---
title: "Long-term Recurrent Convolutional Networks — Donahue 2014"
description: "Deep CNNs had come to dominate image interpretation, but most video and description systems still relied on a fixed visual representation or simple…"
authors: ["Jeff Donahue", "Lisa Anne Hendricks", "Marcus Rohrbach", "Subhashini Venugopalan", "Sergio Guadarrama", "Kate Saenko", "Trevor Darrell"]
year: 2014
venue: "arXiv:1411.4389"
source: "https://arxiv.org/abs/1411.4389"
doi: "10.48550/arxiv.1411.4389"
arxiv: "1411.4389"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/donahue-2014-long-term-recurrent-convolutional-networks/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/donahue-2014-long-term-recurrent-convolutional-networks/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/) / Long-term Recurrent Convolutional Networks for Visual Recognition and Description

# Long-term Recurrent Convolutional Networks for Visual Recognition and Description

**Authors:** [Jeff Donahue](https://md.research.elamur.ai/free/en/authors/jeff-donahue/), [Lisa Anne Hendricks](https://md.research.elamur.ai/free/en/authors/lisa-anne-hendricks/), [Marcus Rohrbach](https://md.research.elamur.ai/free/en/authors/marcus-rohrbach/), Subhashini Venugopalan, [Sergio Guadarrama](https://md.research.elamur.ai/free/en/authors/sergio-guadarrama/), [Kate Saenko](https://md.research.elamur.ai/free/en/authors/kate-saenko/), [Trevor Darrell](https://md.research.elamur.ai/free/en/authors/trevor-darrell/) · **Year:** 2014 · **Venue:** arXiv:1411.4389 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1411.4389) · [PDF](https://arxiv.org/pdf/1411.4389) · [DOI 10.48550/arxiv.1411.4389](https://doi.org/10.48550/arxiv.1411.4389) · [arXiv 1411.4389](https://arxiv.org/abs/1411.4389) · [OpenAlex](https://openalex.org/W4296244047)

**Citations:** 12 citations (OpenAlex, 2026-10-14) · [130](https://md.research.elamur.ai/free/en/papers/donahue-2014-long-term-recurrent-convolutional-networks/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
Deep CNNs had come to dominate image interpretation, but most video and description systems still relied on a fixed visual representation or simple averaging over time. The authors propose Long-term Recurrent Convolutional Networks (LRCNs). These combine a convolutional visual encoder with stacked LSTM layers, and the whole model is trained end-to-end. Both parts share weights across time steps, so the model handles inputs and outputs of variable length. This makes it "doubly deep": it learns structure across both space and time. The framework is applied to three settings:
- **Video activity recognition:** CNN features from each frame feed deep LSTMs.
- **Image captioning:** a convnet encodes the image and an LSTM decodes it into a sentence, as a multimodal counterpart to LSTM encoder-decoder translation.
- **Video description:** an LSTM decoder generates text from the outputs of conventional semantic role tuple predictors.

The joint models outperform approaches whose recognition and generation components are defined or optimized separately. The work matters because it was an early, general demonstration that recurrent networks attached to convnets can turn pixels directly into language.

## Key points
- LRCN pipeline: a CNN extracts features from each frame or image, a stack of LSTMs models the sequence, and the model emits a variable-length prediction.
- The paper uses the LSTM variant from [13]. It has input, forget, output and input-modulation gates plus a memory cell, which counters the vanishing-gradient problem of plain RNNs.
- On standard activity benchmarks, adding LSTMs gives notable gains, even though those datasets show limited temporal complexity.
- Captioning is end-to-end trainable on large image–text datasets. Even with modest training, it produces generation results competitive with existing methods.
- For video description, the LSTM decoder beats earlier pipelines based on statistical machine translation. The RNN and LSTM implementations were released in Caffe (jeffdonahue.com/lrcn).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{donahue2014long,
  title = {{Long-term Recurrent Convolutional Networks for Visual Recognition and Description}},
  author = {Donahue, Jeff and Hendricks, Lisa Anne and Rohrbach, Marcus and Venugopalan, Subhashini and Guadarrama, Sergio and Saenko, Kate and Darrell, Trevor},
