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pro vyhledávání: '"Careaga, Chris"'
The low dynamic range (LDR) of common cameras fails to capture the rich contrast in natural scenes, resulting in loss of color and details in saturated pixels. Reconstructing the high dynamic range (HDR) of luminance present in the scene from single
Externí odkaz:
http://arxiv.org/abs/2409.13803
Autor:
Careaga, Chris, Aksoy, Yağız
Intrinsic image decomposition aims to separate the surface reflectance and the effects from the illumination given a single photograph. Due to the complexity of the problem, most prior works assume a single-color illumination and a Lambertian world,
Externí odkaz:
http://arxiv.org/abs/2409.13690
Despite significant advancements in network-based image harmonization techniques, there still exists a domain disparity between typical training pairs and real-world composites encountered during inference. Most existing methods are trained to revers
Externí odkaz:
http://arxiv.org/abs/2312.03698
Autor:
Careaga, Chris, Aksoy, Yağız
Intrinsic decomposition is a fundamental mid-level vision problem that plays a crucial role in various inverse rendering and computational photography pipelines. Generating highly accurate intrinsic decompositions is an inherently under-constrained t
Externí odkaz:
http://arxiv.org/abs/2311.12792
Flash is an essential tool as it often serves as the sole controllable light source in everyday photography. However, the use of flash is a binary decision at the time a photograph is captured with limited control over its characteristics such as str
Externí odkaz:
http://arxiv.org/abs/2306.06089
Advances in deep learning have resulted in state-of-the-art performance for many audio classification tasks but, unlike humans, these systems traditionally require large amounts of data to make accurate predictions. Not every person or organization h
Externí odkaz:
http://arxiv.org/abs/2012.01573
In the few-shot scenario, a learner must effectively generalize to unseen classes given a small support set of labeled examples. While a relatively large amount of research has gone into few-shot learning for image classification, little work has bee
Externí odkaz:
http://arxiv.org/abs/1909.09602
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