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- Self-Supervised deep homography estimation with invertibility constraints53
A novel homography estimation approach, named “Self-Supervised Regression Network(SSR-Net)”, which relaxes the need of ground truth annotations and takes advantage of invertibility constraints is proposed, which outperforms several state-of-the-art approaches.
- Scoot: A Perceptual Metric for Facial Sketches52
The results suggest that “spatial structure” and “co-occurrence texture” are two generally applicable perceptual features in face sketch synthesis, and the first largest scale human-perception-based sketch database that can evaluate how well a metric consistent with human perception is evaluated.
- DEL: Deep Embedding Learning for Efficient Image Segmentation52
A novel method called DEL (deep embedding learning) which can efficiently transform superpixels into image segmentation which can achieve comparable segments when compared with MCG but is much faster than it, i.e. 11.4fps vs. 0.07fps.
- Learning hybrid convolutional features for edge detection37
A novel convolutional neural network (CNN) based pipeline which can effectively fuse multi-level information extracted from different intermediate layers generating hybrid Convolutional features (HCF) for edge detection is presented.
- No-reference stereoscopic image quality evaluator with segmented monocular features and perceptual binocular features33
A novel NR SIQA model is proposed by the introduction of the meaningful superpixel visual patches and the significant binocular perception models that obtains a good consistency with the subjective scores, and has more reliable performance than previous works in terms of prediction accuracy and generalization capability.
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- Image Matching Benchmark1
This work presents the first application-oriented image matching benchmark to facilitate the analysis of matching algorithms in application level and demonstrates the effectiveness of a simple technique which is readily pluggable into any matching system to improve performance.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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