  year = {2014},
  doi = {10.48550/arxiv.1411.4389},
  eprint = {1411.4389},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1411.4389}
}
```

- [← previous Lexicon Infused Phrase Embeddings for Named Entity Resolution](https://md.research.elamur.ai/free/en/papers/passos-2014-lexicon-infused-phrase-embeddings-named/)
- [next → An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews](https://md.research.elamur.ai/free/en/papers/buschmeier-2014-impact-analysis-features-classification-approach/)
- [↑ back to papers of 2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/)

## References

73 references, 15 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. S. Ji, W. Xu, M. Yang, and K. Yu, “3D convolutional neural networks for human action recognition,” in IEEE Trans. Pattern Anal. Mach. Intell., 2013. [find](https://scholar.google.com/scholar?q=S%2E+Ji%2C+W%2E+Xu%2C+M%2E+Yang%2C+and+K%2E+Yu%2C+%E2%80%9C3D+convolutional+neural+networks+for+human+action+recognition%2C%E2%80%9D+in+IEEE+Trans%2E+Pattern+Anal%2E+Mach%2E+Intell%2E%2C+2013%2E)
2. M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt, “Sequential deep learning for human action recognition,” in Human Behavior Understanding, 2011. [find](https://scholar.google.com/scholar?q=M%2E+Baccouche%2C+F%2E+Mamalet%2C+C%2E+Wolf%2C+C%2E+Garcia%2C+and+A%2E+Baskurt%2C+%E2%80%9CSequential+deep+learning+for+human+action+recognition%2C%E2%80%9D+in+Human+Behavior+Understanding%2C+2011%2E)
3. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in CVPR, 2014. [find](https://scholar.google.com/scholar?q=A%2E+Karpathy%2C+G%2E+Toderici%2C+S%2E+Shetty%2C+T%2E+Leung%2C+R%2E+Sukthankar%2C+and+L%2E+Fei%2DFei%2C+%E2%80%9CLarge%2Dscale+video+classification+with+convolutional+neural+networks%2C%E2%80%9D+in+CVPR%2C+2014%2E)
4. K. Simonyan and A. Zisserman, “Two-stream convolutional networks for action recognition in videos,” in NIPS, 2014. [find](https://scholar.google.com/scholar?q=K%2E+Simonyan+and+A%2E+Zisserman%2C+%E2%80%9CTwo%2Dstream+convolutional+networks+for+action+recognition+in+videos%2C%E2%80%9D+in+NIPS%2C+2014%2E)
5. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” DTIC Document, Tech. Rep., 1985. [find](https://scholar.google.com/scholar?q=D%2E+E%2E+Rumelhart%2C+G%2E+E%2E+Hinton%2C+and+R%2E+J%2E+Williams%2C+%E2%80%9CLearning+internal+representations+by+error+propagation%2C%E2%80%9D+DTIC+Document%2C+Tech%2E+Rep%2E%2C+1985%2E)
6. R. J. Williams and D. Zipser, “A learning algorithm for continually running fully recurrent neural networks,” in Neural Computation, 1989. [find](https://scholar.google.com/scholar?q=R%2E+J%2E+Williams+and+D%2E+Zipser%2C+%E2%80%9CA+learning+algorithm+for+continually+running+fully+recurrent+neural+networks%2C%E2%80%9D+in+Neural+Computation%2C+1989%2E)
7. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” in Neural Computation. MIT Press, 1997. [find](https://scholar.google.com/scholar?q=S%2E+Hochreiter+and+J%2E+Schmidhuber%2C+%E2%80%9CLong+short%2Dterm+memory%2C%E2%80%9D+in+Neural+Computation%2E+MIT+Press%2C+1997%2E)
8. A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” in ICML, 2014. [find](https://scholar.google.com/scholar?q=A%2E+Graves+and+N%2E+Jaitly%2C+%E2%80%9CTowards+end%2Dto%2Dend+speech+recognition+with+recurrent+neural+networks%2C%E2%80%9D+in+ICML%2C+2014%2E)
9. I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in NIPS, 2014. [find](https://scholar.google.com/scholar?q=I%2E+Sutskever%2C+O%2E+Vinyals%2C+and+Q%2E+V%2E+Le%2C+%E2%80%9CSequence+to+sequence+learning+with+neural+networks%2C%E2%80%9D+in+NIPS%2C+2014%2E)
10. K. Cho, B. van Merriënboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-decoder approaches,” in SSST Workshop, 2014. [find](https://scholar.google.com/scholar?q=K%2E+Cho%2C+B%2E+van+Merrie%CC%88nboer%2C+D%2E+Bahdanau%2C+and+Y%2E+Bengio%2C+%E2%80%9COn+the+properties+of+neural+machine+translation%3A+Encoder%2Ddecoder+approaches%2C%E2%80%9D+in+SSST+Workshop%2C+2014%2E) [our card](https://md.research.elamur.ai/free/en/papers/cho-2014-properties-neural-machine-translation-encoder/)
11. M. Rohrbach, W. Qiu, I. Titov, S. Thater, M. Pinkal, and B. Schiele, “Translating video content to natural language descriptions,” in ICCV, 2013. [find](https://scholar.google.com/scholar?q=M%2E+Rohrbach%2C+W%2E+Qiu%2C+I%2E+Titov%2C+S%2E+Thater%2C+M%2E+Pinkal%2C+and+B%2E+Schiele%2C+%E2%80%9CTranslating+video+content+to+natural+language+descriptions%2C%E2%80%9D+in+ICCV%2C+2013%2E)
12. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in ACM MM, 2014. [find](https://scholar.google.com/scholar?q=Y%2E+Jia%2C+E%2E+Shelhamer%2C+J%2E+Donahue%2C+S%2E+Karayev%2C+J%2E+Long%2C+R%2E+Girshick%2C+S%2E+Guadarrama%2C+and+T%2E+Darrell%2C+%E2%80%9CCaffe%3A+Convolutional+architecture+for+fast+feature+embedding%2C%E2%80%9D+in+ACM+MM%2C+2014%2E) [our card](https://md.research.elamur.ai/free/en/papers/jia-2014-caffe-convolutional-architecture-fast-feature/)
13. W. Zaremba and I. Sutskever, “Learning to execute,” in arXiv preprint arXiv:1410.4615, 2014. [find](https://scholar.google.com/scholar?q=W%2E+Zaremba+and+I%2E+Sutskever%2C+%E2%80%9CLearning+to+execute%2C%E2%80%9D+in+arXiv+preprint+arXiv%3A1410%2E4615%2C+2014%2E)
14. A. Graves, “Generating sequences with recurrent neural networks,” in arXiv preprint arXiv:1308.0850, 2013. [find](https://scholar.google.com/scholar?q=A%2E+Graves%2C+%E2%80%9CGenerating+sequences+with+recurrent+neural+networks%2C%E2%80%9D+in+arXiv+preprint+arXiv%3A1308%2E0850%2C+2013%2E)
15. O. Vinyals, S. V. Ravuri, and D. Povey, “Revisiting recurrent neural networks for robust ASR,” in ICASSP, 2012. [find](https://scholar.google.com/scholar?q=O%2E+Vinyals%2C+S%2E+V%2E+Ravuri%2C+and+D%2E+Povey%2C+%E2%80%9CRevisiting+recurrent+neural+networks+for+robust+ASR%2C%E2%80%9D+in+ICASSP%2C+2012%2E)
16. I. Sutskever, J. Martens, and G. E. Hinton, “Generating text with recurrent neural networks,” in ICML, 2011. [find](https://scholar.google.com/scholar?q=I%2E+Sutskever%2C+J%2E+Martens%2C+and+G%2E+E%2E+Hinton%2C+%E2%80%9CGenerating+text+with+recurrent+neural+networks%2C%E2%80%9D+in+ICML%2C+2011%2E)
17. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in NIPS, 2012. [find](https://scholar.google.com/scholar?q=A%2E+Krizhevsky%2C+I%2E+Sutskever%2C+and+G%2E+E%2E+Hinton%2C+%E2%80%9CImageNet+classification+with+deep+convolutional+neural+networks%2C%E2%80%9D+in+NIPS%2C+2012%2E)
18. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in ICLR, 2015. [find](https://scholar.google.com/scholar?q=K%2E+Simonyan+and+A%2E+Zisserman%2C+%E2%80%9CVery+deep+convolutional+networks+for+large%2Dscale+image+recognition%2C%E2%80%9D+in+ICLR%2C+2015%2E) [our card](https://md.research.elamur.ai/free/en/papers/simonyan-2014-very-deep-convolutional-networks-large/)
19. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in CVPR, 2015. [find](https://scholar.google.com/scholar?q=C%2E+Szegedy%2C+W%2E+Liu%2C+Y%2E+Jia%2C+P%2E+Sermanet%2C+S%2E+Reed%2C+D%2E+Anguelov%2C+D%2E+Erhan%2C+V%2E+Vanhoucke%2C+and+A%2E+Rabinovich%2C+%E2%80%9CGoing+deeper+with+convolutions%2C%E2%80%9D+in+CVPR%2C+2015%2E)
20. K. Cho, B. van Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” in EMNLP, 2014. [find](https://scholar.google.com/scholar?q=K%2E+Cho%2C+B%2E+van+Merrienboer%2C+C%2E+Gulcehre%2C+F%2E+Bougares%2C+H%2E+Schwenk%2C+and+Y%2E+Bengio%2C+%E2%80%9CLearning+phrase+representations+using+rnn+encoder%2Ddecoder+for+statistical+machine+translation%2C%E2%80%9D+in+EMNLP%2C+2014%2E)
21. T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, “High accuracy optical flow estimation based on a theory for warping,” in ECCV, 2004. [find](https://scholar.google.com/scholar?q=T%2E+Brox%2C+A%2E+Bruhn%2C+N%2E+Papenberg%2C+and+J%2E+Weickert%2C+%E2%80%9CHigh+accuracy+optical+flow+estimation+based+on+a+theory+for+warping%2C%E2%80%9D+in+ECCV%2C+2004%2E)
22. M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in ECCV, 2014. [find](https://scholar.google.com/scholar?q=M%2E+D%2E+Zeiler+and+R%2E+Fergus%2C+%E2%80%9CVisualizing+and+understanding+convolutional+networks%2C%E2%80%9D+in+ECCV%2C+2014%2E) [our card](https://md.research.elamur.ai/free/en/papers/zeiler-2013-visualizing-understanding-convolutional-networks/)
23. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” in IJCV, vol. 115, no. 3, 2015. [find](https://scholar.google.com/scholar?q=O%2E+Russakovsky%2C+J%2E+Deng%2C+H%2E+Su%2C+J%2E+Krause%2C+S%2E+Satheesh%2C+S%2E+Ma%2C+Z%2E+Huang%2C+A%2E+Karpathy%2C+A%2E+Khosla%2C+M%2E+Bernstein%2C+A%2E+C%2E+Berg%2C+and+L%2E+Fei%2DFei%2C+%E2%80%9CImageNet+Large+Scale+Visual+Recognition+Challenge%2C%E2%80%9D+in+IJCV%2C) [our card](https://md.research.elamur.ai/free/en/papers/russakovsky-2014-imagenet-large-scale-visual-recognition/)
24. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in CVPR, 2009. [find](https://scholar.google.com/scholar?q=J%2E+Deng%2C+W%2E+Dong%2C+R%2E+Socher%2C+L%2E%2DJ%2E+Li%2C+K%2E+Li%2C+and+L%2E+Fei%2DFei%2C+%E2%80%9CImageNet%3A+A+large%2Dscale+hierarchical+image+database%2C%E2%80%9D+in+CVPR%2C+2009%2E)
25. K. Soomro, A. R. Zamir, and M. Shah, “UCF101: A dataset of 101 human actions classes from videos in the wild,” CRCV-TR-12-01, Tech. Rep., 2012. [find](https://scholar.google.com/scholar?q=K%2E+Soomro%2C+A%2E+R%2E+Zamir%2C+and+M%2E+Shah%2C+%E2%80%9CUCF101%3A+A+dataset+of+101+human+actions+classes+from+videos+in+the+wild%2C%E2%80%9D+CRCV%2DTR%2D12%2D01%2C+Tech%2E+Rep%2E%2C+2012%2E)
26. P. Y. Micah Hodosh and J. Hockenmaier, “Framing image description as a ranking task: Data, models and evaluation metrics,” in JAIR, vol. 47, 2013. [find](https://scholar.google.com/scholar?q=P%2E+Y%2E+Micah+Hodosh+and+J%2E+Hockenmaier%2C+%E2%80%9CFraming+image+description+as+a+ranking+task%3A+Data%2C+models+and+evaluation+metrics%2C%E2%80%9D+in+JAIR%2C+vol%2E+47%2C+2013%2E)
27. J. Mao, W. Xu, Y. Yang, J. Wang, and A. Yuille, “Deep captioning with multimodal recurrent neural networks (m-RNN),” in ICLR, 2015. [find](https://scholar.google.com/scholar?q=J%2E+Mao%2C+W%2E+Xu%2C+Y%2E+Yang%2C+J%2E+Wang%2C+and+A%2E+Yuille%2C+%E2%80%9CDeep+captioning+with+multimodal+recurrent+neural+networks+%28m%2DRNN%29%2C%E2%80%9D+in+ICLR%2C+2015%2E)
28. A. Karpathy, A. Joulin, and L. Fei-Fei, “Deep fragment embeddings for bidirectional image sentence mapping,” in NIPS, 2014. [find](https://scholar.google.com/scholar?q=A%2E+Karpathy%2C+A%2E+Joulin%2C+and+L%2E+Fei%2DFei%2C+%E2%80%9CDeep+fragment+embeddings+for+bidirectional+image+sentence+mapping%2C%E2%80%9D+in+NIPS%2C+2014%2E)
29. R. Socher, A. Karpathy, Q. V. Le, C. D. Manning, and A. Y. Ng, “Grounded compositional semantics for finding and describing images with sentences,” in TACL, vol. 2, 2014. [find](https://scholar.google.com/scholar?q=R%2E+Socher%2C+A%2E+Karpathy%2C+Q%2E+V%2E+Le%2C+C%2E+D%2E+Manning%2C+and+A%2E+Y%2E+Ng%2C+%E2%80%9CGrounded+compositional+semantics+for+finding+and+describing+images+with+sentences%2C%E2%80%9D+in+TACL%2C+vol%2E+2%2C+2014%2E) [our card](https://md.research.elamur.ai/free/en/papers/socher-2014-grounded-compositional-semantics-finding-describing/)
30. A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, T. Mikolov et al., “Devise: A deep visual-semantic embedding model,” in NIPS, 2013. [find](https://scholar.google.com/scholar?q=A%2E+Frome%2C+G%2E+S%2E+Corrado%2C+J%2E+Shlens%2C+S%2E+Bengio%2C+J%2E+Dean%2C+T%2E+Mikolov+et+al%2E%2C+%E2%80%9CDevise%3A+A+deep+visual%2Dsemantic+embedding+model%2C%E2%80%9D+in+NIPS%2C+2013%2E)
31. R. Kiros, R. Salakhuditnov, and R. S. Zemel, “Unifying visual-semantic embeddings with multimodal neural language models,” in TACL, 2015. [find](https://scholar.google.com/scholar?q=R%2E+Kiros%2C+R%2E+Salakhuditnov%2C+and+R%2E+S%2E+Zemel%2C+%E2%80%9CUnifying+visual%2Dsemantic+embeddings+with+multimodal+neural+language+models%2C%E2%80%9D+in+TACL%2C+2015%2E) [our card](https://md.research.elamur.ai/free/en/papers/kiros-2014-unifying-visual-semantic-embeddings-multimodal/)
32. M. H. Peter Young, Alice Lai and J. Hockenmaier, “From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions,” in TACL, vol. 2, 2014. [find](https://scholar.google.com/scholar?q=M%2E+H%2E+Peter+Young%2C+Alice+Lai+and+J%2E+Hockenmaier%2C+%E2%80%9CFrom+image+descriptions+to+visual+denotations%3A+New+similarity+metrics+for+semantic+inference+over+event+descriptions%2C%E2%80%9D+in+TACL%2C+vol%2E+2%2C+2014%2E)
33. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: Common objects in context,” arXiv preprint arXiv:1405.0312, Tech. Rep., 2014. [find](https://scholar.google.com/scholar?q=T%2E%2DY%2E+Lin%2C+M%2E+Maire%2C+S%2E+Belongie%2C+J%2E+Hays%2C+P%2E+Perona%2C+D%2E+Ramanan%2C+P%2E+Dolla%CC%81r%2C+and+C%2E+L%2E+Zitnick%2C+%E2%80%9CMicrosoft+COCO%3A+Common+objects+in+context%2C%E2%80%9D+arXiv+preprint+arXiv%3A1405%2E0312%2C+Tech%2E+Rep%2E%2C+2014%2E) [our card](https://md.research.elamur.ai/free/en/papers/lin-2014-microsoft-coco-common-objects-context/)
34. K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “BLEU: a method for automatic evaluation of machine translation,” in ACL, 2002. [find](https://scholar.google.com/scholar?q=K%2E+Papineni%2C+S%2E+Roukos%2C+T%2E+Ward%2C+and+W%2E%2DJ%2E+Zhu%2C+%E2%80%9CBLEU%3A+a+method+for+automatic+evaluation+of+machine+translation%2C%E2%80%9D+in+ACL%2C+2002%2E) [our card](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/)
