6 ms·

Do vision transformers see like convolutional neural networks?

110 points by jonbaer 5y ago

kettleballroll 5y ago
It's always nice to see big labs working more towards building an understanding of things instead of just chasing SOTA. On the other hand, I'm not sure there is a lot of actionable findings in here. I guess that's the trade off with these things though....
- sdenton4 5y ago
  Demonstrating really different representations is a good indicator that ensembles are worth trying.
omarhaneef 5y ago
A useful HN feature would be small space to put in a summary, like the abstract: Convolutional neural networks (CNNs) have so far been the de-facto model for visual data. Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or even superior performance on image classification tasks. This raises a central question: how are Vision Transformers solving these tasks? Are they acting like convolutional networks, or learning entirely different visual representations? Analyzing the internal representation structure of ViTs and CNNs on image classification benchmarks, we find striking differences between the two architectures, such as ViT having more uniform representations across all layers. We explore how these differences arise, finding crucial roles played by self-attention, which enables early aggregation of global information, and ViT residual connections, which strongly propagate features from lower to higher layers. We study the ramifications for spatial localization, demonstrating ViTs successfully preserve input spatial information, with noticeable effects from different classification methods. Finally, we study the effect of (pretraining) dataset scale on intermediate features and transfer learning, and conclude with a discussion on connections to new architectures such as the MLP-Mixer.
- p_j_w 5y ago
  This would be handy, but at the same time, I think I like that not doing so encourages people to click the link and read more of the article than they might otherwise.
- codetrotter 5y ago
  > A useful HN feature would be small space to put in a summary, like the abstract Interestingly, HN does actually save accompanying text when you submit a link also. It just doesn’t show the text on the website. https://news.ycombinator.com/item?id=28180298 https://news.ycombinator.com/item?id=28180298 Personally I like it the way that it is. I think showing an accompanying text for links would allow too much for anyone posting a link to “force” everyone to read their comment on it. Leaving it so that comments must be posted separately in order to be visible in the thread makes it so that useful accompanying comments can float to the top, whereas a useless comment from the submitter sinks to the bottom while still allowing the submitted link to be voted on individually.
  - omarhaneef 5y ago
    I guess this is really in response to all the other responses as well, but I thought the idea would be: to help people decide if they want to click the link. So the title may not be sufficiently informative to let people know if they can understand the article, are interested in it, if it is at the right technical level and so on. I think you're right that it will be abused in many instances and might not be worth it.
- detaro 5y ago
  There is little point in duplicating the first thing you see on the page on HN too.
- 6gvONxR4sf7o 5y ago
  Is the idea to not make people click the links before discussing?
mrfusion 5y ago
It makes sense to me that attention would be hugely beneficial for vision tasks. We use contextual clues every day to decide what we’re looking at.
- sillysaurusx 5y ago
  It may make sense, but it also makes no sense. CNNs already have full view of the entire input image. That's how discriminators are able to discriminate in GANs. We added attention and observed no benefits at all in our GAN experiments.
mrfusion 5y ago
Does tesla use transformers for the auto pilot?
- sillysaurusx 5y ago
  Doubtful. The biggest downside of transformers for vision is how ungodly long they take to produce results. Tesla has to operate in realtime.
  - gamegoblin 5y ago
    In Karpathy’s recent AI day presentation he specifically stated they use transformers. But not on the raw camera input — they use regnets for that. The transformers come higher up the stack: https://youtu.be/j0z4FweCy4M https://youtu.be/j0z4FweCy4M Transformers mentioned on the slide at timestamp 1:00:18.
    - sillysaurusx 5y ago
      Thank you!
    - tandem5000 5y ago
      They use the key-value lookup/routing mechanism from Transformers to predict pixel-wise labels in bird view (lane, car, obstacle, intersection etc.). The motivation here is that some of the predictions may temporarily be occluded, so for predicting these occluded areas it may be particularly helpful to attend to remote regions in the input images which requires long-range dependencies that highly depend on the input itself (e.g. on whether there is an occlusion), which is exactly where the key-value mechanism excels. Not sure they even process past camera frames at this point. They only mention that later in the pipline they have an LSTM-like NN incorporating past camera frames (Schmidhuber will be proud!!). Edit: A random observation which just occurred to me is that their predictions seem surprisingly temporally unstable. Observe, for example, the lane layout wildly changing while it drives makes a left-turn at the intersection (https://youtu.be/j0z4FweCy4M?t=2608 https://youtu.be/j0z4FweCy4M?t=2608). You can use the comma and period keys to step through the video frame-by-frame.
mrfusion 5y ago
Offtopic sort of, but does anyone know if folks are working on combining vision and natural language in one model? I think that could wield some interesting results.
- abalaji 5y ago
  yeah there has definitely been work done in that space: it’s called multi-modal models not sure if this is the latest work but here’s some results from Google’s AI Blog https://ai.googleblog.com/2017/06/multimodel-multi-task-machine-learning.html?m=1 https://ai.googleblog.com/2017/06/multimodel-multi-task-mach...
  - im3w1l 5y ago
    What would be really cool is neural networks with routing. Like circuit switching or packet switching. No idea how you would train such a beast though. Like imagine the vision part making a phonecall to the natural language part to ask it for help with something.
    - TylerLives 5y ago
      Sounds like The Society of Mind - https://en.m.wikipedia.org/wiki/Society_of_Mind https://en.m.wikipedia.org/wiki/Society_of_Mind
    - wittenator 5y ago
      Capsule networks have a routing algorithm as far as I know
- sillysaurusx 5y ago
  https://github.com/openai/CLIP https://github.com/openai/CLIP The results are quite interesting: https://www.reddit.com/r/Art/comments/p866wv/deep_dive_meai_model_digital_2021/ https://www.reddit.com/r/Art/comments/p866wv/deep_dive_meai_...
  - codetrotter 5y ago
stared 5y ago
I am much more interested if they fall for the same tricks. For example, if it is easy to fool them with optical illusions, such as innocent images that look racy at the first glance: https://medium.com/@marekkcichy/does-ai-have-a-dirty-mind-too-6948430e4b2b https://medium.com/@marekkcichy/does-ai-have-a-dirty-mind-to... CW: Even though it does not contain a single explicit picture, it might be considered NSFW (literally - as at the first glance it looks like nudity); full disclosure: I mentored the project.
- PartiallyTyped 5y ago
  I suggest you take a look at Geometric Deep Learning, but the gist here is that convolutions can be thought of as translation equivariant functions, and pooling operations as permutation invariant combinations, all operating on a graph of as many components as the number of times the operation will be output, each component is composed of the pixels that the operation will act on, so there is local information that is slowly combined through the layers, then relative positioning can be decoded when the representation is transformed into a dense 1d-vector aka flattening. In contrast, attention mechanisms in transformers can be seen as taking into consideration dense graphs of the whole input (at least in text, I haven't really worked with vision transformers but if an attention mechanism exists then it should be similar), along with some positional encoding and a neighborhood summary. If they indeed can be thought as stacking neighborhood summaries along with attention mechanisms, then they shouldn't fall for the same tricks since they have access to "global" information instead of disconnected components. But take this reply with a grain of salt as I am still learning about Geometric DL. If I misunderstood something, please correct me.
  - deleted 5y ago
    [deleted]
  - dpflan 5y ago
    Thank you for bringing up GDL. I've been following its developments, and a really great resource is this site: https://geometricdeeplearning.com/ https://geometricdeeplearning.com/ It contains links to the paper and lectures, and the keynote by M. Bronstein is illuminating and discusses the operations on graphs that lead to equivalence to other network topologies and designs: transformer equivalence, and more. > Keynote: https://www.youtube.com/watch?v=9cxhvQK9ALQ https://www.youtube.com/watch?v=9cxhvQK9ALQ How are you learning/studying GDL? Would you like someone else to discuss/learn it with?

