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Seq2Seq Model & Text-to-Image Synthesis

Seq2Seq Model & Text-to-Image Synthesis. Presenter: Fuwen Tan. Today’s Class. Mini-batch training of the RNN model Special “End-of-Sequence” token: <end> Padding Sequence-to-sequence model Neural Machine Translation [1] Text-to-Image Synthesis [2].

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Seq2Seq Model & Text-to-Image Synthesis

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  1. Seq2Seq Model & Text-to-Image Synthesis • Presenter: Fuwen Tan

  2. Today’s Class • Mini-batch training of the RNN model • Special “End-of-Sequence” token: <end> • Padding • Sequence-to-sequence model • Neural Machine Translation[1] • Text-to-Image Synthesis[2] [1] Effective Approaches to Attention-based Neural Machine Translation. Thang Luong, Hieu Pham, and Christopher D. Manning. EMNLP 2015 [2] Text2Scene: Generating Compositional Scenes from Textual Descriptions. Fuwen Tan, Song Feng, Vicente Ordonez. CVPR 2019.

  3. A RNN model will never end “Hello”, “world”, “!”, “!”, “!”, “!”, “!”, …

  4. Unless: set the maximum length before hand I want sentences of 5 words

  5. Or: learn to predict the END. Ground-truth: “Hello”, “world”, “<end>” Training: learn to generate the ground-truth sequence with “<end>”. Testing: generate the sequence until an “<end>” is predicted.

  6. Computing loss: what if #ground-truth != #prediction loss1 loss2 loss3

  7. Mini-batch training: padding

  8. Mini-batch training: padding

  9. Generating text that makes sense: Language Model un étudiant <end> suis je h1 h2 h3 h5 h4 h0 étudiant un suis je <start> Unconditional: h0 = 0

  10. Generating text with a goal:Machine Translation un étudiant <end> suis je 1 2 3 4 h0 h1 h2 h3 h5 h4 étudiant student a un suis am je <start> I Conditional: h0 = 4 [3] Sequence to Sequence Learning with Neural Networks. Ilya Sutskever, Oriol Vinyals, Quoc V. Le. NeurIPS 2014.

  11. Seq2Seq model “suis” Seq2Seq:

  12. Seq2Seq model with perfect word alignments “suis” Seq2Seq: “suis” Ideally:

  13. Seq2Seq model with perfect word alignments “suis” Seq2Seq: Ideally: “suis” Or: “suis”

  14. Seq2Seq model with attention Ideally: “suis” “suis” In practice: Pray that is true Or train the model such thatis almost true

  15. Seq2Seq model with attention Key assumption: [1] Effective Approaches to Attention-based Neural Machine Translation. Thang Luong, Hieu Pham, and Christopher D. Manning. EMNLP 2015

  16. Seq2Seq model with attention “suis” [1] Effective Approaches to Attention-based Neural Machine Translation. Thang Luong, Hieu Pham, and Christopher D. Manning. EMNLP 2015

  17. Perform much better for long sequences [1] Effective Approaches to Attention-based Neural Machine Translation. Thang Luong, Hieu Pham, and Christopher D. Manning. EMNLP 2015

  18. Also very helpful in image captioning [4] Show, attend and tell: neural image caption generation with visual attention. Kelvin Xu, Jimmy Lei Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, YoshuaBengio. ICML 2015

  19. ECCV 2018 accepted 776 papers 38 of them with “attention” in their titles

  20. Seq2Seq vs Text-to-Image Synthesis Image: composition of patches Sentence: composition of words

  21. Can we do this? 1 2 3 4 h0 h1 h2 hotdog a holds Mike

  22. Challenges Machine Translation: “I amastudent”  “jesuisunétudiant” Text-to-Image Synthesis: "A person is holding a surfboard"

  23. Challenges: in each step Machine Translation: student  étudiant Text-to-Image Synthesis: object category: person, surfboard "A person is holding a surfboard" location: somewhere in the 2D world attributes: size, pose, expression, …

  24. Challenges: in each step Text-to-Image Synthesis: object category: person "A person is holding a surfboard" location: somewhere in the 2D world attributes: size, pose, expression, … Learning the distributions of categories, locations, attributes from the training samples

  25. 1 2 3 4 h0 h1 h2 hotdog a holds Mike

  26. Task 1: Abstract Scene Generation “Mike is surprised at the duck. The duck is standing on the grill. Jenny is running towards Mike and the duck.”

  27. Task 1: Abstract Scene Generation Object category 58 clip-art objects Location 28 x 28 grid 3 sizes, 2 orientations, 7 poses and 5 expressions for “Mike” and “Jenny”. Attributes

  28. Task 2: Scene Layout Generation “A guy on a motorcycle with some people watching.”

  29. Task 2: Scene Layout Generation 80 object categories from COCO: “person”, “car”, “chair”,… Object category Location 28 x 28 grid Attributes 17 sizes, 17 aspect-ratios

  30. Task 3: Composite Image Generation “Several elephants walking together in a line near water.”

  31. Task 3: Composite Image Generation 95 object & stuff categories from COCO: “person”, “grass”, “sky”,… Object category Location 32 x 32 grid 17 sizes, 17 aspect-ratios Attributes a feature vector for patch retrieval

  32. Step-by-step generation of Abstract Scene

  33. Step-by-step generation of composite image

  34. Step-by-step generation of composite image

  35. More examples

  36. More examples

  37. More examples

  38. More examples

  39. Questions?

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