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ICCV 2019 Best Papers Announced
ICCV 2019 today announced its Best Paper Awards in three categories. The ICCV (IEEE International Conference on Computer Vision) is a top international biannual computer vision gathering comprising a main conference and several co-located workshops and tutorials. ICCV 2019 received 4,303 papers — more than twice the number submitted to ICCV 2017 — and accepted 1,075, for a reception rate of roughly 25 percent.
ICCV 2019 | Best Paper Award (Marr Prize):SinGAN: Learning a Generative Model from a Single Natural Image
ICCV 2019 | Best Student Paper Award:PLMP — Point-Line Minimal Problems in Complete Multi-View Visibility
ICCV 2019 | Best Paper Honorable Mentions
Paper: Asynchronous Single-Photon 3D Imaging
Paper: Specifying Object Attributes and Relations in Interactive Scene Generation
Google AI Beats Top Human Players at Strategy Game StarCraft II
Players of the science-fiction video game StarCraft II faced an unusual opponent this summer. An artificial intelligence (AI) known as AlphaStar — which was built by Google’s AI firm DeepMind — achieved a grandmaster rating after it was unleashed on the game’s European servers, placing within the top 0.15% of the region’s 90,000 players.
(Nature) / (Paper) / (DeepMind Blog)/ (Watch the video)
CoRL 2019 Announces Best Paper Awards
The Conference on Robot Learning (CoRL) is a new annual international conference focusing on the intersection of robotics and machine learning.
CoRL 2019 | Best Paper Award
A Divergence Minimization Perspective on Imitation Learning Methods
CoRL 2019 | Best System Paper Award
Learning to Manipulate Object Collections Using Grounded State Representations
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
Researchers present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable, two-phase approach consists of an imitation learning stage resulting in goal-conditioned hierarchical policies that can be easily improved using fine-tuning via reinforcement learning in the subsequent phase.
Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation
The fundamental challenge of planning for multi-step manipulation is to find effective and plausible action sequences that lead to the task goal. Researchers present Cascaded Variational Inference (CAVIN) Planner, a model-based method that hierarchically generates plans by sampling from latent spaces.
(Stanford University & Nvidia &University of Toronto & Vector Institute)
Prescribed Generative Adversarial Networks
In this paper, researchers develop the prescribed GAN (PresGAN) to address these shortcomings. PresGANs add noise to the output of a density network and optimize an entropy-regularized adversarial loss. The added noise renders tractable approximations of the predictive log-likelihood and stabilizes the training procedure.
(Columbia University & University of Cambridge & DeepMind)
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