35. R. Vedantam, C. L. Zitnick, and D. Parikh, “CIDEr: Consensus-based image description evaluation,” in CVPR, 2015. [find](https://scholar.google.com/scholar?q=R%2E+Vedantam%2C+C%2E+L%2E+Zitnick%2C+and+D%2E+Parikh%2C+%E2%80%9CCIDEr%3A+Consensus%2Dbased+image+description+evaluation%2C%E2%80%9D+in+CVPR%2C+2015%2E) [our card](https://md.research.elamur.ai/free/en/papers/vedantam-2014-cider-consensus-based-image-description/)
36. S. Banerjee and A. Lavie, “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, 2005. [find](https://scholar.google.com/scholar?q=S%2E+Banerjee+and+A%2E+Lavie%2C+%E2%80%9CMETEOR%3A+An+automatic+metric+for+MT+evaluation+with+improved+correlation+with+human+judgments%2C%E2%80%9D+in+Proceedings+of+the+ACL+Workshop+on+Intrinsic+and+Extrinsic+Evaluation+Measu) [our card](https://md.research.elamur.ai/free/en/papers/banerjee-2005-meteor-automatic-metric-mt-evaluation/)
37. C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, 2004. [find](https://scholar.google.com/scholar?q=C%2E%2DY%2E+Lin%2C+%E2%80%9CRouge%3A+A+package+for+automatic+evaluation+of+summaries%2C%E2%80%9D+in+Text+Summarization+Branches+Out%3A+Proceedings+of+the+ACL%2D04+Workshop%2C+2004%2E) [our card](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/)
38. O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, “Show and tell: A neural image caption generator,” in CVPR, 2015. [find](https://scholar.google.com/scholar?q=O%2E+Vinyals%2C+A%2E+Toshev%2C+S%2E+Bengio%2C+and+D%2E+Erhan%2C+%E2%80%9CShow+and+tell%3A+A+neural+image+caption+generator%2C%E2%80%9D+in+CVPR%2C+2015%2E) [our card](https://md.research.elamur.ai/free/en/papers/vinyals-2014-show-tell-neural-image-caption/)
39. J. Devlin, H. Cheng, H. Fang, S. Gupta, L. Deng, X. He, G. Zweig, and M. Mitchell, “Language models for image captioning: The quirks and what works,” in ACL, 2015. [find](https://scholar.google.com/scholar?q=J%2E+Devlin%2C+H%2E+Cheng%2C+H%2E+Fang%2C+S%2E+Gupta%2C+L%2E+Deng%2C+X%2E+He%2C+G%2E+Zweig%2C+and+M%2E+Mitchell%2C+%E2%80%9CLanguage+models+for+image+captioning%3A+The+quirks+and+what+works%2C%E2%80%9D+in+ACL%2C+2015%2E)
40. J. Mao, W. Xu, Y. Yang, J. Wang, Z. Huang, and A. Yuille, “Learning like a child: Fast novel visual concept learning from sentence descriptions of images,” in ICCV, 2015. [find](https://scholar.google.com/scholar?q=J%2E+Mao%2C+W%2E+Xu%2C+Y%2E+Yang%2C+J%2E+Wang%2C+Z%2E+Huang%2C+and+A%2E+Yuille%2C+%E2%80%9CLearning+like+a+child%3A+Fast+novel+visual+concept+learning+from+sentence+descriptions+of+images%2C%E2%80%9D+in+ICCV%2C+2015%2E)
41. H. Fang, S. Gupta, F. Iandola, R. Srivastava, L. Deng, P. Dollár, J. Gao, X. He, M. Mitchell, J. Platt et al., “From captions to visual concepts and back,” in CVPR, 2015. [find](https://scholar.google.com/scholar?q=H%2E+Fang%2C+S%2E+Gupta%2C+F%2E+Iandola%2C+R%2E+Srivastava%2C+L%2E+Deng%2C+P%2E+Dolla%CC%81r%2C+J%2E+Gao%2C+X%2E+He%2C+M%2E+Mitchell%2C+J%2E+Platt+et+al%2E%2C+%E2%80%9CFrom+captions+to+visual+concepts+and+back%2C%E2%80%9D+in+CVPR%2C+2015%2E)
42. J. Devlin, S. Gupta, R. Girshick, M. Mitchell, and C. L. Zitnick, “Exploring nearest neighbor approaches for image captioning,” arXiv preprint arXiv:1505.04467, Tech. Rep., 2015. — [arXiv:1505.04467](https://arxiv.org/abs/1505.04467) [find](https://scholar.google.com/scholar?q=J%2E+Devlin%2C+S%2E+Gupta%2C+R%2E+Girshick%2C+M%2E+Mitchell%2C+and+C%2E+L%2E+Zitnick%2C+%E2%80%9CExploring+nearest+neighbor+approaches+for+image+captioning%2C%E2%80%9D+arXiv+preprint+arXiv%3A1505%2E04467%2C+Tech%2E+Rep%2E%2C+2015%2E+%E2%80%94+%5BarXiv%3A1505%2E04467%5D%28)
43. J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, “Long-term recurrent convolutional networks for visual recognition and description,” in CVPR, 2015. [find](https://scholar.google.com/scholar?q=J%2E+Donahue%2C+L%2E+A%2E+Hendricks%2C+S%2E+Guadarrama%2C+M%2E+Rohrbach%2C+S%2E+Venugopalan%2C+K%2E+Saenko%2C+and+T%2E+Darrell%2C+%E2%80%9CLong%2Dterm+recurrent+convolutional+networks+for+visual+recognition+and+description%2C%E2%80%9D+in+CVPR%2C+2015%2E)
44. K. Xu, J. Ba, R. Kiros, A. Courville, R. Salakhutdinov, R. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” in ICML, 2015. [find](https://scholar.google.com/scholar?q=K%2E+Xu%2C+J%2E+Ba%2C+R%2E+Kiros%2C+A%2E+Courville%2C+R%2E+Salakhutdinov%2C+R%2E+Zemel%2C+and+Y%2E+Bengio%2C+%E2%80%9CShow%2C+attend+and+tell%3A+Neural+image+caption+generation+with+visual+attention%2C%E2%80%9D+in+ICML%2C+2015%2E) [our card](https://md.research.elamur.ai/free/en/papers/xu-2015-show-attend-tell-neural-image/)
