6 accepted papers
This paper presents Bayesian-inspired Space-Time Superpixels (BIST): a fast, state-of-the-art method to compute space-time superpixels. BIST is a novel extension of a single-image Bayesian method named BASS, and it is inspired by hill-climbing to a local mode of a Dirichlet-Process Gaussian Mixture…
Image generation today can produce somewhat realistic images from text prompts. However, if one asks the generator to synthesize a specific camera setting such as creating different fields of view using a 24mm lens versus a 70mm lens, the generator will not be able to interpret and generate scene-co…
"Blind deconvolution problems are severely ill-posed because neither the underlying signal nor the forward operator are not known exactly. Conventionally, these problems are solved by alternating between estimation of the image and kernel while keeping the other fixed. In this paper, we show that th…
Accurately estimating the human pose is an essential task for many applications in robotics. However, existing pose estimation methods suffer from poor performance when occlusion occurs. Recent advances in NLP have been very successful in predicting the missing words conditioned on visible words. We…
Generalization to novel domains is a fundamental challenge for computer vision. Near-perfect accuracy on benchmarks is common, but these models do not work as expected when deployed outside of the training distribution. To build computer vision systems that truly solve real-world problems at global…
When training an estimator such as a neural network for tasks like image denoising, it is often preferred to train one estimator and apply it to all noise levels. The de facto training protocol to achieve this goal is to train the estimator with noisy samples whose noise levels are uniformly distrib…