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- Knowledge Distillation: A Survey4,737
A comprehensive survey of knowledge distillation from the perspectives of knowledge categories, training schemes, teacher–student architecture, distillation algorithms, performance comparison and applications is provided.
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- Camera self-calibration: Theory and experiments1,153
It is shown, using experiments with noisy data, that it is possible to calibrate a camera just by pointing it at the environment, selecting points of interest and then tracking them in the image as the camera moves.
- A theory of self-calibration of a moving camera819
The feasibility of camera calibration based on the epipolar transformation is demonstrated and two curves of degree six can be obtained in the dual plane such that one of the real intersections of the two yields the correct camera calibration.
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- Theory of Reconstruction from Image Motion365
Theory of Reconstruction from Image Motion presents the mathematics underlying the reconstruction of camera motion from the movements of points in the camera image, employing mathematical methods drawn from linear algebra, projective geometry, algebraic geometry, the theory of transversality and the theories of least squares approximation.
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- DCFNet: Discriminant Correlation Filters Network for Visual Tracking230
This paper presents an end-to-end lightweight network architecture, namely Discriminant Correlation Filter Network (DCFNet), where a differentiable DCF (discriminant correlation filter) layer is incorporated into a Siamese network architecture in order to learn the convolutional features and the correlation filter simultaneously.
- Asymmetric 3D Convolutional Neural Networks for action recognition209
The asymmetric 3D-CNN model outperforms all the traditional 3D-CNN models in both effectiveness and efficiency, and its performance is comparable with that of recent state-of-the-art action recognition methods on both benchmarks.
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- Bridging Composite and Real: Towards End-to-End Deep Image Matting174
This paper proposes a novel Glance and Focus Matting network (GFM), which employs a shared encoder and two separate decoders to learn both tasks in a collaborative manner for end-to-end natural image matting and finds that a carefully designed composition route RSSN that aims to reduce the discrepancies can lead to a better model with remarkable generalization ability.
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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 2026-10-14. 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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