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HMDB: A large video database for human motion recognition
This paper uses the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube, to evaluate the performance of two representative computer vision systems for action recognition and explore the robustness of these methods under various conditions.
Hierarchical models of object recognition in cortex
A new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions is described.
Robust Object Recognition with Cortex-Like Mechanisms
A hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating between a template matching and a maximum pooling operation is described.
Networks for approximation and learning
The problem of the approximation of nonlinear mapping, (especially continuous mappings) is considered. Regularization theory and a theoretical framework for approximation (based on regularization
Holographic Embeddings of Knowledge Graphs
Holographic embeddings are proposed to learn compositional vector space representations of entire knowledge graphs to outperform state-of-the-art methods for link prediction on knowledge graphs and relational learning benchmark datasets.
Incremental and Decremental Support Vector Machine Learning
An on-line recursive algorithm for training support vector machines, one vector at a time, is presented and interpretation of decremental unlearning in feature space sheds light on the relationship between generalization and geometry of the data.
Multiclass cancer diagnosis using tumor gene expression signatures
The results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.
Face Recognition: Features Versus Templates
Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the
Object recognition with features inspired by visual cortex
The performance of the approach constitutes a suggestive plausibility proof for a class of feedforward models of object recognition in cortex and exhibits excellent recognition performance and outperforms several state-of-the-art systems on a variety of image datasets including many different object categories.
Example-Based Learning for View-Based Human Face Detection
An example-based learning approach for locating vertical frontal views of human faces in complex scenes and shows empirically that the distance metric adopted for computing difference feature vectors, and the "nonface" clusters included in the distribution-based model, are both critical for the success of the system.