Today we can see how computer vision (CV) systems are revolutionizing whole industries and business functions with successful applications in healthcare, security, transportation, retail, banking, agriculture, and more.
In 2019, we saw lots of novel architectures and approaches that further improved the perceptive and generative capacities of visual systems. To help you navigate through the overwhelming number of great computer vision papers presented this year, we’ve curated and summarized the top 10 CV research papers of 2019 that will help you understand the latest trends in this research area.
The papers that we selected cover optimization of convolutional networks, unsupervised learning in computer vision, image generation and evaluation of machine-generated images, visual-language navigation, captioning changes between two images with natural language, and more.
If you’d like to skip around, here are the papers we featured:
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- Learning the Depths of Moving People by Watching Frozen People
- Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation
- A Theory of Fermat Paths for Non-Line-of-Sight Shape Reconstruction
- Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection
- Fixing the Train-Test Resolution Discrepancy
- SinGAN: Learning a Generative Model from a Single Natural Image
- Local Aggregation for Unsupervised Learning of Visual Embeddings
- Robust Change Captioning
- HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models
Link : https://www.topbots.com/top-ai-vision-research-papers-2019/?utm_source=ActiveCampaign&utm_medium=email&utm_content=Efficient+reinforcement+learning%3A+the+latest+research&utm_campaign=Weekly+Newsletter+12+18+2019+Issue+172