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Claim this profileWorks277 from public data
- Going deeper into action recognition: A survey651
This survey provides a comprehensive review of the notable steps taken towards recognizing human actions, starting with the pioneering methods that use handcrafted representations, and then, navigating into the realm of deep learning based approaches.
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- Hierarchical Neural Architecture Search for Deep Stereo Matching438
This paper proposes the first end-to-end hierarchical NAS framework for deep stereo matching by incorporating task-specific human knowledge into the neural architecture search framework and optimizing the architectures of the entire pipeline jointly.
- Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices316
To encode the geometry of the manifold in the mapping, a family of provably positive definite kernels on the Riemannian manifold of SPD matrices is introduced, derived from the Gaussian kernel, but exploit different metrics on the manifold.
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- Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels270
It is shown that many popular algorithms designed for Euclidean spaces, such as support vector machines, discriminant analysis and principal component analysis can be generalized to Riemannian manifolds with the help of such positive definite Gaussian kernels.
- A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation241
A Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume, is proposed.
- Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning240
A distillation algorithm is introduced to address the problem of FSCIL and a method based on an attention mechanism on multiple parallel embeddings of visual data to align visual and semantic vectors, which reduces issues related to catastrophic forgetting.
- Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods228
This paper proposes to model the mapping from the high-dimensional SPD manifold to the low-dimensional one with an orthonormal projection and shows that learning can be expressed as an optimization problem on a Grassmann manifold and discusses fast solutions for special cases.
- From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices214
An approach that lets us handle high-dimensional SPD matrices by constructing a lower-dimensional, more discriminative SPD manifold is introduced and leads to a significant accuracy gain over state-of-the-art methods.
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- Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach212
Experiments show that the proposed sparse coding approach achieves notable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as tensor sparse coding, Riemannian locality preserving projection, and symmetry-driven accumulation of local features.
- Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective207
This work presents a novel perspective to unsupervised saliency detection through learning from multiple noisy labeling generated by "weak" and "noisy" unsuper supervised handcrafted saliency methods.
- Spatio-temporal covariance descriptors for action and gesture recognition173
A new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors is proposed.
- Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution165
To handle non-linearity in data, this paper proposes to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which enables it to devise a closed-form solution for updating a Grassmann dictionary, atom by atom.
- On Learning the Geodesic Path for Incremental Learning164
This work proposes to firstly construct low-dimensional manifolds for previous and current responses and minimize the dissimilarity between the responses along the geodesic connecting the manifolds which induces a more formidable knowledge distillation with smooth properties which preserves the past knowledge more efficiently as observed by the comprehensive empirical study.
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- Distribution-matching embedding for visual domain adaptation119
This paper introduces a Distribution-Matching Embedding approach: An unsupervised domain adaptation method that overcomes this issue by mapping the data to a latent space where the distance between the empirical distributions of the source and target examples is minimized.
- Bregman Divergences for Infinite Dimensional Covariance Matrices114
This work proposes an approach to computing and comparing Covariance Descriptors (CovDs) in infinite-dimensional spaces by first mapping the original data to a high-dimensional Hilbert space, and only then compute the CovDs.
- Uncertainty-guided dual-views for semi-supervised volumetric medical image segmentation113
Empirical results suggest that an uncertainty-guided co-training framework can make two neural networks robust to data artefacts and have the ability to generate plausible segmentation masks that can be helpful for semi-automated segmentation processes.
- Learning an Invariant Hilbert Space for Domain Adaptation112
This paper introduces a learning scheme to construct a Hilbert space (i.e., a vector space along its inner product) to address both unsupervised and semi-supervised domain adaptation problems to simultaneously minimizing a notion of domain variance while maximizing a measure of discriminatory power.
- Implicit Motion Handling for Video Camouflaged Object Detection111
A new video camouflaged object detection framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames is proposed and unifies motion estimation and object segmentation within a single optimization framework.
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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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