Human scene understanding uses a variety of visual and non-visual cues to perform inference on object types, poses, and relations. Physics is a rich and universal cue that we exploit to enhance scene understanding. In this paper, we integrate the physical cue of stability into the learning process by looping in a physics engine into bottom-up recognition models, and apply it to the problem of 3D scene parsing. We first show that applying physics supervision to an existing scene understanding model increases performance, produces more stable predictions, and allows training to an equivalent performance level with fewer annotated training examples. We then present a novel architecture for 3D scene parsing named Prim R-CNN, learning to predict bounding boxes as well as their 3D size, translation, and rotation. With physics supervision, Prim R-CNN outperforms existing scene understanding approaches on this problem. Finally, we show that finetuning with physics supervision on unlabeled real images improves real domain transfer of models training on synthetic data.

Iranian Crown Prince Reza Pahlavi said he is ready to lead Iran's transition as soon as the Islamic Republic falls, announcing plans for a transitional system under his leadership.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

LA school hid student's gender switch from parents before teen's suicide, lawsuit claims I looked like a monster after a car accident burned off my face... but a pioneering face transplant gave me my life back. America's heartland to see huge population plunge by 2050 - professor has a controversial visa plan to fix it Insufferable blowhard Stephen Colbert is being taken out like the trash... and thank God! What he's done is so diabolical: MAUREEN CALLAHAN JFK Jr's mortifying night of phone sex... day Sarah Jessica Parker ditched her underwear to seduce him in public... and the girlfriend he REALLY wanted to marry: All the women before Carolyn Truth about'super secretive' Michael B. Jordan's love life... and real reason he is perpetually single: Years of private'heartache' and'loneliness' laid bare I'm raising my two-year-old on a cruise ship These are the harsh realities of life at sea Extramarital sex with witches, cursed bloodlines and possessed politicians: DC's chief exorcist reveals the potent stench of evil among America's elite I ignored my itchy legs and cold-like symptoms. Then doctors discovered something horrifying on a scan... I'm terrified I'm going to die I made a 34-page dress code for my wedding guests... critics say I'm controlling but I want it to be perfect Trump's religious inner circle implodes as beauty queen's firing sparks revolt... and'spiritual adviser' faces shocking Israel claims China's sinister'Trojan horse' that has already breached America's gates and scooped up YOUR data We fled Trump to chase the REAL American dream in the most idyllic European hotspot... here's why we're coming back to a red state Harry and Meghan explode at claim the Queen accused Markle of'brainwashing' Iran's deadly drone arsenal is a'wake-up call for America': Expert warns US defenses may be unprepared for swarm attacks A US military drone expert has warned that Iranian attack drones could potentially slip through America's defenses and strike targets on US soil. Brett Velicovich, a former US Army intelligence and special operations soldier who spent years using drones to hunt ISIS leaders before founding drone company PowerUs, said the threat comes from a new type of warfare that the US is still struggling to defend against. 'These new asymmetric threats, where you've got low-cost, cheap, small drones, in some cases, that are able to be sent in massive waves, don't have the same signature of an intercontinental ballistic missile,' Velicovich explained.

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Zach Braff AI chatbot girlfriend rumors denied by the actor on Instagram after podcast speculation. The Scrubs star clarified he is not dating an AI chatbot and called claims false.

In this paper, we propose a novel maximum causal Tsallis entropy (MCTE) framework for imitation learning which can efficiently learn a sparse multi-modal policy distribution from demonstrations. We provide the full mathematical analysis of the proposed framework. First, the optimal solution of an MCTE problem is shown to be a sparsemax distribution, whose supporting set can be adjusted. The proposed method has advantages over a softmax distribution in that it can exclude unnecessary actions by assigning zero probability. Second, we prove that an MCTE problem is equivalent to robust Bayes estimation in the sense of the Brier score. Third, we propose a maximum causal Tsallis entropy imitation learning (MCTEIL) algorithm with a sparse mixture density network (sparse MDN) by modeling mixture weights using a sparsemax distribution. In particular, we show that the causal Tsallis entropy of an MDN encourages exploration and efficient mixture utilization while Shannon entropy is less effective.