第2回 NLP/CV最先端勉強会での「A Survey on Cross-modal Embedding」の発表資料です

1. A Survey
on Cross-Modal Embedding
( )

2. n
n @ymym3412
n nlpaper.challenge
n
n @ymas0315
n

3. n Cross-Modal Embedding
n Cross-Modal Retrieval
n Audio-Visual Embedding
n

4. Cross-Modal Embedding

5. Cross-Modal Embedding
n

6. Cross-Modal Embedding
n

7. Cross-Modal Embedding
nCross-Modal Retrieval
◦ 3D
◦ Adversarial Training Consistency Loss
nAudio-Visual Embedding
◦ Web
◦

8. Cross-Modal Retrieval

9. Cross-Modal Retrieval
n
n Text <-> Image
Wikipedia

10. Image/Tag
n
NUS-WIDE
Flickr / 18.6
Low-level
Pascal VOC
Flickr LabelMe 9963

11. Image/Text
n
Wikipedia
Wikipedia
Flickr-30k
Flickr 30000
16
Recipe1M
1M

12. Cross-Modal Retrieval
n
• Real-Valued Representation
• Binary Representation
• Unsupervised Method
• Pairwise based Method
• Supervised Method

13. Unsupervised Method
CCA
2
AutoEncoder

14. Ranking Method
Pairwise based Method

15. / /
Shared Space
Supervised Method

16. :
nLocalizing Moments in Video with Natural Language(ICCV2017)
◦
◦ Global Context

17. :
nAttentive Moment Retrieval in Videos (SIGIR2018)
°
° Attention
First

18. : 3D
nY2Seq2Seq: Cross-Modal Representation Learning for 3D Shape
and Text by Joint Reconstruction and Prediction of View and
Word Sequence (AAAI2019)
◦ 3D Cross-Modal Retrieval
◦ 3D

19. : Adversarial Training
nSelf-Supervised Adversarial Hashing Networks for Cross-Modal
Retrieval(CVPR2018)
◦
◦ Adversarial
Training =

20. : Adversarial Training
nCoupled CycleGAN: Unsupervised Hashing Network for Cross-
Modal Retrieval (AAAI2019)
◦ 2 GAN
◦ Outer Cycle GAN
◦ Inner Cycle GAN

21. : Consistency Loss
nLook, Imagine and Match: Improving Textual-Visual Cross-
Modal Retrieval with Generative Models(CVPR2018)
◦ Decoder
Adversarial Training
◦ Adversarial Training
Adversarial Training

22. : Consistency Loss
nLearning Cross-Modal Embeddings with Adversarial Networks
for Cooking Recipes and Food Images(CVPR2019)
◦ Metric Learning, Adversarial Training
Consistency Loss

23. nViLBERT: Pretraining Task-Agnostic Visiolinguistic
Representation for Vision-and-Language Tasks
◦ Vision Language BERT
◦ Vision->Language Language->Vision Attention Co-Attention
Transformer
◦ / Mask
◦ Vision/Language Encoder
BERT

24. Audio-Visual Embedding

25. Audio-Visual Embedding
Audio-Visual
n
◦ Audio Visual
⇒ Alignment
◦
⇒
n
◦
⇒ ” ”
( … )

26. Cross-modal retrieval
nAudio-Visual Embedding Network (AVE-Net)
◦
◦ DNN
n
◦ /
◦ Cross-modal Intra-modal
Audio-Visual

27. Audio-Visual
Cross-modal retrieval
nAudio-Visual Embedding Network (AVE-Net)
◦
◦ DNN
n
◦ /
◦ Cross-modal Intra-modal
nDCG@30
(Higher is better)

28. Audio-Visual
Audio-visual source separation
nLooking to Listen at the Cocktail Party
◦
◦ https://www.youtube.com/watch?v=rVQVAPiJWKU

29. Audio-Visual
Sound source localization
nLearning to Localize Sound Source in Visual Scenes
◦ attention
◦ Attention supervised