45. A. Karpathy and L. Fei-Fei, “Deep visual-semantic alignments for generating image descriptions,” in CVPR, 2015. [find](https://scholar.google.com/scholar?q=A%2E+Karpathy+and+L%2E+Fei%2DFei%2C+%E2%80%9CDeep+visual%2Dsemantic+alignments+for+generating+image+descriptions%2C%E2%80%9D+in+CVPR%2C+2015%2E) [our card](https://md.research.elamur.ai/free/en/papers/karpathy-2014-deep-visual-semantic-alignments-generating/)
46. P. Kuznetsova, V. Ordonez, T. L. Berg, U. C. Hill, and Y. Choi, “TreeTalk: Composition and compression of trees for image descriptions,” in TACL, vol. 2, no. 10, 2014. [find](https://scholar.google.com/scholar?q=P%2E+Kuznetsova%2C+V%2E+Ordonez%2C+T%2E+L%2E+Berg%2C+U%2E+C%2E+Hill%2C+and+Y%2E+Choi%2C+%E2%80%9CTreeTalk%3A+Composition+and+compression+of+trees+for+image+descriptions%2C%E2%80%9D+in+TACL%2C+vol%2E+2%2C+no%2E+10%2C+2014%2E)
47. P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, C. Moran, R. Zens, C. Dyer, O. Bojar, A. Constantin, and E. Herbst, “Moses: Open source toolkit for statistical machine translation,” in ACL, 2007. [find](https://scholar.google.com/scholar?q=P%2E+Koehn%2C+H%2E+Hoang%2C+A%2E+Birch%2C+C%2E+Callison%2DBurch%2C+M%2E+Federico%2C+N%2E+Bertoldi%2C+B%2E+Cowan%2C+W%2E+Shen%2C+C%2E+Moran%2C+R%2E+Zens%2C+C%2E+Dyer%2C+O%2E+Bojar%2C+A%2E+Constantin%2C+and+E%2E+Herbst%2C+%E2%80%9CMoses%3A+Open+source+toolkit+for+statis)
48. A. Rohrbach, M. Rohrbach, W. Qiu, A. Friedrich, M. Pinkal, and B. Schiele, “Coherent multi-sentence video description with variable level of detail,” in German Conference on Pattern Recognition (GCPR). Springer, 2014. [find](https://scholar.google.com/scholar?q=A%2E+Rohrbach%2C+M%2E+Rohrbach%2C+W%2E+Qiu%2C+A%2E+Friedrich%2C+M%2E+Pinkal%2C+and+B%2E+Schiele%2C+%E2%80%9CCoherent+multi%2Dsentence+video+description+with+variable+level+of+detail%2C%E2%80%9D+in+German+Conference+on+Pattern+Recognition+%28GCPR%29)
49. H. Wang, A. Kläser, C. Schmid, and C. Liu, “Dense trajectories and motion boundary descriptors for action recognition,” in IJCV, 2013. [find](https://scholar.google.com/scholar?q=H%2E+Wang%2C+A%2E+Kla%CC%88ser%2C+C%2E+Schmid%2C+and+C%2E+Liu%2C+%E2%80%9CDense+trajectories+and+motion+boundary+descriptors+for+action+recognition%2C%E2%80%9D+in+IJCV%2C+2013%2E)
50. H. Wang and C. Schmid, “Action recognition with improved trajectories,” in ICCV, 2013. [find](https://scholar.google.com/scholar?q=H%2E+Wang+and+C%2E+Schmid%2C+%E2%80%9CAction+recognition+with+improved+trajectories%2C%E2%80%9D+in+ICCV%2C+2013%2E)
51. H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre, “HMDB: a large video database for human motion recognition,” in ICCV, 2011. [find](https://scholar.google.com/scholar?q=H%2E+Kuehne%2C+H%2E+Jhuang%2C+E%2E+Garrote%2C+T%2E+Poggio%2C+and+T%2E+Serre%2C+%E2%80%9CHMDB%3A+a+large+video+database+for+human+motion+recognition%2C%E2%80%9D+in+ICCV%2C+2011%2E)
52. M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt, “Action classification in soccer videos with long short-term memory recurrent neural networks,” in International Conference on Artificial Neural Networks (ICANN), 2010. [find](https://scholar.google.com/scholar?q=M%2E+Baccouche%2C+F%2E+Mamalet%2C+C%2E+Wolf%2C+C%2E+Garcia%2C+and+A%2E+Baskurt%2C+%E2%80%9CAction+classification+in+soccer+videos+with+long+short%2Dterm+memory+recurrent+neural+networks%2C%E2%80%9D+in+International+Conference+on+Artificial+)
53. A. Farhadi, M. Hejrati, M. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth, “Every picture tells a story: Generating sentences from images,” in ECCV, 2010. [find](https://scholar.google.com/scholar?q=A%2E+Farhadi%2C+M%2E+Hejrati%2C+M%2E+Sadeghi%2C+P%2E+Young%2C+C%2E+Rashtchian%2C+J%2E+Hockenmaier%2C+and+D%2E+Forsyth%2C+%E2%80%9CEvery+picture+tells+a+story%3A+Generating+sentences+from+images%2C%E2%80%9D+in+ECCV%2C+2010%2E)
54. G. Kulkarni, V. Premraj, S. Dhar, S. Li, Y. Choi, A. C. Berg, and T. L. Berg, “Baby talk: Understanding and generating simple image descriptions,” in CVPR, 2011. [find](https://scholar.google.com/scholar?q=G%2E+Kulkarni%2C+V%2E+Premraj%2C+S%2E+Dhar%2C+S%2E+Li%2C+Y%2E+Choi%2C+A%2E+C%2E+Berg%2C+and+T%2E+L%2E+Berg%2C+%E2%80%9CBaby+talk%3A+Understanding+and+generating+simple+image+descriptions%2C%E2%80%9D+in+CVPR%2C+2011%2E)
55. Y. Yang, C. L. Teo, H. Daumé III, and Y. Aloimonos, “Corpus-guided sentence generation of natural images,” in EMNLP, 2011. [find](https://scholar.google.com/scholar?q=Y%2E+Yang%2C+C%2E+L%2E+Teo%2C+H%2E+Daume%CC%81+III%2C+and+Y%2E+Aloimonos%2C+%E2%80%9CCorpus%2Dguided+sentence+generation+of+natural+images%2C%E2%80%9D+in+EMNLP%2C+2011%2E)
56. M. Mitchell, X. Han, J. Dodge, A. Mensch, A. Goyal, A. Berg, K. Yamaguchi, T. Berg, K. Stratos, and H. Daumé III, “Midge: Generating image descriptions from computer vision detections,” in Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, 2… [find](https://scholar.google.com/scholar?q=M%2E+Mitchell%2C+X%2E+Han%2C+J%2E+Dodge%2C+A%2E+Mensch%2C+A%2E+Goyal%2C+A%2E+Berg%2C+K%2E+Yamaguchi%2C+T%2E+Berg%2C+K%2E+Stratos%2C+and+H%2E+Daume%CC%81+III%2C+%E2%80%9CMidge%3A+Generating+image+descriptions+from+computer+vision+detections%2C%E2%80%9D+in+Proceeding)
57. P. Kuznetsova, V. Ordonez, A. C. Berg, T. L. Berg, and Y. Choi, “Collective generation of natural image descriptions,” in ACL, 2012. [find](https://scholar.google.com/scholar?q=P%2E+Kuznetsova%2C+V%2E+Ordonez%2C+A%2E+C%2E+Berg%2C+T%2E+L%2E+Berg%2C+and+Y%2E+Choi%2C+%E2%80%9CCollective+generation+of+natural+image+descriptions%2C%E2%80%9D+in+ACL%2C+2012%2E)
58. R. Kiros, R. Salakhutdinov, and R. Zemel, “Multimodal neural language models,” in ICML, 2014. [find](https://scholar.google.com/scholar?q=R%2E+Kiros%2C+R%2E+Salakhutdinov%2C+and+R%2E+Zemel%2C+%E2%80%9CMultimodal+neural+language+models%2C%E2%80%9D+in+ICML%2C+2014%2E)
59. S. Guadarrama, N. Krishnamoorthy, G. Malkarnenkar, S. Venugopalan, R. Mooney, T. Darrell, and K. Saenko, “YouTube2Text: Recognizing and describing arbitrary activities using semantic hierarchies and zero-shoot recognition,” in ICCV, 2013. [find](https://scholar.google.com/scholar?q=S%2E+Guadarrama%2C+N%2E+Krishnamoorthy%2C+G%2E+Malkarnenkar%2C+S%2E+Venugopalan%2C+R%2E+Mooney%2C+T%2E+Darrell%2C+and+K%2E+Saenko%2C+%E2%80%9CYouTube2Text%3A+Recognizing+and+describing+arbitrary+activities+using+semantic+hierarchies+and+z)
60. M. U. G. Khan, L. Zhang, and Y. Gotoh, “Human focused video description,” in ICCV Workshops, 2011. [find](https://scholar.google.com/scholar?q=M%2E+U%2E+G%2E+Khan%2C+L%2E+Zhang%2C+and+Y%2E+Gotoh%2C+%E2%80%9CHuman+focused+video+description%2C%E2%80%9D+in+ICCV+Workshops%2C+2011%2E)
61. A. Barbu, A. Bridge, Z. Burchill, D. Coroian, S. Dickinson, S. Fidler, A. Michaux, S. Mussman, S. Narayanaswamy, D. Salvi, L. Schmidt, J. Shangguan, J. M. Siskind, J. Waggoner, S. Wang, J. Wei, Y. Yin, and Z. Zhang, “Video in sentences out,” in The Conference on Uncertainty in Artificial Intelligenc… [find](https://scholar.google.com/scholar?q=A%2E+Barbu%2C+A%2E+Bridge%2C+Z%2E+Burchill%2C+D%2E+Coroian%2C+S%2E+Dickinson%2C+S%2E+Fidler%2C+A%2E+Michaux%2C+S%2E+Mussman%2C+S%2E+Narayanaswamy%2C+D%2E+Salvi%2C+L%2E+Schmidt%2C+J%2E+Shangguan%2C+J%2E+M%2E+Siskind%2C+J%2E+Waggoner%2C+S%2E+Wang%2C+J%2E+Wei%2C+Y%2E+Yin)
62. P. Das, C. Xu, R. Doell, and J. Corso, “Thousand frames in just a few words: Lingual description of videos through latent topics and sparse object stitching,” in CVPR, 2013. [find](https://scholar.google.com/scholar?q=P%2E+Das%2C+C%2E+Xu%2C+R%2E+Doell%2C+and+J%2E+Corso%2C+%E2%80%9CThousand+frames+in+just+a+few+words%3A+Lingual+description+of+videos+through+latent+topics+and+sparse+object+stitching%2C%E2%80%9D+in+CVPR%2C+2013%2E)
63. C. C. Tan, Y.-G. Jiang, and C.-W. Ngo, “Towards textually describing complex video contents with audio-visual concept classifiers,” in ACM MM, 2011. [find](https://scholar.google.com/scholar?q=C%2E+C%2E+Tan%2C+Y%2E%2DG%2E+Jiang%2C+and+C%2E%2DW%2E+Ngo%2C+%E2%80%9CTowards+textually+describing+complex+video+contents+with+audio%2Dvisual+concept+classifiers%2C%E2%80%9D+in+ACM+MM%2C+2011%2E)
64. J. Thomason, S. Venugopalan, S. Guadarrama, K. Saenko, and R. J. Mooney, “Integrating language and vision to generate natural language descriptions of videos in the wild,” in International Conference on Computational Linguistics (COLING), 2014. [find](https://scholar.google.com/scholar?q=J%2E+Thomason%2C+S%2E+Venugopalan%2C+S%2E+Guadarrama%2C+K%2E+Saenko%2C+and+R%2E+J%2E+Mooney%2C+%E2%80%9CIntegrating+language+and+vision+to+generate+natural+language+descriptions+of+videos+in+the+wild%2C%E2%80%9D+in+International+Conference+)
