Codes
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition
Unsupervised Procedure Learning from Instructional Videos
Shared Attention for Multi-label Zero-shot Learning
- We address zero-shot multi-label learning for recognition of a large number of seen and unseen labels using a shared multi-attention method with a novel training mechanism.
- We provide the Python/Tensorflow implementation of LESA. When using the code in your research work, you should cite the following paper:
D. Huynh, E. Elhamifar, A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
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Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention
- We develop a zero-shot fine-grained recognition method with the ability to localize attributes using a dense attribute-based attention and embedding mechanism.
- We provide the Python/Pytorch implementation of DAZLE. When using the code in your research work, you should cite the following paper:
D. Huynh, E. Elhamifar, Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
Project Page
Interactive Multi-Label CNN Learning with Partial Labels
- We address efficient end-to-end learning of multi-label CNN classifiers with partial labels using an interactive learning framework.
- We provide the Python/Tensorflow implementation of IMCL. When using the code in your research work, you should cite the following paper:
D. Huynh, E. Elhamifar, Interactive Multi-Label CNN Learning with Partial Labels, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
Project Page
Deep Summarization via Supervised Facility Location (SupFL)
Dissimilarity-based Sparse Subset Selection (DS3)
- Dissimilarity-based Sparse Subset Selection (DS3) is an algorithm based on simultaneous sparse recovery for finding data/model representatives from a large collection of data/models.
- We provide a MATLAB implementation of DS3. When using the code in your research work, you should cite the following paper:
E. Elhamifar, G. Sapiro and S. Sastry, Dissimilarity-based Sparse Subset Selection, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016.
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Sparse Subspace Clustering (SSC)
- Sparse Subspace Clustering (SSC) is an algorithm based on sparse representation theory for segmentation of data lying in a union of subspaces. For more information please visit the SSC research page.
- We provide a MATLAB implementation of SSC. When using the code in your research work, you should cite the following paper:
E. Elhamifar and R. Vidal, Sparse Subspace Clustering: Algorithm, Theory, and Applications, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2013.
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Sparse Manifold Clustering and Embedding (SMCE)
- Sparse Manifold Clustering and Embedding (SMCE) is an algorithm based on sparse representation theory for clustering and dimensionality reduction of data lying in a union of nonlinear manifolds.
- We provide a MATLAB implementation of SMCE algorithm. When using the code in your research work, you should cite the following paper:
E. Elhamifar and R. Vidal, Sparse Manifold Clustering and Embedding, Neural Information Processing Systems (NIPS), 2011.
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Sparse Modeling Representative Selection (SMRS)
- Sparse Modeling Representative Selection (SMRS) is an algorithm based on sparse multiple-measurement-vector recovery theory for selecting a subset of data points as the representatives.
- We provide a MATLAB implementation of SMRS algorithm. When using the code in your research work, you should cite the following paper:
E. Elhamifar, G. Sapiro, and R. Vidal, See All by Looking at A Few: Sparse Modeling for Finding Representative Objects, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
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Block-Sparse Subspace Classification (BSSC)
- Structured-Sparse Subspace Classification is an algorithm based on block-sparse representation techniques for classifying multi-subspace data, where the training data in each class lie in a union of subspaces.
- We provide a MATLAB implementation of the algorithm. When using the code in your research work, you should cite the following paper:
E. Elhamifar and R. Vidal, Robust Classification using Structured Sparse Representation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
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