30. Audio-Visual
Image/sound generation
nSpeech2Face: Learning the Face Behind a Voice
◦ decoder

31. nYoutube 8M
◦
nAudioSet
◦ 632 2,084,320
nAVSpeech
◦ 29,000 ID
nYahoo Flickr Creative Commons 100M (YFCC100M)
◦ 80 (100M )
◦ Flickr Creative Commons
nVoxCeleb1, 2
◦ Youtube 2000

32. nSoundnet: Learning sound representations from unlabeled
video (NIPS2016)
◦
◦
◦ SVM
(Audio+Vision)

33. nLook, Listen and Learn (ICCV2017)
◦ visual audio
◦
(Audio-Visual Correspondence(AVC))
◦
AVC Audio-visual

34. nLook, Listen and Learn (ICCV2017)
◦ visual audio
◦
(Audio-Visual Correspondence(AVC))
◦
AVC Audio-visual

35. nObjects that Sound (ECCV2018)
◦ L3 ( )
◦ Cross-modal retrieval Sound source localization
AVC
AVOL-Net

36. nObjects that Sound (ECCV2018)
◦ L3 ( )
◦ Cross-modal retrieval Sound source localization
AVC
AVOL-Net

37. nAudio-Visual Scene Analysis with Self-Supervised Multisensory
Features (ECCV2018)
◦ ( )
◦ Action recognition
◦ Audio-visual source separation
Alignment

38. AVSS
nThe Sound of Pixels (ECCV2018)
◦ PixelPlayer
(http://sound-of-pixels.csail.mit.edu/)
◦
Mix-and-Separate
Mix-and-separate

39. AVSS
nThe Sound of Pixels (ECCV2018)
◦ PixelPlayer
(http://sound-of-pixels.csail.mit.edu/)
◦
Mix-and-Separate
Mix-and-separate

40. AVSS
nThe Sound of Pixels (ECCV2018)
◦ PixelPlayer
(http://sound-of-pixels.csail.mit.edu/)
◦
Mix-and-Separate
Mix-and-separate
K

41. AVSS
nThe Sound of Pixels (ECCV2018)
◦ PixelPlayer
(http://sound-of-pixels.csail.mit.edu/)
◦
Mix-and-Separate
Mix-and-separate

42. AVSS
nThe Sound of Pixels (ECCV2018)
◦ PixelPlayer
(http://sound-of-pixels.csail.mit.edu/)
◦
Mix-and-Separate
Mix-and-separate

43. AVSS
nThe Sound of Pixels (ECCV2018)
◦ PixelPlayer
(http://sound-of-pixels.csail.mit.edu/)
◦
Mix-and-Separate
Mix-and-separate

44. nSpeech2Face: Learning the Face Behind a Voice (CVPR2019)
◦
◦ Encoder
⇔

45. nTalking Face Generation by Adversarially Disentangled Audio-
Visual Representation (AAAI2019)
◦ /
◦ (disentangle )

46. nTalking Face Generation by Adversarially Disentangled Audio-
Visual Representation (AAAI2019)
◦ /
◦ (disentangle )

47. n Cross-Modal Embeddings Image
Text Cross-Modal Retrieval, Audio Vision Audio-Visual
Embeddings
n Cross-Modal Retrieval Image Text
Video 3D Adversarial Training
n Audio-Visual
n Image/Text/Audio/Video Cross-Modal ->
(
)

