623 research outputs found
Privileged Prior Information Distillation for Image Matting
Performance of trimap-free image matting methods is limited when trying to
decouple the deterministic and undetermined regions, especially in the scenes
where foregrounds are semantically ambiguous, chromaless, or high
transmittance. In this paper, we propose a novel framework named Privileged
Prior Information Distillation for Image Matting (PPID-IM) that can effectively
transfer privileged prior environment-aware information to improve the
performance of students in solving hard foregrounds. The prior information of
trimap regulates only the teacher model during the training stage, while not
being fed into the student network during actual inference. In order to achieve
effective privileged cross-modality (i.e. trimap and RGB) information
distillation, we introduce a Cross-Level Semantic Distillation (CLSD) module
that reinforces the trimap-free students with more knowledgeable semantic
representations and environment-aware information. We also propose an
Attention-Guided Local Distillation module that efficiently transfers
privileged local attributes from the trimap-based teacher to trimap-free
students for the guidance of local-region optimization. Extensive experiments
demonstrate the effectiveness and superiority of our PPID framework on the task
of image matting. In addition, our trimap-free IndexNet-PPID surpasses the
other competing state-of-the-art methods by a large margin, especially in
scenarios with chromaless, weak texture, or irregular objects.Comment: 15 pages, 7 figure
Interactive removal and ground truth for difficult shadow scenes
A user-centric method for fast, interactive, robust, and high-quality shadow removal is presented. Our algorithm can perform detection and removal in a range of difficult cases, such as highly textured and colored shadows. To perform detection, an on-the-fly learning approach is adopted guided by two rough user inputs for the pixels of the shadow and the lit area. After detection, shadow removal is performed by registering the penumbra to a normalized frame, which allows us efficient estimation of nonuniform shadow illumination changes, resulting in accurate and robust removal. Another major contribution of this work is the first validated and multiscene category ground truth for shadow removal algorithms. This data set containing 186 images eliminates inconsistencies between shadow and shadow-free images and provides a range of different shadow types such as soft, textured, colored, and broken shadow. Using this data, the most thorough comparison of state-of-the-art shadow removal methods to date is performed, showing our proposed algorithm to outperform the state of the art across several measures and shadow categories. To complement our data set, an online shadow removal benchmark website is also presented to encourage future open comparisons in this challenging field of research
Geodesic Distance Histogram Feature for Video Segmentation
This paper proposes a geodesic-distance-based feature that encodes global
information for improved video segmentation algorithms. The feature is a joint
histogram of intensity and geodesic distances, where the geodesic distances are
computed as the shortest paths between superpixels via their boundaries. We
also incorporate adaptive voting weights and spatial pyramid configurations to
include spatial information into the geodesic histogram feature and show that
this further improves results. The feature is generic and can be used as part
of various algorithms. In experiments, we test the geodesic histogram feature
by incorporating it into two existing video segmentation frameworks. This leads
to significantly better performance in 3D video segmentation benchmarks on two
datasets
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