65. H. Sak, O. Vinyals, G. Heigold, A. Senior, E. McDermott, R. Monga, and M. Mao, “Sequence discriminative distributed training of long short-term memory recurrent neural networks,” in Interspeech, 2014. [find](https://scholar.google.com/scholar?q=H%2E+Sak%2C+O%2E+Vinyals%2C+G%2E+Heigold%2C+A%2E+Senior%2C+E%2E+McDermott%2C+R%2E+Monga%2C+and+M%2E+Mao%2C+%E2%80%9CSequence+discriminative+distributed+training+of+long+short%2Dterm+memory+recurrent+neural+networks%2C%E2%80%9D+in+Interspeech%2C+2014%2E)
66. J. Y.-H. Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, “Beyond short snippets: Deep networks for video classification,” in CVPR, 2015. [find](https://scholar.google.com/scholar?q=J%2E+Y%2E%2DH%2E+Ng%2C+M%2E+Hausknecht%2C+S%2E+Vijayanarasimhan%2C+O%2E+Vinyals%2C+R%2E+Monga%2C+and+G%2E+Toderici%2C+%E2%80%9CBeyond+short+snippets%3A+Deep+networks+for+video+classification%2C%E2%80%9D+in+CVPR%2C+2015%2E)
67. S. Yeung, O. Russakovsky, N. Jin, M. Andriluka, G. Mori, and L. Fei-Fei, “Every moment counts: Dense detailed labeling of actions in complex videos,” arXiv preprint arXiv:1507.05738, Tech. Rep., 2015. — [arXiv:1507.05738](https://arxiv.org/abs/1507.05738) [find](https://scholar.google.com/scholar?q=S%2E+Yeung%2C+O%2E+Russakovsky%2C+N%2E+Jin%2C+M%2E+Andriluka%2C+G%2E+Mori%2C+and+L%2E+Fei%2DFei%2C+%E2%80%9CEvery+moment+counts%3A+Dense+detailed+labeling+of+actions+in+complex+videos%2C%E2%80%9D+arXiv+preprint+arXiv%3A1507%2E05738%2C+Tech%2E+Rep%2E%2C+2015%2E)
68. L. A. Hendricks, S. Venugopalan, M. Rohrbach, R. Mooney, K. Saenko, and T. Darrell, “Deep compositional captioning: Describing novel object categories without paired training data,” in CVPR, 2016. [find](https://scholar.google.com/scholar?q=L%2E+A%2E+Hendricks%2C+S%2E+Venugopalan%2C+M%2E+Rohrbach%2C+R%2E+Mooney%2C+K%2E+Saenko%2C+and+T%2E+Darrell%2C+%E2%80%9CDeep+compositional+captioning%3A+Describing+novel+object+categories+without+paired+training+data%2C%E2%80%9D+in+CVPR%2C+2016%2E)
69. S. Venugopalan, H. Xu, J. Donahue, M. Rohrbach, R. Mooney, and K. Saenko, “Translating videos to natural language using deep recurrent neural networks,” in NAACL, 2015. [find](https://scholar.google.com/scholar?q=S%2E+Venugopalan%2C+H%2E+Xu%2C+J%2E+Donahue%2C+M%2E+Rohrbach%2C+R%2E+Mooney%2C+and+K%2E+Saenko%2C+%E2%80%9CTranslating+videos+to+natural+language+using+deep+recurrent+neural+networks%2C%E2%80%9D+in+NAACL%2C+2015%2E)
70. S. Venugopalan, M. Rohrbach, J. Donahue, R. Mooney, T. Darrell, and K. Saenko, “Sequence to sequence–video to text,” in ICCV, 2015. [find](https://scholar.google.com/scholar?q=S%2E+Venugopalan%2C+M%2E+Rohrbach%2C+J%2E+Donahue%2C+R%2E+Mooney%2C+T%2E+Darrell%2C+and+K%2E+Saenko%2C+%E2%80%9CSequence+to+sequence%E2%80%93video+to+text%2C%E2%80%9D+in+ICCV%2C+2015%2E)
71. L. Yao, A. Torabi, K. Cho, N. Ballas, C. Pal, H. Larochelle, and A. Courville, “Describing videos by exploiting temporal structure,” in CVPR, vol. 1050, 2015. [find](https://scholar.google.com/scholar?q=L%2E+Yao%2C+A%2E+Torabi%2C+K%2E+Cho%2C+N%2E+Ballas%2C+C%2E+Pal%2C+H%2E+Larochelle%2C+and+A%2E+Courville%2C+%E2%80%9CDescribing+videos+by+exploiting+temporal+structure%2C%E2%80%9D+in+CVPR%2C+vol%2E+1050%2C+2015%2E)
72. A. Rohrbach, M. Rohrbach, R. Hu, T. Darrell, and B. Schiele, “Grounding of textual phrases in images by reconstruction,” arXiv preprint arXiv:1511.03745, Tech. Rep., 2015. — [arXiv:1511.03745](https://arxiv.org/abs/1511.03745) [find](https://scholar.google.com/scholar?q=A%2E+Rohrbach%2C+M%2E+Rohrbach%2C+R%2E+Hu%2C+T%2E+Darrell%2C+and+B%2E+Schiele%2C+%E2%80%9CGrounding+of+textual+phrases+in+images+by+reconstruction%2C%E2%80%9D+arXiv+preprint+arXiv%3A1511%2E03745%2C+Tech%2E+Rep%2E%2C+2015%2E+%E2%80%94+%5BarXiv%3A1511%2E03745%5D%28https%3A%2F)
73. R. Hu, H. Xu, M. Rohrbach, J. Feng, K. Saenko, and T. Darrell, “Natural language object retrieval,” in CVPR, 2016. Jeff Donahue is a PhD student at the University of California, Berkeley, advised by Prof. Trevor Darrell. His research focuses on the use of deep learning for computer vision applicatio… [find](https://scholar.google.com/scholar?q=R%2E+Hu%2C+H%2E+Xu%2C+M%2E+Rohrbach%2C+J%2E+Feng%2C+K%2E+Saenko%2C+and+T%2E+Darrell%2C+%E2%80%9CNatural+language+object+retrieval%2C%E2%80%9D+in+CVPR%2C+2016%2E+Jeff+Donahue+is+a+PhD+student+at+the+University+of+California%2C+Berkeley%2C+advised+by+P)