48. Kaiye Wang, Qiyue Yin, Wei Wang, Shu Wu, Liang Wang: A Comprehensive Survey on Cross-modal Retrieval
T.-S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y. Zheng: NUS-WIDE: A real-world web image database from National
University of Singapore
Sung Ju Hwang ; Kristen Grauman: Reading between the Lines: Object Localization Using Implicit Cues from Image
Tags
Peter Young Alice Lai Micah Hodosh Julia Hockenmaier: From image descriptions to visual denotations: New
similarity metrics for semantic inference over event descriptions
Hao Wang, Doyen Sahoo, Chenghao Liu, Ee-peng Lim, Steven C. H. Hoi: Learning Cross-Modal Embeddings with
Adversarial Networks for Cooking Recipes and Food Images
J. Zhou, G. Ding, and Y. Guo: Latent Semantic Sparse Hashing for Cross-Modal Similarity Search
Amaia Salvador1∗ Nicholas Hynes2∗ Yusuf Aytar2, Javier Marin2 Ferda Ofli3, Ingmar Weber3 Antonio Torralba2:
Learning Cross-modal Embeddings for Cooking Recipes and Food Images
Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Nicolas Thome, Matthieu Cord: Cross-Modal Retrieval in
the Cooking Context: Learning Semantic Text-Image Embeddings
Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, Sanja Fidler: VSE++: Improving Visual-Semantic Embeddings with
Hard Negatives
Alexander Hermans, Lucas Beyer, Bastian Leibe: In Defense of the Triplet Loss for Person Re-Identification
Chao Li, Cheng Deng, Ning Li, Wei Liu, Xinbo Gao, Dacheng Tao: Self-Supervised Adversarial Hashing Networks for
Cross-Modal Retrieval

49. Nikhil Rasiwasia1, Jose Costa Pereira1, Emanuele Coviello1, Gabriel Doyle2,
Gert R.G. Lanckriet1, Roger Levy2, Nuno Vasconcelos1: A New Approach to Cross-Modal Multimedia Retrieval
Ting Yao †, Tao Mei †, and Chong-Wah Ngo ‡† Microsoft Research, Beijing, China‡ City University of Hong Kong,
Kowloon, Hong Kong: Learning Query and Image Similarities with Ranking Canonical Correlation Analysis
Lisa Anne Hendricks1∗, Oliver Wang2, Eli Shechtman2, Josef Sivic2,3∗, Trevor Darrell1, Bryan Russell2: Localizing
Moments in Video with Natural Language
Zhu Zhang, Zhijie Lin, Zhou Zhao and Zhenxin Xiao: Attentive Moment Retrieval in Videos
Zhizhong Han1,2, Mingyang Shang1, Xiyang Wang1, Yu-Shen Liu1∗, Matthias Zwicker2: Y2Seq2Seq: Cross-Modal
Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences
Chao Li, Cheng Deng, Ning Li, Wei Liu, Xinbo Gao, Dacheng Tao: Self-Supervised Adversarial Hashing Networks for
Cross-Modal Retrieval
Chao Li,1 Cheng Deng,1∗ Lei Wang,1 De Xie,1 Xianglong Liu2†: Coupled CycleGAN: Unsupervised Hashing Network
for Cross-Modal Retrieval
Jiuxiang Gu, Jianfei Cai, Shafiq Joty, Li Niu, Gang Wang: Look, Imagine and Match: Improving Textual-Visual Cross-
Modal Retrieval with Generative Models
Jiasen Lu1, Dhruv Batra1,2, Devi Parikh1,2, Stefan Lee: ViLBERT: Pretraining Task-Agnostic Visiolinguistic
Representations for Vision-and-Language Task

50. Yusuf Aytar, Carl Vondrick, Antonio Torralba: SoundNet: Learning Sound Representations from Unlabeled Video
Relja Arandjelović, Andrew Zisserman: Objects that Sound
Relja Arandjelović, Andrew Zisserman: Look, Listen and Learn
Hang Zhao, Chuang Gan, Andrew Rouditchenko, Carl Vondrick, Josh McDermott, Antonio Torralba: The Sound of
Pixels
Andrew Owens, Alexei A. Efros: Audio-Visual Scene Analysis with Self-Supervised Multisensory Features
Tae-Hyun Oh, Tali Dekel, Changil Kim, Inbar Mosseri, William T. Freeman, Michael Rubinstein, Wojciech Matusik:
Speech2Face: Learning the Face Behind a Voice
Ariel Ephrat, Inbar Mosseri, Oran Lang, Tali Dekel, Kevin Wilson, Avinatan Hassidim, William T. Freeman, Michael
Rubinstein: Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation
Arda Senocak, Tae-Hyun Oh, Junsik Kim, Ming-Hsuan Yang, In So Kweon: Learning to Localize Sound Source in Visual
Scenes
Hang Zhou, Yu Liu, Ziwei Liu, Ping Luo, Xiaogang Wang:
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation