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3 days participate in #CVPR2020 conference. excited about a lot of interesting subjects covered in computer vision: Adversarial Learning, Effective training and inference, representation learning...

Will do a write-up later.
#CVPR20 #computervision
Some preferred papers so far 👇
1. Dynamic Graph Message Passing Networks…
It addresses the modelling long-range dependencies problem by using feature map as a feature vector nodes and dynamically sample the neighborhood of a node from the feature graph.
2. Semantic Pyramid for Image Generation…
A generative image model that can leverage the feature space from different semantic levels learned by a pretrained classification network. many generative applications to play with
3. Momentum Contrast for Unsupervised Visual Representation Learning…
Inspired by idea of Bert in #nlp, the authors propose to contruct a large and consistent dictionnaries for unsupervised learning with constrative loss.
Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics…
I found it quite similar representation learning idea as Electra in #nlp. It proposes Limited Context Inpainting by masking regions of image and disciminate different transformations Image
5. Revisting knowledge distillation via label smoothing regularization…
The authors challenge the common belief in knowledge distillation by showing that the student can also enhance the teacher significantly and poorly trained-teacher can help the student
6. PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization…
#FAIR researchers did an amazing job on human 3D model from a single image! impressive results!

7. EfficientDet: Scalable and Efficient Object Detection…
A paper on Model efficiency of object detection by introducing weighted bi-directional feature pyramid network (BiFPN) for efficient multi-scale feature fusion and a novel compound scaling method Image
8. Adversarial Latent Autoencoders…
The authors propose an autoencoder which can generate images with quality comparable to state-of-the-art GANs while also learning a less entangled representation.
Some impressive results with reconstruction of images: Image
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