages 1564--1574

    2018

  12. [20]

    Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. In International Conference on Learning Representations

    2014

  13. [21]

    Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. 2017. Visual genome: Connecting language and vision using crowdsourced dense image annotations. International Journal of Com...

    2017

  14. [22]

    Guillaume Lample and Alexis Conneau. 2019. Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291

    2019 arXiv

  15. [23]

    Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. 2019. Visualbert: A simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557

    2019 arXiv

  16. [24]

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll \'a r, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740--755. Springer

    2014

  17. [25]

    Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. arXiv preprint arXiv:1908.02265

    2019 arXiv

  18. [26]

    Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh. 2016. Hierarchical question-image co-attention for visual question answering. In Advances In Neural Information Processing Systems, pages 289--297

    2016

  19. [27]

    Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville. 2018. Film: Visual reasoning with a general conditioning layer. In Thirty-Second AAAI Conference on Artificial Intelligence

    2018

  20. [28]

    Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of NAACL-HLT, pages 2227--2237

    2018

  21. [29]

    Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. URL https://s3-us-west-2. amazonaws. com/openai-assets/researchcovers/languageunsupervised/language understanding paper. pdf

    2018

  22. [30]

    Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383--2392

    2016

  23. [31]

    Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91--99

    2015

  24. [32]

    Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017. Bidirectional attention flow for machine comprehension. In International Conference on Learning Representations

    2017

  25. [33]

    Meet Shah, Xinlei Chen, Marcus Rohrbach, and Devi Parikh. 2019. Cycle-consistency for robust visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

    2019

  26. [34]

    Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

    2014 arXiv

  27. [35]

    Alane Suhr, Stephanie Zhou, Iris Zhang, Huajun Bai, and Yoav Artzi. 2019. A corpus for reasoning about natural language grounded in photographs. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

    2019

  28. [36]

    Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid. 2019. Videobert: A joint model for video and language representation learning. arXiv preprint arXiv:1904.01766

    2019 arXiv

  29. [37]

    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1--9

    2015

  30. [38]

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems, pages 5998--6008

    2017

  31. [39]

    Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018. Glue: A multi-task benchmark and analysis platform for natural language understanding. EMNLP 2018, page 353

    2018

  32. [40]

    Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144

    2016 arXiv

  33. [41]

    Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International conference on machine learning, pages 2048--2057

    2015

  34. [42]

    Zhou Yu, Yuhao Cui, Jun Yu, Dacheng Tao, and Qi Tian. 2019 a . Multimodal unified attention networks for vision-and-language interactions. arXiv preprint arXiv:1908.04107

    2019 arXiv

  35. [43]

    Zhou Yu, Jun Yu, Yuhao Cui, Dacheng Tao, and Qi Tian. 2019 b . Deep modular co-attention networks for visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6281--6290

    2019

  36. [44]

    Zhou Yu, Jun Yu, Chenchao Xiang, Jianping Fan, and Dacheng Tao. 2018. Beyond bilinear: Generalized multimodal factorized high-order pooling for visual question answering. IEEE Transactions on Neural Networks and Learning Systems, 29(12):5947--5959

    2018

  37. [45]

    Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. 2016. Visual7w: Grounded question answering in images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4995--5004

    2016

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