110 points by jonbaer 5y ago

It's always nice to see big labs working more towards building an understanding of things instead of just chasing SOTA. On the other hand, I'm not sure there is a lot of actionable findings in here. I guess that's the trade off with these things though....

sdenton4 5y ago
Demonstrating really different representations is a good indicator that ensembles are worth trying.

omarhaneef 5y ago

A useful HN feature would be small space to put in a summary, like the abstract: Convolutional neural networks (CNNs) have so far been the de-facto model for visual data. Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or even superior performance on image classification tasks. This raises a central question: how are Vision Transformers solving these tasks? Are they acting like convolutional networks, or learning entirely different visual representations? Analyzing the internal representation structure of ViTs and CNNs on image classification benchmarks, we find striking differences between the two architectures, such as ViT having more uniform representations across all layers. We explore how these differences arise, finding crucial roles played by self-attention, which enables early aggregation of global information, and ViT residual connections, which strongly propagate features from lower to higher layers. We study the ramifications for spatial localization, demonstrating ViTs successfully preserve input spatial information, with noticeable effects from different classification methods. Finally, we study the effect of (pretraining) dataset scale on intermediate features and transfer learning, and conclude with a discussion on connections to new architectures such as the MLP-Mixer.

p_j_w 5y ago
This would be handy, but at the same time, I think I like that not doing so encourages people to click the link and read more of the article than they might otherwise.
codetrotter 5y ago
> A useful HN feature would be small space to put in a summary, like the abstract Interestingly, HN does actually save accompanying text when you submit a link also. It just doesn’t show the text on the website. https://news.ycombinator.com/item?id=28180298 https://news.ycombinator.com/item?id=28180298 Personally I like it the way that it is. I think showing an accompanying text for links would allow too much for anyone posting a link to “force” everyone to read their comment on it. Leaving it so that comments must be posted separately in order to be visible in the thread makes it so that useful accompanying comments can float to the top, whereas a useless comment from the submitter sinks to the bottom while still allowing the submitted link to be voted on individually.
- omarhaneef 5y ago
  I guess this is really in response to all the other responses as well, but I thought the idea would be: to help people decide if they want to click the link. So the title may not be sufficiently informative to let people know if they can understand the article, are interested in it, if it is at the right technical level and so on. I think you're right that it will be abused in many instances and might not be worth it.
detaro 5y ago
There is little point in duplicating the first thing you see on the page on HN too.
6gvONxR4sf7o 5y ago
Is the idea to not make people click the links before discussing?

mrfusion 5y ago

It makes sense to me that attention would be hugely beneficial for vision tasks. We use contextual clues every day to decide what we’re looking at.

sillysaurusx 5y ago
It may make sense, but it also makes no sense. CNNs already have full view of the entire input image. That's how discriminators are able to discriminate in GANs. We added attention and observed no benefits at all in our GAN experiments.

mrfusion 5y ago

Does tesla use transformers for the auto pilot?

sillysaurusx 5y ago
Doubtful. The biggest downside of transformers for vision is how ungodly long they take to produce results. Tesla has to operate in realtime.
- gamegoblin 5y ago
  In Karpathy’s recent AI day presentation he specifically stated they use transformers. But not on the raw camera input — they use regnets for that. The transformers come higher up the stack: https://youtu.be/j0z4FweCy4M https://youtu.be/j0z4FweCy4M Transformers mentioned on the slide at timestamp 1:00:18.
  - sillysaurusx 5y ago
    Thank you!
  - tandem5000 5y ago
    They use the key-value lookup/routing mechanism from Transformers to predict pixel-wise labels in bird view (lane, car, obstacle, intersection etc.). The motivation here is that some of the predictions may temporarily be occluded, so for predicting these occluded areas it may be particularly helpful to attend to remote regions in the input images which requires long-range dependencies that highly depend on the input itself (e.g. on whether there is an occlusion), which is exactly where the key-value mechanism excels. Not sure they even process past camera frames at this point. They only mention that later in the pipline they have an LSTM-like NN incorporating past camera frames (Schmidhuber will be proud!!). Edit: A random observation which just occurred to me is that their predictions seem surprisingly temporally unstable. Observe, for example, the lane layout wildly changing while it drives makes a left-turn at the intersection (https://youtu.be/j0z4FweCy4M?t=2608 https://youtu.be/j0z4FweCy4M?t=2608). You can use the comma and period keys to step through the video frame-by-frame.

