We introduce OpenIllumination, a real-world dataset containing over 108K images of 64 objects with diverse materials, captured under 72 camera views and a large number of different illuminations. For each image in the dataset, we provide accurate camera parameters, illumination ground truth, and foreground segmentation masks. Our dataset enables the quantitative evaluation of most inverse rendering and material decomposition methods for real objects.

In this work, wepresent G2SAT,thefirst deep generativeframework that learns to generate SAT formulas from a given set of input formulas.

Wepresentgroupequivariant capsule networks,aframeworktointroduce guaranteed equivariance and invariance properties to the capsule network idea.

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

P and Q, respectively, is tight in P (X Y). Hero, 2004; Ghourchian et al., 2017), we present the outline of our argument into three steps: K P (X) is tight iff the closure of K is sequentially compact in P ( X) with respect to the weak convergence. Remark 1. W e could proceed differently by imposing stronger assumptions using the following W e briefly discuss the outline of the proof for the sake of completeness. (Loeve, 2017). The argument here depends on two important facts: 1.

China builds regulatory framework for low-altitude economy as AutoFlight unveils Matrix flying car with passenger flights planned by 2026. Officials expect baseline rules by 2027.

During the CL of VLMs, we need not only to prevent the catastrophic forgetting on incrementally learned knowledge but also to preserve the zero-shot ability of VLMs.