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Works68 from public data
- A Riemannian Network for SPD Matrix Learning564
A Riemannian network architecture is built to open up a new direction of SPD matrix non-linear learning in a deep model and it is shown that the proposed SPD matrix network can be simply trained and outperform existing SPD matrix learning and state-of-the-art methods in three typical visual classification tasks.
- Deep Learning on Lie Groups for Skeleton-Based Action Recognition300
The Lie group structure is incorporated into a deep network architecture to learn more appropriate Lie group features for 3D action recognition and a logarithm mapping layer is proposed to map the resulting manifold data into a tangent space that facilitates the application of regular output layers for the final classification.
- Log-Euclidean metric learning on symmetric positive definite Manifold with application to image set classification269
This paper proposes a novel metric learning approach to work directly on logarithms of SPD matrices by learning a tangent map that can directly transform the matrix Log-Euclidean Metric from the original tangent space to a new tangentspace of more discriminability.
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- Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild213
The method for the Emotion Recognition in the Wild Challenge (EmotiW 2014) is presented, and an optimal fusion of classifiers learned from different kernels and different modalities (video and audio) is conducted at the decision level for further boosting the performance.
- Building Deep Networks on Grassmann Manifolds206
This paper proposes a deep network architecture by generalizing the Euclidean network paradigm to Grassmann manifolds and designs full rank mapping layers to transform input Grassmannian data to more desirable ones, and exploits re-orthonormalization layers to normalize the resulting matrices.
- Covariance Pooling for Facial Expression Recognition181
The benefits of using a manifold network structure for covariance pooling to improve facial expression recognition are explored and the advantage of pooling image-set features temporally by stacking the designed manifold network of covariance Pooling on top of convolutional network layers is demonstrated.
- Wasserstein Divergence for GANs167
A novel Wasserstein divergence (W-div) is proposed, which is a relaxed version of W-met and does not require the k-Lipschitz constraint and is introduced as a concrete application, which can faithfully approximate W-div through optimization.
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- Sliced Wasserstein Generative Models150
This paper proposes to approximate SWDs with a small number of parameterized orthogonal projections in an end-to-end deep learning fashion and designs two types of differentiable SWD blocks to equip modern generative frameworks---Auto-Encoders and Generative Adversarial Networks.
- Face recognition on large-scale video in the wild with hybrid Euclidean-and-Riemannian metric learning102
A novel Hybrid Euclidean-and-Riemannian Metric Learning (HERML) method to fuse multiple statistics of image set that has a clear superiority over the state-of-the-art set-based methods for large-scale video-based face recognition.
- Freestyle Layout-to-Image Synthesis95
This work introduces a new module called Rectified Cross-Attention (RCA) that can be conveniently plugged in the diffusion model to integrate semantic masks, and opts to leverage large-scale pre-trained text-to-image diffusion models to achieve the generation of unseen semantics.
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- A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials91
A continual deepfake detection benchmark (CDDB) over a new collection of deepfakes from both known and unknown generative models is suggested, which is clearly more challenging than the existing benchmarks, and offers a suitable evaluation avenue to the future research.
- Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search76
A new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search that exploits an off-policy GAN architecture search algorithm that makes efficient use of the samples generated by previous policies.
- Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video76
A novel metric learning framework to learn a distance metric across a Euclidean space and a Riemannian manifold to fuse average appearance and pattern variation of faces within one video to improve face recognition from videos is proposed.
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- Partial least squares regression on grassmannian manifold for emotion recognition65
For each video clip, all frames are represented as an image set, which can be modeled as a linear subspace to be embedded in Grassmannian manifold and an optimal fusion of classifiers learned from both modalities is conducted at decision level.
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- Direct Differentiable Augmentation Search48
This paper proposes an efficient differentiable search algorithm called Direct Differentiable Augmentation Search (DDAS), which exploits meta-learning with one-step gradient update and continuous relaxation to the expected training loss for efficient search.
- Riemannian Local Mechanism for SPD Neural Networks43
This work first analyses the convolution operator commonly used for capturing local information in Euclidean deep networks from the perspective of a higher level of abstraction afforded by category theory, and defines the local Information in the SPD manifold and designs a multi-scale submanifold block for mining local geometry.
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- Geometry-Aware Similarity Learning on SPD Manifolds for Visual Recognition43
A geometry-aware SPD similarity learning (SPDSL) framework to learn discriminative SPD features by directly pursuing a manifold-manifold transformation matrix of full column rank is proposed.
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 2026-10-16. 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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