Learning Hypergraph-regularized Attribute Predictors
This work addresses attribute learning problems in computer vision, offering an efficient and flexible method, though it appears incremental as it builds on existing hypergraph and regularization techniques.
The authors tackled attribute prediction and zero-shot/N-shot learning by proposing a hypergraph-based framework that jointly learns attribute projections, achieving competitive results on AWA, USAA, and CUB databases compared to state-of-the-art methods.
We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem in which HAP jointly learns a collection of attribute projections from the feature space to a hypergraph embedding space aligned with the attribute space. The learned projections directly act as attribute classifiers (linear and kernelized). This formulation leads to a very efficient approach. By considering our model as a multi-graph cut task, our framework can flexibly incorporate other available information, in particular class label. We apply our approach to attribute prediction, Zero-shot and $N$-shot learning tasks. The results on AWA, USAA and CUB databases demonstrate the value of our methods in comparison with the state-of-the-art approaches.