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Deep learning powers a motion-tracking revolution
- CoffeePython 7y agoInteresting to see deep learning techniques entering the realms of non-cs focused academia. I recently heard of another example from the Planet Money podcast[1], that used deep learning to perform an elephant census. One of my favorite things about ML is using it to reduce human labor hours on tasks like these. [1]https://www.npr.org/2019/08/09/749938354/episode-932-deep-learning-with-the-elephants https://www.npr.org/2019/08/09/749938354/episode-932-deep-le...
- sp332 7y agoImage segmentation and 3d pose estimation are not new. I'd like to see how the new deep learning approaches compare to the existing state of the art.
- bonoboTP 7y agoDeep learning approaches have been the state-of-the-art for these tasks for several years now. Other techniques just cannot compete.
- ska 7y agoThat's only partially true. Deep learning approaches for segmentation are state of the art where you have large labeling sets with high variability in the targets, but that hardly covers everything. The techniques are also being used where this isn't the case, of course, but with mixed results at best. Which should hardly be surprising, as transfer methods are hardly a panacea.
- bonoboTP 7y agoCan you give an example where a non-deep learning segmentation method outperforms deep learning algorithms? Do you mean static camera setups where traditional background subtraction works well enough? I would assume DL approaches match or outperform those even there.
- deleted 7y ago[deleted]
- ska 7y agoYou can't just look at segmentation tasks, but the availability of good labeling and sufficient training data. Given enough data of sufficient quality capturing the variability of your domain, and good quality labeling on it, you can probably make a deep model perform quite well. Failing to have these inputs, you will often do better with other techniques (there are many). Furthermore, if you have a decent physical model and/or some constraints (e.g. lots of industrial QA, cell counting, etc.) with a fixed FOV etc., you can do quite well with classic approaches and that can be quite robust. Some of the deep models you see performing well on e.g. small curated sets for conferences just don't generalize well at all, which isn't surprising given the setup.
- partyboat1586 7y agoThey are not fast enough for real time 3D pose estimation (60fps)
- bonoboTP 7y agoThere are DL methods that well exceed 60 fps for single-instance estimation (on high-end GPUs, especially on Turing or Volta cards with FP16). I would also be interested in any alternative approach that even comes to the ballpark of DL performance in pose estimation.
- sp332 7y agoThe Kinect could do pose estimation on 48 joints at 30 FPS on the Xbox 360's CPU.
- sp332 7y agoThat might have been true yesterday. But today, https://developer.nvidia.com/rtx-broadcast-engine https://developer.nvidia.com/rtx-broadcast-engine
- bitL 7y agoThis one doesn't need a huge separate dataset for each animal (~200 frames), just user has to define parts of animal they wish to track. Most 3D pose estimators require a huge 3D pose dataset for training.
- joeyo 7y agoThis is because of transfer learning: for example, by default, DeepLabCut starts with a pre-trained ResNet-50. Thus these trackers start out with an excellent "understanding" of the statistics of natural scenes out of the box.
- rpmisms 7y agoI wonder what the next iteration of new CS tech will be once we apply ML to every field where it's easily applied.
- bitL 7y agoYou can apply it on top of arbitrary combinations of fields where ML could be easily applied to.
- HaukeHi 7y agoDrones. https://arbital.com/p/AI_safety_mindset/ https://arbital.com/p/AI_safety_mindset/