🙈 Interesting findings
The most challenging for both human annotators and the DeepLabCut baseline is to predict the positions of shoulders and hips. Another point of failure for Neural Networks is self occlusions.

📝 Paper
🏛 Dataset
⚙️ Pretrained models in DeepLabCut Model Zoo
📓 Colab

CLIP + StyleGAN. Searching in StyleGAN latent space using description embedded with CLIP.

Queries: "A pony that looks like Beyonce", "... like Billie Eilish", ".. like Rihanna"

📐 The basic idea
Generate an image with StyleGAN and pass the image to CLIP for the loss against a CLIP text query representation. You then backprop through both networks and optimize a latent space in StyleGAN.

🤬 Drawbacks 1) it only works on text it knows 2) needs some cherry picking, only about 1/5 are really good.

Source twitt.

Robotics & Biomechanics lab in our Uni (Heidelberg, Germany).

GANs are getting their way into production

Adobe has rolled out a super-resolution feature for Photoshop. Now one can upscale the image x2 times on each side.

💎 For curious, I leave several links to SOTA super-resolution methods:
1. Structure-Preserving Super Resolution with Gradient Guidance (SPSR), CVPR2020
2. Learned Image Downscaling for Upscaling using Content Adaptive Resampler (CAR), ECCV2020
3. Single Image Super-Resolution via a Holistic Attention Network (HAN), ECCV2020
4. ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic, CVPR2021

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Let me know in comments if there is a better super-res paper.

Facebook AI has built TimeSformer, a new architecture for video understanding. It’s the first based exclusively on the self-attention mechanism used in Transformers. It outperforms the state of the art while being more efficient than 3D ConvNets for video.

❓Why it matters
To train video-understanding models, the best 3D CNNs today can only use video segments that are a few seconds long. With TimeSformer, we are able to train on far longer video clips — up to several minutes long. This may dramatically advance research to teach machines to understand complex long-form actions in videos, which is an important step for many AI applications geared toward human behavior understanding (e.g., an AI assistant).

Furthermore, the low inference cost of TimeSformer is an important step toward supporting future real-time video processing applications, such as AR/VR, or intelligent assistants that provide services based on video taken from wearable cameras.

🌐 FAIR Blog
📝 Paper

Fabulous DeepDream straight outta Mexico. The ambient sounds are well adjusted as well.

Who said that DeepDream is useless? 😂