mrfusion 5y ago

Offtopic sort of, but does anyone know if folks are working on combining vision and natural language in one model? I think that could wield some interesting results.

abalaji 5y ago
yeah there has definitely been work done in that space: it’s called multi-modal models not sure if this is the latest work but here’s some results from Google’s AI Blog https://ai.googleblog.com/2017/06/multimodel-multi-task-machine-learning.html?m=1 https://ai.googleblog.com/2017/06/multimodel-multi-task-mach...
- im3w1l 5y ago
  What would be really cool is neural networks with routing. Like circuit switching or packet switching. No idea how you would train such a beast though. Like imagine the vision part making a phonecall to the natural language part to ask it for help with something.
  - TylerLives 5y ago
    Sounds like The Society of Mind - https://en.m.wikipedia.org/wiki/Society_of_Mind https://en.m.wikipedia.org/wiki/Society_of_Mind
  - wittenator 5y ago
    Capsule networks have a routing algorithm as far as I know
sillysaurusx 5y ago
https://github.com/openai/CLIP https://github.com/openai/CLIP The results are quite interesting: https://www.reddit.com/r/Art/comments/p866wv/deep_dive_meai_model_digital_2021/ https://www.reddit.com/r/Art/comments/p866wv/deep_dive_meai_...
- codetrotter 5y ago

stared 5y ago

I am much more interested if they fall for the same tricks. For example, if it is easy to fool them with optical illusions, such as innocent images that look racy at the first glance: https://medium.com/@marekkcichy/does-ai-have-a-dirty-mind-too-6948430e4b2b https://medium.com/@marekkcichy/does-ai-have-a-dirty-mind-to... CW: Even though it does not contain a single explicit picture, it might be considered NSFW (literally - as at the first glance it looks like nudity); full disclosure: I mentored the project.

PartiallyTyped 5y ago
I suggest you take a look at Geometric Deep Learning, but the gist here is that convolutions can be thought of as translation equivariant functions, and pooling operations as permutation invariant combinations, all operating on a graph of as many components as the number of times the operation will be output, each component is composed of the pixels that the operation will act on, so there is local information that is slowly combined through the layers, then relative positioning can be decoded when the representation is transformed into a dense 1d-vector aka flattening. In contrast, attention mechanisms in transformers can be seen as taking into consideration dense graphs of the whole input (at least in text, I haven't really worked with vision transformers but if an attention mechanism exists then it should be similar), along with some positional encoding and a neighborhood summary. If they indeed can be thought as stacking neighborhood summaries along with attention mechanisms, then they shouldn't fall for the same tricks since they have access to "global" information instead of disconnected components. But take this reply with a grain of salt as I am still learning about Geometric DL. If I misunderstood something, please correct me.
- deleted 5y ago
  [deleted]
- dpflan 5y ago
  Thank you for bringing up GDL. I've been following its developments, and a really great resource is this site: https://geometricdeeplearning.com/ https://geometricdeeplearning.com/ It contains links to the paper and lectures, and the keynote by M. Bronstein is illuminating and discusses the operations on graphs that lead to equivalence to other network topologies and designs: transformer equivalence, and more. > Keynote: https://www.youtube.com/watch?v=9cxhvQK9ALQ https://www.youtube.com/watch?v=9cxhvQK9ALQ How are you learning/studying GDL? Would you like someone else to discuss/learn it with?