Dynamic Convolution: Attention over Convolution Kernels

Channel:
Microsoft Research
Subscribers:
344,000
Published on ● Video Link: https://www.youtube.com/watch?v=FNkY7I2R_zM


Duration: 4:56
3,231 views
59

Learn all the ways Microsoft is a part of CVPR 2020: https://www.microsoft.com/en-us/research/event/cvpr-2020/



Other Videos By Microsoft Research


2020-06-14Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve
2020-06-14Combining Human and Machine Intelligence to Describe Images to People with Visual Impairments
2020-06-14Geometry Aware Satellite to Ground Image Synthesis for Urban Areas
2020-06-14Self Supervised Human Depth Estimation From Monocular Videos
2020-06-14LSM: Learning Subspace Minimization for Low-level Vision
2020-06-14MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask
2020-06-14MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation
2020-06-14Leveraging Photometric Consistency over Time for Sparsely Supervised Hand Object Reconstruction
2020-06-14Deep Shutter Unrolling Network
2020-06-14Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re W
2020-06-14Dynamic Convolution: Attention over Convolution Kernels
2020-06-10Supervised Deep Hashing for Efficient Audio Retrieval
2020-06-10Transparency and Intelligibility Throughout the Machine Learning Life Cycle
2020-06-10Machine Learning and Fairness Webinar
2020-06-10Consumer Brain-Computer Interfaces: From Science Fiction to Reality
2020-06-10Highly Conductive Flexible Sensor Integrated With Personal Devices For Practical Bio-Signal Measure
2020-06-08Microsoft Build 2020: Kevin Scott keynote with Lila Tretikov
2020-06-03Harvesting randomness, HAIbrid algorithms and safe AI with Dr. Siddhartha Sen | Podcast
2020-06-03Provably efficient reinforcement learning with Dr. Akshay Krishnamurthy | Podcast
2020-06-01What ‘bhasha’ do you want to talk in? With Kalika Bali and Dr. Monojit Choudhury | Podcast
2020-05-26Explaining Decisions from Vision Models and Correcting them via Human Feedback