We demonstrate a method to calculate high-precision benchmarks for the reflectance and transmittance of a finite rod with a stochastic cross section, assuming that the attenuation law has a known closed form and both the single-scattering albedo and scattering kernel are deterministic. We introduce new 10-digit values for an existing binary-Markov benchmark (including mean and variance), along with several new benchmarks defined for non-Markov binary mixtures and a continuous-fluctuation model featuring gamma stationary statistics.

We introduce a layered, heterogeneous spectral reflectance model for human skin. The model captures the inter-scattering of light among layers, each of which may have an independent set of spatially-varying absorption and scattering parameters. For greater physical accuracy and control, we introduce an infinitesimally thin absorbing layer between scattering layers. To obtain parameters for our model, we use a novel acquisition method that begins with multi-spectral photographs.

In the task of reference-based image inpainting, an additional reference image is provided to restore a damaged target image to its original state. The advancement of diffusion models, particularly Stable Diffusion, allows for simple formulations in this task. However, existing diffusion-based methods often lack explicit constraints on the correlation between the reference and damaged images, resulting in lower faithfulness to the reference images in the inpainting results.

Spatio-temporal action detection encompasses the tasks of localizing and classifying individual actions within a video. Recent works aim to enhance this process by incorporating interaction modeling, which captures the relationship between people and their surrounding context. However, these approaches have primarily focused on fully-supervised learning, and the current limitation lies in the lack of generalization capability to recognize unseen action categories. In this paper, we aim to adapt the pretrained image-language models to detect unseen actions.